diff --git a/.eslintrc.json b/.eslintrc.json index f442c62e..294ad555 100644 --- a/.eslintrc.json +++ b/.eslintrc.json @@ -19,10 +19,10 @@ "extends": [ "airbnb-base", "eslint:recommended", - "plugin:@typescript-eslint/eslint-recommended", + "plugin:@typescript-eslint/strict", "plugin:@typescript-eslint/recommended", "plugin:@typescript-eslint/recommended-requiring-type-checking", - "plugin:@typescript-eslint/strict", + "plugin:@typescript-eslint/eslint-recommended", "plugin:import/recommended", "plugin:promise/recommended" ], @@ -30,16 +30,17 @@ "@typescript-eslint/ban-ts-comment":"off", "@typescript-eslint/dot-notation":"off", "@typescript-eslint/no-empty-interface":"off", + "@typescript-eslint/no-empty-object-type":"off", "@typescript-eslint/no-inferrable-types":"off", "@typescript-eslint/no-misused-promises":"off", + "@typescript-eslint/no-require-imports":"off", "@typescript-eslint/no-unnecessary-condition":"off", + "@typescript-eslint/no-unnecessary-type-assertion":"off", "@typescript-eslint/no-unsafe-argument":"off", "@typescript-eslint/no-unsafe-assignment":"off", "@typescript-eslint/no-unsafe-call":"off", "@typescript-eslint/no-unsafe-member-access":"off", "@typescript-eslint/no-unsafe-return":"off", - "@typescript-eslint/no-require-imports":"off", - "@typescript-eslint/no-empty-object-type":"off", "@typescript-eslint/non-nullable-type-assertion-style":"off", "@typescript-eslint/prefer-for-of":"off", "@typescript-eslint/prefer-nullish-coalescing":"off", @@ -163,24 +164,6 @@ }, "extends": [] }, - { - "files": ["**/*.html"], - "parserOptions": { "sourceType": "module", "ecmaVersion": "latest" }, - "parser": "@html-eslint/parser", - "plugins": ["html", "@html-eslint"], - "env": { - "browser": true, - "commonjs": false, - "node": false, - "es2021": false - }, - "extends": ["plugin:@html-eslint/recommended"], - "rules": { - "@html-eslint/element-newline":"off", - "@html-eslint/attrs-newline":"off", - "@html-eslint/indent": ["error", 2] - } - }, { "files": ["**/*.md"], "plugins": ["markdown"], diff --git a/CHANGELOG.md b/CHANGELOG.md index 02509f83..649b49f7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # @vladmandic/human - Version: **3.3.5** + Version: **3.3.6** Description: **Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition** Author: **Vladimir Mandic ** @@ -9,11 +9,16 @@ ## Changelog +### **3.3.6** 2025/08/26 mandic00@live.com + + +### **origin/main** 2025/02/05 mandic00@live.com + +- full rebuild + ### **3.3.5** 2025/02/05 mandic00@live.com - -### **origin/main** 2024/10/24 mandic00@live.com - +- rebuild - add human.draw.tensor method ### **3.3.4** 2024/10/24 mandic00@live.com diff --git a/demo/nodejs/node-age-gender.js b/demo/nodejs/node-age-gender.js new file mode 100644 index 00000000..555ecd2b --- /dev/null +++ b/demo/nodejs/node-age-gender.js @@ -0,0 +1,70 @@ +const fs = require('fs'); +const process = require('process'); +const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require +const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require +const Human = require('../../dist/human.node.js'); + +const humanConfig = { + debug: false, + face: { + enabled: true, + detector: { enabled: true, modelPath: 'blazeface.json' }, + description: { enabled: true, modelPath: 'faceres.json' }, + // gear: { enabled: true, modelPath: '/home/vlado/dev/human-models/models/gear.json' }, + // ssrnet: { enabled: true, modelPathAge: '/home/vlado/dev/human-models/models/age.json', modelPathGender: '/home/vlado/dev/human-models/models/gender.json' }, + emotion: { enabled: false }, + mesh: { enabled: false }, + iris: { enabled: false }, + antispoof: { enabled: false }, + liveness: { enabled: false }, + }, + body: { enabled: false }, + hand: { enabled: false }, + gesture: { enabled: false }, +}; +const human = new Human.Human(humanConfig); +const ageThreshold = 18; + +async function detect(inputFile) { + try { + const buffer = fs.readFileSync(inputFile); + const tensor = human.tf.node.decodeImage(buffer); + const result = await human.detect(tensor); + human.tf.dispose(tensor); + if (!result || !result.face || result.face.length === 0) return false; + let msg = ` file=${inputFile} resolution=${tensor.shape}`; + for (const face of result.face) { + msg = ` file=${inputFile} resolution=${tensor.shape} age=${face.age} gender=${face.gender} confidence=${face.genderScore}`; + if (face.age < ageThreshold) { + log.warn('fail:' + msg); + return true; + } + } + log.info('pass: ' + msg); + return false; + } catch (err) { + log.error(`error: file=${inputFile}: ${err}`); + } + return false; +} + +async function main() { + log.info(`Human: version=${human.version} tf=${tf.version_core}`); + process.noDeprecation = true; + if (process.argv.length < 3) return; + await human.load(); + await human.warmup(); + const t0 = performance.now(); + const args = process.argv.slice(2); + let pass = 0; + let fail = 0; + for (const arg of args) { + const ok = await detect(arg); + if (ok) pass++; + else fail++; + } + const t1 = performance.now(); + log.info(`Human: files=${args.length} pass=${pass} fail=${fail} time=${Math.round(t1 - t0)} fps=${Math.round(10000 * args.length / (t1 - t0)) / 10}`); +} + +main(); diff --git a/demo/nodejs/node-canvas.js b/demo/nodejs/node-canvas.js index c9a403d0..79862bd7 100644 --- a/demo/nodejs/node-canvas.js +++ b/demo/nodejs/node-canvas.js @@ -24,6 +24,7 @@ const config = { // just enable all and leave default settings async function main() { log.header(); + process.noDeprecation = true; globalThis.Canvas = canvas.Canvas; // patch global namespace with canvas library globalThis.ImageData = canvas.ImageData; // patch global namespace with canvas library diff --git a/demo/nodejs/node-similarity.js b/demo/nodejs/node-similarity.js index f5752daf..cbd04de1 100644 --- a/demo/nodejs/node-similarity.js +++ b/demo/nodejs/node-similarity.js @@ -45,6 +45,7 @@ async function detect(input) { } async function main() { + process.noDeprecation = true; log.configure({ inspect: { breakLength: 265 } }); log.header(); if (process.argv.length !== 4) { diff --git a/demo/nodejs/node-simple.js b/demo/nodejs/node-simple.js index 133f9cf3..b54c5b00 100644 --- a/demo/nodejs/node-simple.js +++ b/demo/nodejs/node-simple.js @@ -17,6 +17,7 @@ const humanConfig = { }; async function detect(inputFile) { + process.noDeprecation = true; const human = new Human.Human(humanConfig); // create instance of human using default configuration console.log('Human:', human.version, 'TF:', tf.version_core); // eslint-disable-line no-console await human.load(); // optional as models would be loaded on-demand first time they are required diff --git a/demo/nodejs/node-video.js b/demo/nodejs/node-video.js index 3ea60589..959c45ae 100644 --- a/demo/nodejs/node-video.js +++ b/demo/nodejs/node-video.js @@ -76,6 +76,7 @@ async function detect(jpegBuffer) { async function main() { log.header(); + process.noDeprecation = true; await human.tf.ready(); // pre-load models log.info({ human: human.version, tf: human.tf.version_core }); diff --git a/demo/nodejs/node.js b/demo/nodejs/node.js index 495c1e2d..8046ce55 100644 --- a/demo/nodejs/node.js +++ b/demo/nodejs/node.js @@ -187,6 +187,7 @@ async function test() { async function main() { log.configure({ inspect: { breakLength: 265 } }); log.header(); + process.noDeprecation = true; log.info('Current folder:', process.env.PWD); await init(); const f = process.argv[2]; diff --git a/demo/nodejs/process-folder.js b/demo/nodejs/process-folder.js index 637a1d52..86de666e 100644 --- a/demo/nodejs/process-folder.js +++ b/demo/nodejs/process-folder.js @@ -77,6 +77,7 @@ async function main() { globalThis.ImageData = canvas.ImageData; // patch global namespace with canvas library 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k=u.nearest(b,1,e.distanceLimit)[0],T=u.nearest(v,1,e.distanceLimit)[0],O=u.nearest(b,2,e.distanceLimit);if(k){var x=a.indexOf(k[0]);if(!d[x]){d[x]={idDisplay:v.idDisplay};var E=a[x];n.get(v.id).makeUnavailable().update(E,o)}}});else throw`Unknown matching algorithm "${e.matchingAlgorithm}"`;else g&&console.log("[Tracker] Nothing detected for frame n\xBA"+o),n.forEach(function(v){v.makeAvailable()});e.matchingAlgorithm==="kdTree"&&n.size>0&&(m=new c(Array.from(n.values()),e.distanceFunc,["x","y","w","h"]),d.forEach(function(v,b){if(!v){var k=m.nearest(a[b],1,e.distanceLimit)[0];if(!k){var T=f(a[b],o,e.unMatchedFramesTolerance,e.fastDelete);n.set(T.id,T),m.insert(T),T.makeUnavailable()}}})),n.forEach(function(v){v.available&&(v.countDown(o),v.updateTheoricalPositionAndSize(),v.isDead()&&(n.delete(v.id),m.remove(v),r&&t.set(v.id,v)))})}},l.reset=function(){n=new Map,t=new 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R={debug:!0,backend:"webgl",modelBasePath:"https://vladmandic.github.io/human-models/models",filter:{enabled:!0,equalization:!1,flip:!1},face:{enabled:!0,detector:{rotation:!1,maxDetected:10,minConfidence:.3},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!1},description:{enabled:!1},emotion:{enabled:!1},antispoof:{enabled:!1},liveness:{enabled:!1}},body:{enabled:!1,maxDetected:6,modelPath:"movenet-multipose.json"},hand:{enabled:!1},object:{enabled:!1,maxDetected:10},segmentation:{enabled:!1},gesture:{enabled:!1}},F={unMatchedFramesTolerance:100,iouLimit:.05,fastDelete:!1,distanceLimit:1e4,matchingAlgorithm:"kdTree"},M=new U.Human(R),_={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),tracker:document.getElementById("tracker"),interpolation:document.getElementById("interpolation"),config:document.getElementById("config"),ctx:document.getElementById("canvas").getContext("2d")},A={detect:0,draw:0,tensors:0,start:0},N={detectFPS:0,drawFPS:0,frames:0,averageMs:0},H=(...l)=>{_.log.innerText+=l.join(" ")+` `,console.log(...l)},B=l=>_.fps.innerText=l;async function V(){if(!_.video.paused&&_.video.readyState>=2){A.start===0&&(A.start=M.now()),await M.detect(_.video,R);let l=M.tf.memory().numTensors;l-A.tensors!==0&&H("allocated tensors:",l-A.tensors),A.tensors=l,N.detectFPS=Math.round(1e3*1e3/(M.now()-A.detect))/1e3,N.frames++,N.averageMs=Math.round(1e3*(M.now()-A.start)/N.frames)/1e3}A.detect=M.now(),requestAnimationFrame(V)}function Z(){if(!_.video.paused&&_.video.readyState>=2){let y=_.interpolation.checked?M.next(M.result):M.result,f=[];M.config.face.enabled?f=y.face:M.config.body.enabled?f=y.body:M.config.object.enabled?f=y.object:H("unknown object type");let c=[];if(_.tracker.checked){let s=f.map(i=>({x:i.box[0]+i.box[2]/2,y:i.box[1]+i.box[3]/2,w:i.box[2],h:i.box[3],name:i.label||(M.config.face.enabled?"face":"body"),confidence:i.score}));D.updateTrackedItemsWithNewFrame(s,N.frames),c=D.getJSONOfTrackedItems(!0)}M.draw.canvas(_.video,_.canvas);for(let s=0;sl.preventDefault()),document.body.addEventListener("dragleave",l=>l.preventDefault()),document.body.addEventListener("dragover",l=>l.preventDefault()),document.body.addEventListener("drop",async l=>{var f,c;l.preventDefault(),l.dataTransfer&&(l.dataTransfer.dropEffect="copy");let y=(c=(f=l.dataTransfer)==null?void 0:f.files)==null?void 0:c[0];y&&await P(y),H(_.video.readyState)}),document.getElementById("inputvideo").onchange=async l=>{var f,c;l.preventDefault();let y=(c=(f=l.target)==null?void 0:f.files)==null?void 0:c[0];y&&await P(y)},_.config.onchange=()=>{F.distanceLimit=document.getElementById("distanceLimit").valueAsNumber,F.iouLimit=document.getElementById("iouLimit").valueAsNumber,F.unMatchedFramesTolerance=document.getElementById("unMatchedFramesTolerance").valueAsNumber,F.unMatchedFramesTolerance=document.getElementById("unMatchedFramesTolerance").valueAsNumber,F.matchingAlgorithm=document.getElementById("matchingAlgorithm-kdTree").checked?"kdTree":"munkres",D.setParams(F),document.getElementById("keepInMemory").checked?D.enableKeepInMemory():D.disableKeepInMemory(),D.reset(),H("tracker config change",JSON.stringify(F)),R.face.enabled=document.getElementById("box-face").checked,R.body.enabled=document.getElementById("box-body").checked,R.object.enabled=document.getElementById("box-object").checked},_.tracker.onchange=l=>{H("tracker",l.target.checked?"enabled":"disabled"),D.setParams(F),D.reset()}}async function te(){H("human version:",M.version,"| tfjs version:",M.tf.version["tfjs-core"]),H("platform:",M.env.platform,"| agent:",M.env.agent),B("loading..."),await M.load(),H("backend:",M.tf.getBackend(),"| available:",M.env.backends),H("models loaded:",M.models.loaded()),B("initializing..."),await M.warmup(),ee(),await V(),Z()}window.onload=te; //# sourceMappingURL=index.js.map diff --git a/demo/tracker/index.js.map b/demo/tracker/index.js.map index d23e1158..e408847b 100644 --- a/demo/tracker/index.js.map +++ b/demo/tracker/index.js.map @@ -2,6 +2,6 @@ "version": 3, "sources": ["index.ts", "tracker.js"], "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\nimport tracker from './tracker.js';\n\nconst humanConfig: Partial = { // user configuration for human, used to fine-tune behavior\n debug: true,\n backend: 'webgl',\n // cacheSensitivity: 0,\n // cacheModels: false,\n // warmup: 'none',\n modelBasePath: 'https://vladmandic.github.io/human-models/models',\n filter: { enabled: true, equalization: false, flip: false },\n face: {\n enabled: true,\n detector: { rotation: false, maxDetected: 10, minConfidence: 0.3 },\n mesh: { enabled: true },\n attention: { enabled: false },\n iris: { enabled: false },\n description: { enabled: false },\n emotion: { enabled: false },\n antispoof: { enabled: false },\n liveness: { enabled: false },\n },\n body: { enabled: false, maxDetected: 6, modelPath: 'movenet-multipose.json' },\n hand: { enabled: false },\n object: { enabled: false, maxDetected: 10 },\n segmentation: { enabled: false },\n gesture: { enabled: false },\n};\n\ninterface TrackerConfig {\n unMatchedFramesTolerance: number, // number of frame when an object is not matched before considering it gone; ignored if fastDelete is set\n iouLimit: number, // exclude things from beeing matched if their IOU less than; 1 means total overlap; 0 means no overlap\n fastDelete: boolean, // remove new objects immediately if they could not be matched in the next frames; if set, ignores unMatchedFramesTolerance\n distanceLimit: number, // distance limit for matching; if values need to be excluded from matching set their distance to something greater than the distance limit\n matchingAlgorithm: 'kdTree' | 'munkres', // algorithm used to match tracks with new detections\n}\n\ninterface TrackerResult {\n id: number,\n confidence: number,\n bearing: number,\n isZombie: boolean,\n name: string,\n x: number,\n y: number,\n w: number,\n h: number,\n}\n\nconst trackerConfig: TrackerConfig = {\n unMatchedFramesTolerance: 100,\n iouLimit: 0.05,\n fastDelete: false,\n distanceLimit: 1e4,\n matchingAlgorithm: 'kdTree',\n};\n\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('status') as HTMLPreElement,\n tracker: document.getElementById('tracker') as HTMLInputElement,\n interpolation: document.getElementById('interpolation') as HTMLInputElement,\n config: document.getElementById('config') as HTMLFormElement,\n ctx: (document.getElementById('canvas') as HTMLCanvasElement).getContext('2d') as CanvasRenderingContext2D,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n console.log(...msg); // eslint-disable-line no-console\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused && dom.video.readyState >= 2) {\n if (timestamp.start === 0) timestamp.start = human.now();\n // log('profiling data:', await human.profile(dom.video));\n await human.detect(dom.video, humanConfig); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n timestamp.tensors = tensors;\n fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;\n fps.frames++;\n fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;\n }\n timestamp.detect = human.now();\n requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nfunction drawLoop() { // main screen refresh loop\n if (!dom.video.paused && dom.video.readyState >= 2) {\n const res: H.Result = dom.interpolation.checked ? human.next(human.result) : human.result; // interpolate results if enabled\n let tracking: H.FaceResult[] | H.BodyResult[] | H.ObjectResult[] = [];\n if (human.config.face.enabled) tracking = res.face;\n else if (human.config.body.enabled) tracking = res.body;\n else if (human.config.object.enabled) tracking = res.object;\n else log('unknown object type');\n let data: TrackerResult[] = [];\n if (dom.tracker.checked) {\n const items = tracking.map((obj) => ({\n x: obj.box[0] + obj.box[2] / 2,\n y: obj.box[1] + obj.box[3] / 2,\n w: obj.box[2],\n h: obj.box[3],\n name: obj.label || (human.config.face.enabled ? 'face' : 'body'),\n confidence: obj.score,\n }));\n tracker.updateTrackedItemsWithNewFrame(items, fps.frames);\n data = tracker.getJSONOfTrackedItems(true) as TrackerResult[];\n }\n human.draw.canvas(dom.video, dom.canvas); // copy input video frame to output canvas\n for (let i = 0; i < tracking.length; i++) {\n // @ts-ignore\n const name = tracking[i].label || (human.config.face.enabled ? 'face' : 'body');\n dom.ctx.strokeRect(tracking[i].box[0], tracking[i].box[1], tracking[i].box[1], tracking[i].box[2]);\n dom.ctx.fillText(`id: ${tracking[i].id} ${Math.round(100 * tracking[i].score)}% ${name}`, tracking[i].box[0] + 4, tracking[i].box[1] + 16);\n if (data[i]) {\n dom.ctx.fillText(`t: ${data[i].id} ${Math.round(100 * data[i].confidence)}% ${data[i].name} ${data[i].isZombie ? 'zombie' : ''}`, tracking[i].box[0] + 4, tracking[i].box[1] + 34);\n }\n }\n }\n const now = human.now();\n fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;\n timestamp.draw = now;\n status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status\n setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function handleVideo(file: File) {\n const url = URL.createObjectURL(file);\n dom.video.src = url;\n await dom.video.play();\n log('loaded video:', file.name, 'resolution:', [dom.video.videoWidth, dom.video.videoHeight], 'duration:', dom.video.duration);\n dom.canvas.width = dom.video.videoWidth;\n dom.canvas.height = dom.video.videoHeight;\n dom.ctx.strokeStyle = 'white';\n dom.ctx.fillStyle = 'white';\n dom.ctx.font = '16px Segoe UI';\n dom.video.playbackRate = 0.25;\n}\n\nfunction initInput() {\n document.body.addEventListener('dragenter', (evt) => evt.preventDefault());\n document.body.addEventListener('dragleave', (evt) => evt.preventDefault());\n document.body.addEventListener('dragover', (evt) => evt.preventDefault());\n document.body.addEventListener('drop', async (evt) => {\n evt.preventDefault();\n if (evt.dataTransfer) evt.dataTransfer.dropEffect = 'copy';\n const file = evt.dataTransfer?.files?.[0];\n if (file) await handleVideo(file);\n log(dom.video.readyState);\n });\n (document.getElementById('inputvideo') as HTMLInputElement).onchange = async (evt) => {\n evt.preventDefault();\n const file = evt.target?.['files']?.[0];\n if (file) await handleVideo(file);\n };\n dom.config.onchange = () => {\n trackerConfig.distanceLimit = (document.getElementById('distanceLimit') as HTMLInputElement).valueAsNumber;\n trackerConfig.iouLimit = (document.getElementById('iouLimit') as HTMLInputElement).valueAsNumber;\n trackerConfig.unMatchedFramesTolerance = (document.getElementById('unMatchedFramesTolerance') as HTMLInputElement).valueAsNumber;\n trackerConfig.unMatchedFramesTolerance = (document.getElementById('unMatchedFramesTolerance') as HTMLInputElement).valueAsNumber;\n trackerConfig.matchingAlgorithm = (document.getElementById('matchingAlgorithm-kdTree') as HTMLInputElement).checked ? 'kdTree' : 'munkres';\n tracker.setParams(trackerConfig);\n if ((document.getElementById('keepInMemory') as HTMLInputElement).checked) tracker.enableKeepInMemory();\n else tracker.disableKeepInMemory();\n tracker.reset();\n log('tracker config change', JSON.stringify(trackerConfig));\n humanConfig.face!.enabled = (document.getElementById('box-face') as HTMLInputElement).checked; // eslint-disable-line @typescript-eslint/no-non-null-assertion\n humanConfig.body!.enabled = (document.getElementById('box-body') as HTMLInputElement).checked; // eslint-disable-line @typescript-eslint/no-non-null-assertion\n humanConfig.object!.enabled = (document.getElementById('box-object') as HTMLInputElement).checked; // eslint-disable-line @typescript-eslint/no-non-null-assertion\n };\n dom.tracker.onchange = (evt) => {\n log('tracker', (evt.target as HTMLInputElement).checked ? 'enabled' : 'disabled');\n tracker.setParams(trackerConfig);\n tracker.reset();\n };\n}\n\nasync function main() { // main entry point\n log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n log('platform:', human.env.platform, '| agent:', human.env.agent);\n status('loading...');\n await human.load(); // preload all models\n log('backend:', human.tf.getBackend(), '| available:', human.env.backends);\n log('models loaded:', human.models.loaded());\n status('initializing...');\n await human.warmup(); // warmup function to initialize backend for future faster detection\n initInput(); // initialize input\n await detectionLoop(); // start detection loop\n drawLoop(); // start draw loop\n}\n\nwindow.onload = main;\n", "/* eslint-disable */\nvar __defProp = Object.defineProperty;\nvar __getOwnPropNames = Object.getOwnPropertyNames;\nvar __name = (target, value) => __defProp(target, \"name\", { value, configurable: true });\nvar __commonJS = (cb, mod) => function __require() {\n return mod || (0, cb[__getOwnPropNames(cb)[0]])((mod = { exports: {} }).exports, mod), mod.exports;\n};\n\n// node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/lib/rng-browser.js\nvar require_rng_browser = __commonJS({\n \"node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/lib/rng-browser.js\"(exports, module) {\n var getRandomValues = typeof crypto != \"undefined\" && crypto.getRandomValues.bind(crypto) || typeof msCrypto != \"undefined\" && msCrypto.getRandomValues.bind(msCrypto);\n if (getRandomValues) {\n rnds8 = new Uint8Array(16);\n module.exports = /* @__PURE__ */ __name(function whatwgRNG() {\n getRandomValues(rnds8);\n return rnds8;\n }, \"whatwgRNG\");\n } else {\n rnds = new Array(16);\n module.exports = /* @__PURE__ */ __name(function mathRNG() {\n for (var i = 0, r; i < 16; i++) {\n if ((i & 3) === 0)\n r = Math.random() * 4294967296;\n rnds[i] = r >>> ((i & 3) << 3) & 255;\n }\n return rnds;\n }, \"mathRNG\");\n }\n var rnds8;\n var rnds;\n }\n});\n\n// node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/lib/bytesToUuid.js\nvar require_bytesToUuid = __commonJS({\n \"node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/lib/bytesToUuid.js\"(exports, module) {\n var byteToHex = [];\n for (i = 0; i < 256; ++i) {\n byteToHex[i] = (i + 256).toString(16).substr(1);\n }\n function bytesToUuid(buf, offset) {\n var i2 = offset || 0;\n var bth = byteToHex;\n return bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + \"-\" + bth[buf[i2++]] + bth[buf[i2++]] + \"-\" + bth[buf[i2++]] + bth[buf[i2++]] + \"-\" + bth[buf[i2++]] + bth[buf[i2++]] + \"-\" + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]];\n }\n __name(bytesToUuid, \"bytesToUuid\");\n module.exports = bytesToUuid;\n var i;\n }\n});\n\n// node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/v4.js\nvar require_v4 = __commonJS({\n \"node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/v4.js\"(exports, module) {\n var rng = require_rng_browser();\n var bytesToUuid = require_bytesToUuid();\n function v4(options, buf, offset) {\n var i = buf && offset || 0;\n if (typeof options == \"string\") {\n buf = options === \"binary\" ? new Array(16) : null;\n options = null;\n }\n options = options || {};\n var rnds = options.random || (options.rng || rng)();\n rnds[6] = rnds[6] & 15 | 64;\n rnds[8] = rnds[8] & 63 | 128;\n if (buf) {\n for (var ii = 0; ii < 16; ++ii) {\n buf[i + ii] = rnds[ii];\n }\n }\n return buf || bytesToUuid(rnds);\n }\n __name(v4, \"v4\");\n module.exports = v4;\n }\n});\n\n// utils.js\nvar require_utils = __commonJS({\n \"utils.js\"(exports) {\n exports.isDetectionTooLarge = (detections, largestAllowed) => {\n if (detections.w >= largestAllowed) {\n return true;\n } else {\n return false;\n }\n };\n var isInsideArea = /* @__PURE__ */ __name((area, point) => {\n const xMin = area.x - area.w / 2;\n const xMax = area.x + area.w / 2;\n const yMin = area.y - area.h / 2;\n const yMax = area.y + area.h / 2;\n if (point.x >= xMin && point.x <= xMax && point.y >= yMin && point.y <= yMax) {\n return true;\n } else {\n return false;\n }\n }, \"isInsideArea\");\n exports.isInsideArea = isInsideArea;\n exports.isInsideSomeAreas = (areas, point) => {\n const isInside = areas.some((area) => isInsideArea(area, point));\n return isInside;\n };\n exports.ignoreObjectsNotToDetect = (detections, objectsToDetect) => {\n return detections.filter((detection) => objectsToDetect.indexOf(detection.name) > -1);\n };\n var getRectangleEdges = /* @__PURE__ */ __name((item) => {\n return {\n x0: item.x - item.w / 2,\n y0: item.y - item.h / 2,\n x1: item.x + item.w / 2,\n y1: item.y + item.h / 2\n };\n }, \"getRectangleEdges\");\n exports.getRectangleEdges = getRectangleEdges;\n exports.iouAreas = (item1, item2) => {\n var rect1 = getRectangleEdges(item1);\n var rect2 = getRectangleEdges(item2);\n var overlap_x0 = Math.max(rect1.x0, rect2.x0);\n var overlap_y0 = Math.max(rect1.y0, rect2.y0);\n var overlap_x1 = Math.min(rect1.x1, rect2.x1);\n var overlap_y1 = Math.min(rect1.y1, rect2.y1);\n if (overlap_x1 - overlap_x0 <= 0 || overlap_y1 - overlap_y0 <= 0) {\n return 0;\n } else {\n const area_rect1 = item1.w * item1.h;\n const area_rect2 = item2.w * item2.h;\n const area_intersection = (overlap_x1 - overlap_x0) * (overlap_y1 - overlap_y0);\n const area_union = area_rect1 + area_rect2 - area_intersection;\n return area_intersection / area_union;\n }\n };\n exports.computeVelocityVector = (item1, item2, nbFrame) => {\n return {\n dx: (item2.x - item1.x) / nbFrame,\n dy: (item2.y - item1.y) / nbFrame\n };\n };\n exports.computeBearingIn360 = function(dx, dy) {\n var angle = Math.atan(dx / dy) / (Math.PI / 180);\n if (angle > 0) {\n if (dy > 0)\n return angle;\n else\n return 180 + angle;\n } else {\n if (dx > 0)\n return 180 + angle;\n else\n return 360 + angle;\n }\n };\n }\n});\n\n// ItemTracked.js\nvar require_ItemTracked = __commonJS({\n \"ItemTracked.js\"(exports) {\n var uuidv4 = require_v4();\n var computeBearingIn360 = require_utils().computeBearingIn360;\n var computeVelocityVector = require_utils().computeVelocityVector;\n exports.ITEM_HISTORY_MAX_LENGTH = 15;\n var idDisplay = 0;\n exports.ItemTracked = function(properties, frameNb, unMatchedFramesTolerance, fastDelete) {\n var DEFAULT_UNMATCHEDFRAMES_TOLERANCE = unMatchedFramesTolerance;\n var itemTracked = {};\n itemTracked.available = true;\n itemTracked.delete = false;\n itemTracked.fastDelete = fastDelete;\n itemTracked.frameUnmatchedLeftBeforeDying = unMatchedFramesTolerance;\n itemTracked.isZombie = false;\n itemTracked.appearFrame = frameNb;\n itemTracked.disappearFrame = null;\n itemTracked.disappearArea = {};\n itemTracked.nameCount = {};\n itemTracked.nameCount[properties.name] = 1;\n itemTracked.x = properties.x;\n itemTracked.y = properties.y;\n itemTracked.w = properties.w;\n itemTracked.h = properties.h;\n itemTracked.name = properties.name;\n itemTracked.confidence = properties.confidence;\n itemTracked.itemHistory = [];\n itemTracked.itemHistory.push({\n x: properties.x,\n y: properties.y,\n w: properties.w,\n h: properties.h,\n confidence: properties.confidence\n });\n if (itemTracked.itemHistory.length >= exports.ITEM_HISTORY_MAX_LENGTH) {\n itemTracked.itemHistory.shift();\n }\n itemTracked.velocity = {\n dx: 0,\n dy: 0\n };\n itemTracked.nbTimeMatched = 1;\n itemTracked.id = uuidv4();\n itemTracked.idDisplay = idDisplay;\n idDisplay++;\n itemTracked.update = function(properties2, frameNb2) {\n if (this.disappearFrame) {\n this.disappearFrame = null;\n this.disappearArea = {};\n }\n this.isZombie = false;\n this.nbTimeMatched += 1;\n this.x = properties2.x;\n this.y = properties2.y;\n this.w = properties2.w;\n this.h = properties2.h;\n this.confidence = properties2.confidence;\n this.itemHistory.push({\n x: this.x,\n y: this.y,\n w: this.w,\n h: this.h,\n confidence: this.confidence\n });\n if (itemTracked.itemHistory.length >= exports.ITEM_HISTORY_MAX_LENGTH) {\n itemTracked.itemHistory.shift();\n }\n this.name = properties2.name;\n if (this.nameCount[properties2.name]) {\n this.nameCount[properties2.name]++;\n } else {\n this.nameCount[properties2.name] = 1;\n }\n this.frameUnmatchedLeftBeforeDying = DEFAULT_UNMATCHEDFRAMES_TOLERANCE;\n this.velocity = this.updateVelocityVector();\n };\n itemTracked.makeAvailable = function() {\n this.available = true;\n return this;\n };\n itemTracked.makeUnavailable = function() {\n this.available = false;\n return this;\n };\n itemTracked.countDown = function(frameNb2) {\n if (this.disappearFrame === null) {\n this.disappearFrame = frameNb2;\n this.disappearArea = {\n x: this.x,\n y: this.y,\n w: this.w,\n h: this.h\n };\n }\n this.frameUnmatchedLeftBeforeDying--;\n this.isZombie = true;\n if (this.fastDelete && this.nbTimeMatched <= 1) {\n this.frameUnmatchedLeftBeforeDying = -1;\n }\n };\n itemTracked.updateTheoricalPositionAndSize = function() {\n this.itemHistory.push({\n x: this.x,\n y: this.y,\n w: this.w,\n h: this.h,\n confidence: this.confidence\n });\n if (itemTracked.itemHistory.length >= exports.ITEM_HISTORY_MAX_LENGTH) {\n itemTracked.itemHistory.shift();\n }\n this.x = this.x + this.velocity.dx;\n this.y = this.y + this.velocity.dy;\n };\n itemTracked.predictNextPosition = function() {\n return {\n x: this.x + this.velocity.dx,\n y: this.y + this.velocity.dy,\n w: this.w,\n h: this.h\n };\n };\n itemTracked.isDead = function() {\n return this.frameUnmatchedLeftBeforeDying < 0;\n };\n itemTracked.updateVelocityVector = function() {\n if (exports.ITEM_HISTORY_MAX_LENGTH <= 2) {\n return { dx: void 0, dy: void 0 };\n }\n if (this.itemHistory.length <= exports.ITEM_HISTORY_MAX_LENGTH) {\n const start = this.itemHistory[0];\n const end = this.itemHistory[this.itemHistory.length - 1];\n return computeVelocityVector(start, end, this.itemHistory.length);\n } else {\n const start = this.itemHistory[this.itemHistory.length - exports.ITEM_HISTORY_MAX_LENGTH];\n const end = this.itemHistory[this.itemHistory.length - 1];\n return computeVelocityVector(start, end, exports.ITEM_HISTORY_MAX_LENGTH);\n }\n };\n itemTracked.getMostlyMatchedName = function() {\n var nameMostlyMatchedOccurences = 0;\n var nameMostlyMatched = \"\";\n Object.keys(this.nameCount).map((name) => {\n if (this.nameCount[name] > nameMostlyMatchedOccurences) {\n nameMostlyMatched = name;\n nameMostlyMatchedOccurences = this.nameCount[name];\n }\n });\n return nameMostlyMatched;\n };\n itemTracked.toJSONDebug = function(roundInt = true) {\n return {\n id: this.id,\n idDisplay: this.idDisplay,\n x: roundInt ? parseInt(this.x, 10) : this.x,\n y: roundInt ? parseInt(this.y, 10) : this.y,\n w: roundInt ? parseInt(this.w, 10) : this.w,\n h: roundInt ? parseInt(this.h, 10) : this.h,\n confidence: Math.round(this.confidence * 100) / 100,\n // Here we negate dy to be in \"normal\" carthesian coordinates\n bearing: parseInt(computeBearingIn360(this.velocity.dx, -this.velocity.dy)),\n name: this.getMostlyMatchedName(),\n isZombie: this.isZombie,\n appearFrame: this.appearFrame,\n disappearFrame: this.disappearFrame\n };\n };\n itemTracked.toJSON = function(roundInt = true) {\n return {\n id: this.idDisplay,\n x: roundInt ? parseInt(this.x, 10) : this.x,\n y: roundInt ? parseInt(this.y, 10) : this.y,\n w: roundInt ? parseInt(this.w, 10) : this.w,\n h: roundInt ? parseInt(this.h, 10) : this.h,\n confidence: Math.round(this.confidence * 100) / 100,\n // Here we negate dy to be in \"normal\" carthesian coordinates\n bearing: parseInt(computeBearingIn360(this.velocity.dx, -this.velocity.dy), 10),\n name: this.getMostlyMatchedName(),\n isZombie: this.isZombie\n };\n };\n itemTracked.toMOT = function(frameIndex) {\n return `${frameIndex},${this.idDisplay},${this.x - this.w / 2},${this.y - this.h / 2},${this.w},${this.h},${this.confidence / 100},-1,-1,-1`;\n };\n itemTracked.toJSONGenericInfo = function() {\n return {\n id: this.id,\n idDisplay: this.idDisplay,\n appearFrame: this.appearFrame,\n disappearFrame: this.disappearFrame,\n disappearArea: this.disappearArea,\n nbActiveFrame: this.disappearFrame - this.appearFrame,\n name: this.getMostlyMatchedName()\n };\n };\n return itemTracked;\n };\n exports.reset = function() {\n idDisplay = 0;\n };\n }\n});\n\n// lib/kdTree-min.js\nvar require_kdTree_min = __commonJS({\n \"lib/kdTree-min.js\"(exports) {\n (function(root, factory) {\n if (typeof define === \"function\" && define.amd) {\n define([\"exports\"], factory);\n } else if (typeof exports === \"object\") {\n factory(exports);\n } else {\n factory(root);\n }\n })(exports, function(exports2) {\n function Node(obj, dimension, parent) {\n this.obj = obj;\n this.left = null;\n this.right = null;\n this.parent = parent;\n this.dimension = dimension;\n }\n __name(Node, \"Node\");\n function kdTree(points, metric, dimensions) {\n var self = this;\n function buildTree(points2, depth, parent) {\n var dim = depth % dimensions.length, median, node;\n if (points2.length === 0) {\n return null;\n }\n if (points2.length === 1) {\n return new Node(points2[0], dim, parent);\n }\n points2.sort(function(a, b) {\n return a[dimensions[dim]] - b[dimensions[dim]];\n });\n median = Math.floor(points2.length / 2);\n node = new Node(points2[median], dim, parent);\n node.left = buildTree(points2.slice(0, median), depth + 1, node);\n node.right = buildTree(points2.slice(median + 1), depth + 1, node);\n return node;\n }\n __name(buildTree, \"buildTree\");\n function loadTree(data) {\n self.root = data;\n function restoreParent(root) {\n if (root.left) {\n root.left.parent = root;\n restoreParent(root.left);\n }\n if (root.right) {\n root.right.parent = root;\n restoreParent(root.right);\n }\n }\n __name(restoreParent, \"restoreParent\");\n restoreParent(self.root);\n }\n __name(loadTree, \"loadTree\");\n if (!Array.isArray(points))\n loadTree(points, metric, dimensions);\n else\n this.root = buildTree(points, 0, null);\n this.toJSON = function(src) {\n if (!src)\n src = this.root;\n var dest = new Node(src.obj, src.dimension, null);\n if (src.left)\n dest.left = self.toJSON(src.left);\n if (src.right)\n dest.right = self.toJSON(src.right);\n return dest;\n };\n this.insert = function(point) {\n function innerSearch(node, parent) {\n if (node === null) {\n return parent;\n }\n var dimension2 = dimensions[node.dimension];\n if (point[dimension2] < node.obj[dimension2]) {\n return innerSearch(node.left, node);\n } else {\n return innerSearch(node.right, node);\n }\n }\n __name(innerSearch, \"innerSearch\");\n var insertPosition = innerSearch(this.root, null), newNode, dimension;\n if (insertPosition === null) {\n this.root = new Node(point, 0, null);\n return;\n }\n newNode = new Node(point, (insertPosition.dimension + 1) % dimensions.length, insertPosition);\n dimension = dimensions[insertPosition.dimension];\n if (point[dimension] < insertPosition.obj[dimension]) {\n insertPosition.left = newNode;\n } else {\n insertPosition.right = newNode;\n }\n };\n this.remove = function(point) {\n var node;\n function nodeSearch(node2) {\n if (node2 === null) {\n return null;\n }\n if (node2.obj === point) {\n return node2;\n }\n var dimension = dimensions[node2.dimension];\n if (point[dimension] < node2.obj[dimension]) {\n return nodeSearch(node2.left, node2);\n } else {\n return nodeSearch(node2.right, node2);\n }\n }\n __name(nodeSearch, \"nodeSearch\");\n function removeNode(node2) {\n var nextNode, nextObj, pDimension;\n function findMin(node3, dim) {\n var dimension, own, left, right, min;\n if (node3 === null) {\n return null;\n }\n dimension = dimensions[dim];\n if (node3.dimension === dim) {\n if (node3.left !== null) {\n return findMin(node3.left, dim);\n }\n return node3;\n }\n own = node3.obj[dimension];\n left = findMin(node3.left, dim);\n right = findMin(node3.right, dim);\n min = node3;\n if (left !== null && left.obj[dimension] < own) {\n min = left;\n }\n if (right !== null && right.obj[dimension] < min.obj[dimension]) {\n min = right;\n }\n return min;\n }\n __name(findMin, \"findMin\");\n if (node2.left === null && node2.right === null) {\n if (node2.parent === null) {\n self.root = null;\n return;\n }\n pDimension = dimensions[node2.parent.dimension];\n if (node2.obj[pDimension] < node2.parent.obj[pDimension]) {\n node2.parent.left = null;\n } else {\n node2.parent.right = null;\n }\n return;\n }\n if (node2.right !== null) {\n nextNode = findMin(node2.right, node2.dimension);\n nextObj = nextNode.obj;\n removeNode(nextNode);\n node2.obj = nextObj;\n } else {\n nextNode = findMin(node2.left, node2.dimension);\n nextObj = nextNode.obj;\n removeNode(nextNode);\n node2.right = node2.left;\n node2.left = null;\n node2.obj = nextObj;\n }\n }\n __name(removeNode, \"removeNode\");\n node = nodeSearch(self.root);\n if (node === null) {\n return;\n }\n removeNode(node);\n };\n this.nearest = function(point, maxNodes, maxDistance) {\n var i, result, bestNodes;\n bestNodes = new BinaryHeap(\n function(e) {\n return -e[1];\n }\n );\n function nearestSearch(node) {\n var bestChild, dimension = dimensions[node.dimension], ownDistance = metric(point, node.obj), linearPoint = {}, linearDistance, otherChild, i2;\n function saveNode(node2, distance) {\n bestNodes.push([node2, distance]);\n if (bestNodes.size() > maxNodes) {\n bestNodes.pop();\n }\n }\n __name(saveNode, \"saveNode\");\n for (i2 = 0; i2 < dimensions.length; i2 += 1) {\n if (i2 === node.dimension) {\n linearPoint[dimensions[i2]] = point[dimensions[i2]];\n } else {\n linearPoint[dimensions[i2]] = node.obj[dimensions[i2]];\n }\n }\n linearDistance = metric(linearPoint, node.obj);\n if (node.right === null && node.left === null) {\n if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {\n saveNode(node, ownDistance);\n }\n return;\n }\n if (node.right === null) {\n bestChild = node.left;\n } else if (node.left === null) {\n bestChild = node.right;\n } else {\n if (point[dimension] < node.obj[dimension]) {\n bestChild = node.left;\n } else {\n bestChild = node.right;\n }\n }\n nearestSearch(bestChild);\n if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {\n saveNode(node, ownDistance);\n }\n if (bestNodes.size() < maxNodes || Math.abs(linearDistance) < bestNodes.peek()[1]) {\n if (bestChild === node.left) {\n otherChild = node.right;\n } else {\n otherChild = node.left;\n }\n if (otherChild !== null) {\n nearestSearch(otherChild);\n }\n }\n }\n __name(nearestSearch, \"nearestSearch\");\n if (maxDistance) {\n for (i = 0; i < maxNodes; i += 1) {\n bestNodes.push([null, maxDistance]);\n }\n }\n if (self.root)\n nearestSearch(self.root);\n result = [];\n for (i = 0; i < Math.min(maxNodes, bestNodes.content.length); i += 1) {\n if (bestNodes.content[i][0]) {\n result.push([bestNodes.content[i][0].obj, bestNodes.content[i][1]]);\n }\n }\n return result;\n };\n this.balanceFactor = function() {\n function height(node) {\n if (node === null) {\n return 0;\n }\n return Math.max(height(node.left), height(node.right)) + 1;\n }\n __name(height, \"height\");\n function count(node) {\n if (node === null) {\n return 0;\n }\n return count(node.left) + count(node.right) + 1;\n }\n __name(count, \"count\");\n return height(self.root) / (Math.log(count(self.root)) / Math.log(2));\n };\n }\n __name(kdTree, \"kdTree\");\n function BinaryHeap(scoreFunction) {\n this.content = [];\n this.scoreFunction = scoreFunction;\n }\n __name(BinaryHeap, \"BinaryHeap\");\n BinaryHeap.prototype = {\n push: function(element) {\n this.content.push(element);\n this.bubbleUp(this.content.length - 1);\n },\n pop: function() {\n var result = this.content[0];\n var end = this.content.pop();\n if (this.content.length > 0) {\n this.content[0] = end;\n this.sinkDown(0);\n }\n return result;\n },\n peek: function() {\n return this.content[0];\n },\n remove: function(node) {\n var len = this.content.length;\n for (var i = 0; i < len; i++) {\n if (this.content[i] == node) {\n var end = this.content.pop();\n if (i != len - 1) {\n this.content[i] = end;\n if (this.scoreFunction(end) < this.scoreFunction(node))\n this.bubbleUp(i);\n else\n this.sinkDown(i);\n }\n return;\n }\n }\n throw new Error(\"Node not found.\");\n },\n size: function() {\n return this.content.length;\n },\n bubbleUp: function(n) {\n var element = this.content[n];\n while (n > 0) {\n var parentN = Math.floor((n + 1) / 2) - 1, parent = this.content[parentN];\n if (this.scoreFunction(element) < this.scoreFunction(parent)) {\n this.content[parentN] = element;\n this.content[n] = parent;\n n = parentN;\n } else {\n break;\n }\n }\n },\n sinkDown: function(n) {\n var length = this.content.length, element = this.content[n], elemScore = this.scoreFunction(element);\n while (true) {\n var child2N = (n + 1) * 2, child1N = child2N - 1;\n var swap = null;\n if (child1N < length) {\n var child1 = this.content[child1N], child1Score = this.scoreFunction(child1);\n if (child1Score < elemScore)\n swap = child1N;\n }\n if (child2N < length) {\n var child2 = this.content[child2N], child2Score = this.scoreFunction(child2);\n if (child2Score < (swap == null ? elemScore : child1Score)) {\n swap = child2N;\n }\n }\n if (swap != null) {\n this.content[n] = this.content[swap];\n this.content[swap] = element;\n n = swap;\n } else {\n break;\n }\n }\n }\n };\n exports2.kdTree = kdTree;\n exports2.BinaryHeap = BinaryHeap;\n });\n }\n});\n\n// node_modules/.pnpm/munkres-js@1.2.2/node_modules/munkres-js/munkres.js\nvar require_munkres = __commonJS({\n \"node_modules/.pnpm/munkres-js@1.2.2/node_modules/munkres-js/munkres.js\"(exports, module) {\n var MAX_SIZE = parseInt(Number.MAX_SAFE_INTEGER / 2) || (1 << 26) * (1 << 26);\n var DEFAULT_PAD_VALUE = 0;\n function Munkres() {\n this.C = null;\n this.row_covered = [];\n this.col_covered = [];\n this.n = 0;\n this.Z0_r = 0;\n this.Z0_c = 0;\n this.marked = null;\n this.path = null;\n }\n __name(Munkres, \"Munkres\");\n Munkres.prototype.pad_matrix = function(matrix, pad_value) {\n pad_value = pad_value || DEFAULT_PAD_VALUE;\n var max_columns = 0;\n var total_rows = matrix.length;\n var i;\n for (i = 0; i < total_rows; ++i)\n if (matrix[i].length > max_columns)\n max_columns = matrix[i].length;\n total_rows = max_columns > total_rows ? max_columns : total_rows;\n var new_matrix = [];\n for (i = 0; i < total_rows; ++i) {\n var row = matrix[i] || [];\n var new_row = row.slice();\n while (total_rows > new_row.length)\n new_row.push(pad_value);\n new_matrix.push(new_row);\n }\n return new_matrix;\n };\n Munkres.prototype.compute = function(cost_matrix, options) {\n options = options || {};\n options.padValue = options.padValue || DEFAULT_PAD_VALUE;\n this.C = this.pad_matrix(cost_matrix, options.padValue);\n this.n = this.C.length;\n this.original_length = cost_matrix.length;\n this.original_width = cost_matrix[0].length;\n var nfalseArray = [];\n while (nfalseArray.length < this.n)\n nfalseArray.push(false);\n this.row_covered = nfalseArray.slice();\n this.col_covered = nfalseArray.slice();\n this.Z0_r = 0;\n this.Z0_c = 0;\n this.path = this.__make_matrix(this.n * 2, 0);\n this.marked = this.__make_matrix(this.n, 0);\n var step = 1;\n var steps = {\n 1: this.__step1,\n 2: this.__step2,\n 3: this.__step3,\n 4: this.__step4,\n 5: this.__step5,\n 6: this.__step6\n };\n while (true) {\n var func = steps[step];\n if (!func)\n break;\n step = func.apply(this);\n }\n var results = [];\n for (var i = 0; i < this.original_length; ++i)\n for (var j = 0; j < this.original_width; ++j)\n if (this.marked[i][j] == 1)\n results.push([i, j]);\n return results;\n };\n Munkres.prototype.__make_matrix = function(n, val) {\n var matrix = [];\n for (var i = 0; i < n; ++i) {\n matrix[i] = [];\n for (var j = 0; j < n; ++j)\n matrix[i][j] = val;\n }\n return matrix;\n };\n Munkres.prototype.__step1 = function() {\n for (var i = 0; i < this.n; ++i) {\n var minval = Math.min.apply(Math, this.C[i]);\n for (var j = 0; j < this.n; ++j)\n this.C[i][j] -= minval;\n }\n return 2;\n };\n Munkres.prototype.__step2 = function() {\n for (var i = 0; i < this.n; ++i) {\n for (var j = 0; j < this.n; ++j) {\n if (this.C[i][j] === 0 && !this.col_covered[j] && !this.row_covered[i]) {\n this.marked[i][j] = 1;\n this.col_covered[j] = true;\n this.row_covered[i] = true;\n break;\n }\n }\n }\n this.__clear_covers();\n return 3;\n };\n Munkres.prototype.__step3 = function() {\n var count = 0;\n for (var i = 0; i < this.n; ++i) {\n for (var j = 0; j < this.n; ++j) {\n if (this.marked[i][j] == 1 && this.col_covered[j] == false) {\n this.col_covered[j] = true;\n ++count;\n }\n }\n }\n return count >= this.n ? 7 : 4;\n };\n Munkres.prototype.__step4 = function() {\n var done = false;\n var row = -1, col = -1, star_col = -1;\n while (!done) {\n var z = this.__find_a_zero();\n row = z[0];\n col = z[1];\n if (row < 0)\n return 6;\n this.marked[row][col] = 2;\n star_col = this.__find_star_in_row(row);\n if (star_col >= 0) {\n col = star_col;\n this.row_covered[row] = true;\n this.col_covered[col] = false;\n } else {\n this.Z0_r = row;\n this.Z0_c = col;\n return 5;\n }\n }\n };\n Munkres.prototype.__step5 = function() {\n var count = 0;\n this.path[count][0] = this.Z0_r;\n this.path[count][1] = this.Z0_c;\n var done = false;\n while (!done) {\n var row = this.__find_star_in_col(this.path[count][1]);\n if (row >= 0) {\n count++;\n this.path[count][0] = row;\n this.path[count][1] = this.path[count - 1][1];\n } else {\n done = true;\n }\n if (!done) {\n var col = this.__find_prime_in_row(this.path[count][0]);\n count++;\n this.path[count][0] = this.path[count - 1][0];\n this.path[count][1] = col;\n }\n }\n this.__convert_path(this.path, count);\n this.__clear_covers();\n this.__erase_primes();\n return 3;\n };\n Munkres.prototype.__step6 = function() {\n var minval = this.__find_smallest();\n for (var i = 0; i < this.n; ++i) {\n for (var j = 0; j < this.n; ++j) {\n if (this.row_covered[i])\n this.C[i][j] += minval;\n if (!this.col_covered[j])\n this.C[i][j] -= minval;\n }\n }\n return 4;\n };\n Munkres.prototype.__find_smallest = function() {\n var minval = MAX_SIZE;\n for (var i = 0; i < this.n; ++i)\n for (var j = 0; j < this.n; ++j)\n if (!this.row_covered[i] && !this.col_covered[j]) {\n if (minval > this.C[i][j])\n minval = this.C[i][j];\n }\n return minval;\n };\n Munkres.prototype.__find_a_zero = function() {\n for (var i = 0; i < this.n; ++i)\n for (var j = 0; j < this.n; ++j)\n if (this.C[i][j] === 0 && !this.row_covered[i] && !this.col_covered[j])\n return [i, j];\n return [-1, -1];\n };\n Munkres.prototype.__find_star_in_row = function(row) {\n for (var j = 0; j < this.n; ++j)\n if (this.marked[row][j] == 1)\n return j;\n return -1;\n };\n Munkres.prototype.__find_star_in_col = function(col) {\n for (var i = 0; i < this.n; ++i)\n if (this.marked[i][col] == 1)\n return i;\n return -1;\n };\n Munkres.prototype.__find_prime_in_row = function(row) {\n for (var j = 0; j < this.n; ++j)\n if (this.marked[row][j] == 2)\n return j;\n return -1;\n };\n Munkres.prototype.__convert_path = function(path, count) {\n for (var i = 0; i <= count; ++i)\n this.marked[path[i][0]][path[i][1]] = this.marked[path[i][0]][path[i][1]] == 1 ? 0 : 1;\n };\n Munkres.prototype.__clear_covers = function() {\n for (var i = 0; i < this.n; ++i) {\n this.row_covered[i] = false;\n this.col_covered[i] = false;\n }\n };\n Munkres.prototype.__erase_primes = function() {\n for (var i = 0; i < this.n; ++i)\n for (var j = 0; j < this.n; ++j)\n if (this.marked[i][j] == 2)\n this.marked[i][j] = 0;\n };\n function make_cost_matrix(profit_matrix, inversion_function) {\n var i, j;\n if (!inversion_function) {\n var maximum = -1 / 0;\n for (i = 0; i < profit_matrix.length; ++i)\n for (j = 0; j < profit_matrix[i].length; ++j)\n if (profit_matrix[i][j] > maximum)\n maximum = profit_matrix[i][j];\n inversion_function = /* @__PURE__ */ __name(function(x) {\n return maximum - x;\n }, \"inversion_function\");\n }\n var cost_matrix = [];\n for (i = 0; i < profit_matrix.length; ++i) {\n var row = profit_matrix[i];\n cost_matrix[i] = [];\n for (j = 0; j < row.length; ++j)\n cost_matrix[i][j] = inversion_function(profit_matrix[i][j]);\n }\n return cost_matrix;\n }\n __name(make_cost_matrix, \"make_cost_matrix\");\n function format_matrix(matrix) {\n var columnWidths = [];\n var i, j;\n for (i = 0; i < matrix.length; ++i) {\n for (j = 0; j < matrix[i].length; ++j) {\n var entryWidth = String(matrix[i][j]).length;\n if (!columnWidths[j] || entryWidth >= columnWidths[j])\n columnWidths[j] = entryWidth;\n }\n }\n var formatted = \"\";\n for (i = 0; i < matrix.length; ++i) {\n for (j = 0; j < matrix[i].length; ++j) {\n var s = String(matrix[i][j]);\n while (s.length < columnWidths[j])\n s = \" \" + s;\n formatted += s;\n if (j != matrix[i].length - 1)\n formatted += \" \";\n }\n if (i != matrix[i].length - 1)\n formatted += \"\\n\";\n }\n return formatted;\n }\n __name(format_matrix, \"format_matrix\");\n function computeMunkres(cost_matrix, options) {\n var m = new Munkres();\n return m.compute(cost_matrix, options);\n }\n __name(computeMunkres, \"computeMunkres\");\n computeMunkres.version = \"1.2.2\";\n computeMunkres.format_matrix = format_matrix;\n computeMunkres.make_cost_matrix = make_cost_matrix;\n computeMunkres.Munkres = Munkres;\n if (typeof module !== \"undefined\" && module.exports) {\n module.exports = computeMunkres;\n }\n }\n});\n\n// tracker.js\nvar require_tracker = __commonJS({\n \"tracker.js\"(exports) {\n var itemTrackedModule = require_ItemTracked();\n var ItemTracked = itemTrackedModule.ItemTracked;\n var kdTree = require_kdTree_min().kdTree;\n var iouAreas = require_utils().iouAreas;\n var munkres = require_munkres();\n var DEBUG_MODE = false;\n var iouDistance = /* @__PURE__ */ __name(function(item1, item2) {\n var iou = iouAreas(item1, item2);\n var distance = 1 - iou;\n if (distance > 1 - params.iouLimit) {\n distance = params.distanceLimit + 1;\n }\n return distance;\n }, \"iouDistance\");\n var params = {\n // DEFAULT_UNMATCHEDFRAMES_TOLERANCE\n // This the number of frame we wait when an object isn't matched before considering it gone\n unMatchedFramesTolerance: 5,\n // DEFAULT_IOU_LIMIT, exclude things from beeing matched if their IOU is lower than this\n // 1 means total overlap whereas 0 means no overlap\n iouLimit: 0.05,\n // Remove new objects fast if they could not be matched in the next frames.\n // Setting this to false ensures the object will stick around at least\n // unMatchedFramesTolerance frames, even if they could neven be matched in\n // subsequent frames.\n fastDelete: true,\n // The function to use to determine the distance between to detected objects\n distanceFunc: iouDistance,\n // The distance limit for matching. If values need to be excluded from\n // matching set their distance to something greater than the distance limit\n distanceLimit: 1e4,\n // The algorithm used to match tracks with new detections. Can be either\n // 'kdTree' or 'munkres'.\n matchingAlgorithm: \"kdTree\"\n };\n var mapOfItemsTracked = /* @__PURE__ */ new Map();\n var mapOfAllItemsTracked = /* @__PURE__ */ new Map();\n var keepAllHistoryInMemory = false;\n exports.computeDistance = iouDistance;\n exports.updateTrackedItemsWithNewFrame = function(detectionsOfThisFrame, frameNb) {\n var treeItemsTracked = new kdTree(Array.from(mapOfItemsTracked.values()), params.distanceFunc, [\"x\", \"y\", \"w\", \"h\"]);\n var treeDetectionsOfThisFrame = new kdTree(detectionsOfThisFrame, params.distanceFunc, [\"x\", \"y\", \"w\", \"h\"]);\n if (mapOfItemsTracked.size === 0) {\n detectionsOfThisFrame.forEach(function(itemDetected) {\n var newItemTracked = new ItemTracked(itemDetected, frameNb, params.unMatchedFramesTolerance, params.fastDelete);\n mapOfItemsTracked.set(newItemTracked.id, newItemTracked);\n treeItemsTracked.insert(newItemTracked);\n });\n } else {\n var matchedList = new Array(detectionsOfThisFrame.length);\n matchedList.fill(false);\n if (detectionsOfThisFrame.length > 0) {\n if (params.matchingAlgorithm === \"munkres\") {\n var trackedItemIds = Array.from(mapOfItemsTracked.keys());\n var costMatrix = Array.from(mapOfItemsTracked.values()).map((itemTracked) => {\n var predictedPosition = itemTracked.predictNextPosition();\n return detectionsOfThisFrame.map(\n (detection) => params.distanceFunc(predictedPosition, detection)\n );\n });\n mapOfItemsTracked.forEach(function(itemTracked) {\n itemTracked.makeAvailable();\n });\n munkres(costMatrix).filter((m) => costMatrix[m[0]][m[1]] <= params.distanceLimit).forEach((m) => {\n var itemTracked = mapOfItemsTracked.get(trackedItemIds[m[0]]);\n var updatedTrackedItemProperties = detectionsOfThisFrame[m[1]];\n matchedList[m[1]] = { idDisplay: itemTracked.idDisplay };\n itemTracked.makeUnavailable().update(updatedTrackedItemProperties, frameNb);\n });\n matchedList.forEach(function(matched, index) {\n if (!matched) {\n if (Math.min(...costMatrix.map((m) => m[index])) > params.distanceLimit) {\n var newItemTracked = ItemTracked(detectionsOfThisFrame[index], frameNb, params.unMatchedFramesTolerance, params.fastDelete);\n mapOfItemsTracked.set(newItemTracked.id, newItemTracked);\n newItemTracked.makeUnavailable();\n costMatrix.push(detectionsOfThisFrame.map(\n (detection) => params.distanceFunc(newItemTracked, detection)\n ));\n }\n }\n });\n } else if (params.matchingAlgorithm === \"kdTree\") {\n mapOfItemsTracked.forEach(function(itemTracked) {\n var predictedPosition = itemTracked.predictNextPosition();\n itemTracked.makeAvailable();\n var treeSearchResult = treeDetectionsOfThisFrame.nearest(predictedPosition, 1, params.distanceLimit)[0];\n var treeSearchResultWithoutPrediction = treeDetectionsOfThisFrame.nearest(itemTracked, 1, params.distanceLimit)[0];\n var treeSearchMultipleResults = treeDetectionsOfThisFrame.nearest(predictedPosition, 2, params.distanceLimit);\n if (treeSearchResult) {\n var indexClosestNewDetectedItem = detectionsOfThisFrame.indexOf(treeSearchResult[0]);\n if (!matchedList[indexClosestNewDetectedItem]) {\n matchedList[indexClosestNewDetectedItem] = {\n idDisplay: itemTracked.idDisplay\n };\n var updatedTrackedItemProperties = detectionsOfThisFrame[indexClosestNewDetectedItem];\n mapOfItemsTracked.get(itemTracked.id).makeUnavailable().update(updatedTrackedItemProperties, frameNb);\n } else {\n }\n }\n });\n } else {\n throw `Unknown matching algorithm \"${params.matchingAlgorithm}\"`;\n }\n } else {\n if (DEBUG_MODE) {\n console.log(\"[Tracker] Nothing detected for frame n\\xBA\" + frameNb);\n }\n mapOfItemsTracked.forEach(function(itemTracked) {\n itemTracked.makeAvailable();\n });\n }\n if (params.matchingAlgorithm === \"kdTree\") {\n if (mapOfItemsTracked.size > 0) {\n treeItemsTracked = new kdTree(Array.from(mapOfItemsTracked.values()), params.distanceFunc, [\"x\", \"y\", \"w\", \"h\"]);\n matchedList.forEach(function(matched, index) {\n if (!matched) {\n var treeSearchResult = treeItemsTracked.nearest(detectionsOfThisFrame[index], 1, params.distanceLimit)[0];\n if (!treeSearchResult) {\n var newItemTracked = ItemTracked(detectionsOfThisFrame[index], frameNb, params.unMatchedFramesTolerance, params.fastDelete);\n mapOfItemsTracked.set(newItemTracked.id, newItemTracked);\n treeItemsTracked.insert(newItemTracked);\n newItemTracked.makeUnavailable();\n } else {\n }\n }\n });\n }\n }\n mapOfItemsTracked.forEach(function(itemTracked) {\n if (itemTracked.available) {\n itemTracked.countDown(frameNb);\n itemTracked.updateTheoricalPositionAndSize();\n if (itemTracked.isDead()) {\n mapOfItemsTracked.delete(itemTracked.id);\n treeItemsTracked.remove(itemTracked);\n if (keepAllHistoryInMemory) {\n mapOfAllItemsTracked.set(itemTracked.id, itemTracked);\n }\n }\n }\n });\n }\n };\n exports.reset = function() {\n mapOfItemsTracked = /* @__PURE__ */ new Map();\n mapOfAllItemsTracked = /* @__PURE__ */ new Map();\n itemTrackedModule.reset();\n };\n exports.setParams = function(newParams) {\n Object.keys(newParams).forEach((key) => {\n params[key] = newParams[key];\n });\n };\n exports.enableKeepInMemory = function() {\n keepAllHistoryInMemory = true;\n };\n exports.disableKeepInMemory = function() {\n keepAllHistoryInMemory = false;\n };\n exports.getJSONOfTrackedItems = function(roundInt = true) {\n return Array.from(mapOfItemsTracked.values()).map(function(itemTracked) {\n return itemTracked.toJSON(roundInt);\n });\n };\n exports.getJSONDebugOfTrackedItems = function(roundInt = true) {\n return Array.from(mapOfItemsTracked.values()).map(function(itemTracked) {\n return itemTracked.toJSONDebug(roundInt);\n });\n };\n exports.getTrackedItemsInMOTFormat = function(frameNb) {\n return Array.from(mapOfItemsTracked.values()).map(function(itemTracked) {\n return itemTracked.toMOT(frameNb);\n });\n };\n exports.getAllTrackedItems = function() {\n return mapOfAllItemsTracked;\n };\n exports.getJSONOfAllTrackedItems = function() {\n return Array.from(mapOfAllItemsTracked.values()).map(function(itemTracked) {\n return itemTracked.toJSONGenericInfo();\n });\n };\n }\n});\nexport default require_tracker();\n/**\n * k-d Tree JavaScript - V 1.01\n *\n * https://github.com/ubilabs/kd-tree-javascript\n *\n * @author Mircea Pricop , 2012\n * @author Martin Kleppe , 2012\n * @author Ubilabs http://ubilabs.net, 2012\n * @license MIT License \n */\n"], - "mappings": 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a=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,i=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],a,i,0,0,2*Math.PI),t.stroke(),B.fillPolygons&&(t.fillStyle=B.useDepth?"rgba(255, 255, 200, 0.3)":B.color,t.fill())}}function Ur(e,t){var n;if(B.drawGaze&&((n=e.rotation)!=null&&n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*ge(e.rotation.angle.yaw)/90,s=e.box[1]+e.box[3]/2+e.box[2]*ge(e.rotation.angle.pitch)/90,A=new Path2D(` M ${e.box[0]+e.box[2]/2} ${e.box[1]} C diff --git a/dist/human.esm.js b/dist/human.esm.js index 45617875..7b100cd5 100644 --- a/dist/human.esm.js +++ b/dist/human.esm.js @@ -646,7 +646,7 @@ var U0 = Kt((ple, W0) => { }; de.getNumBitsAbs = function() { if (this.isNegative()) return this.eq(Vr) ? 64 : this.neg().getNumBitsAbs(); - for (var e = this.high != 0 ? this.high : this.low, t10 = 31; t10 > 0 && !(e & 1 << t10); t10--) ; + for (var e = this.high != 0 ? this.high : this.low, t10 = 31; t10 > 0 && (e & 1 << t10) == 0; t10--) ; return this.high != 0 ? t10 + 33 : t10 + 1; }; de.isZero = function() { @@ -981,7 +981,7 @@ var l1 = Kt((c1, Kw) => { function p(u, c) { var l, m, d, f, h, g = [], x = 128; for (c === (c | 0) ? (m = c, c = null) : (c = c + "\0", m = 0, x = Math.max(x, c.length)), d = 0, f = -32; f < x; ++f) c && (m ^= c.charCodeAt((f + 32) % c.length)), f === 0 && (h = m), m ^= m << 10, m ^= m >>> 15, m ^= m << 4, m ^= m >>> 13, f >= 0 && (h = h + 1640531527 | 0, l = g[f & 127] ^= m + h, d = l == 0 ? d + 1 : 0); - for (d >= 128 && (g[(c && c.length || 0) & 127] = -1), d = 127, f = 4 * 128; f > 0; --f) m = g[d + 34 & 127], l = g[d = d + 1 & 127], m ^= m << 13, l ^= l << 17, m ^= m >>> 15, l ^= l >>> 12, g[d] = m ^ l; + for (d >= 128 && (g[(c && c.length || 0) & 127] = -1), d = 127, f = 512; f > 0; --f) m = g[d + 34 & 127], l = g[d = d + 1 & 127], m ^= m << 13, l ^= l << 17, m ^= m >>> 15, l ^= l >>> 12, g[d] = m ^ l; u.w = h, u.X = g, u.i = d; } p(i, a); @@ -1132,7 +1132,7 @@ var zB = Kt(() => { var VB = Kt((Wg, Gv) => { var Uv = (() => { var r15 = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0; - return typeof __filename != "undefined" && (r15 = r15 || __filename), function(e) { + return typeof __filename != "undefined" && (r15 = r15 || __filename), (function(e) { e = e || {}; function t10() { return oe.buffer != He && Tt(oe.buffer), lt; @@ -2081,7 +2081,7 @@ var VB = Kt((Wg, Gv) => { }; } return e.ready; - }; + }); })(); typeof Wg == "object" && typeof Gv == "object" ? Gv.exports = Uv : typeof define == "function" && define.amd ? define([], function() { return Uv; @@ -2094,7 +2094,7 @@ var UB = Kt((e3t, WB) => { var GB = Kt((Ug, Kv) => { var Hv = (() => { var r15 = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0; - return typeof __filename != "undefined" && (r15 = r15 || __filename), function(e) { + return typeof __filename != "undefined" && (r15 = r15 || __filename), (function(e) { e = e || {}; var t10 = typeof e != "undefined" ? e : {}, o, n; t10.ready = new Promise(function(K, ae) { @@ -2776,7 +2776,7 @@ var GB = Kt((Ug, Kv) => { }; } return e.ready; - }; + }); })(); typeof Ug == "object" && typeof Kv == "object" ? Kv.exports = Hv : typeof define == "function" && define.amd ? define([], function() { return Hv; @@ -4375,7 +4375,7 @@ function Iw() { var T = Iw(); function y4(r15, e) { let t10 = { a: r15, b: e }; - return T.runKernel(uo, t10); + return T.runKernel("Add", t10); } var eu = {}; qe(eu, { isBrowser: () => kw, isMobile: () => w4, mockIsMobile: () => C4 }); @@ -4790,9 +4790,8 @@ function dk(r15) { return ir.join(r15); } function Ew(r15) { - let e = "/"; - for (r15 = r15.trim(); r15.endsWith(e); ) r15 = r15.slice(0, r15.length - 1); - let t10 = r15.split(e); + for (r15 = r15.trim(); r15.endsWith("/"); ) r15 = r15.slice(0, r15.length - 1); + let t10 = r15.split("/"); return t10[t10.length - 1]; } function id(r15, e) { @@ -4824,7 +4823,7 @@ function Sl(r15) { function T4() { let r15 = (t10) => { let o = t10 << 13, n = 0; - for (; !(o & 8388608); ) n -= 8388608, o <<= 1; + for (; (o & 8388608) === 0; ) n -= 8388608, o <<= 1; return o &= -8388609, n += 947912704, o | n; }, e = new Uint32Array(2048); e[0] = 0; @@ -5248,7 +5247,7 @@ function G4(r15, e) { let t10 = v(r15, "a", "add"), o = v(e, "b", "add"); [t10, o] = Oe(t10, o); let n = { a: t10, b: o }; - return T.runKernel(uo, n); + return T.runKernel("Add", n); } var Ce = N({ add_: G4 }); function H4(r15, e) { @@ -5279,7 +5278,7 @@ function j4(r15) { return T.runKernel(Ai, t10); } else { let t10 = { x: e }; - return T.runKernel(Xs, t10); + return T.runKernel("Abs", t10); } } var Qt = N({ abs_: j4 }); @@ -5307,12 +5306,12 @@ function Q4(r15) { var Ak = N({ addN_: Q4 }); function Z4(r15, e = null, t10 = false) { let n = { x: v(r15, "x", "all", "bool") }, s = { axis: e, keepDims: t10 }; - return T.runKernel(Go, n, s); + return T.runKernel("All", n, s); } var Fk = N({ all_: Z4 }); function J4(r15, e = null, t10 = false) { let n = { x: v(r15, "x", "any", "bool") }, s = { axis: e, keepDims: t10 }; - return T.runKernel(Ho, n, s); + return T.runKernel("Any", n, s); } var Pk = N({ any_: J4 }); function eH(r15, e = 0) { @@ -5710,7 +5709,7 @@ function UH(r15, e, t10, o, n) { var p2 = N({ conv3dTranspose_: UH }); function GH(r15) { let t10 = { x: v(r15, "x", "cos", "float32") }; - return T.runKernel(sn, t10); + return T.runKernel("Cos", t10); } var c2 = N({ cos_: GH }); function HH(r15) { @@ -5852,7 +5851,7 @@ function nK(r15, ...e) { var iu = N({ einsum_: nK }); function sK(r15) { let t10 = { x: v(r15, "x", "elu", "float32") }; - return T.runKernel(hn, t10); + return T.runKernel("Elu", t10); } var bd = N({ elu_: sK }); function aK(r15, e) { @@ -5865,7 +5864,7 @@ function iK(r15) { let e = v(r15, "x", "erf"); E(e.dtype === "int32" || e.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), e.dtype === "int32" && (e = Ue(e, "float32")); let t10 = { x: e }; - return T.runKernel(gn, t10); + return T.runKernel("Erf", t10); } var S2 = N({ erf_: iK }); function zw(r15, e) { @@ -5906,19 +5905,19 @@ function mK(r15, e) { } function fK(r15, e = null, t10 = false) { let n = { x: v(r15, "x", "max") }, s = { reductionIndices: e, keepDims: t10 }; - return T.runKernel(zn, n, s); + return T.runKernel("Max", n, s); } var Ra = N({ max_: fK }); function hK(r15, e = null, t10 = false) { let n = { x: v(r15, "x", "min") }, s = { axis: e, keepDims: t10 }; - return T.runKernel(Gn, n, s); + return T.runKernel("Min", n, s); } var Nl = N({ min_: hK }); function gK(r15, e) { let t10 = v(r15, "base", "pow"), o = v(e, "exp", "pow"); [t10, o] = Oe(t10, o); let n = { a: t10, b: o }; - return T.runKernel(ts, n); + return T.runKernel("Pow", n); } var ui = N({ pow_: gK }); function ke(r15, e) { @@ -5940,7 +5939,7 @@ function bK(r15, e = null, t10 = false) { let o = v(r15, "x", "sum"); o.dtype === "bool" && (o = Ue(o, "int32")); let n = { x: o }, s = { axis: e, keepDims: t10 }; - return T.runKernel(Ss, n, s); + return T.runKernel("Sum", n, s); } var ot = N({ sum_: bK }); function CK(r15, e = "euclidean", t10 = null, o = false) { @@ -5978,7 +5977,7 @@ function wK(r15, e = null, t10 = false) { var k2 = N({ euclideanNorm_: wK }); function SK(r15) { let t10 = { x: v(r15, "x", "exp") }; - return T.runKernel(yn, t10); + return T.runKernel("Exp", t10); } var _o = N({ exp_: SK }); function IK(r15, e = 0) { @@ -6086,13 +6085,13 @@ function LK(r15, e = 5, t10 = 1, o = 1, n = 0.5) { rank ${s.rank}.`), E(Ka(e), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${e}.`); let a = s, i = false; s.rank === 3 && (i = true, a = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])); - let p = { x: a }, u = { depthRadius: e, bias: t10, alpha: o, beta: n }, c = T.runKernel(Bn, p, u); + let p = { x: a }, u = { depthRadius: e, bias: t10, alpha: o, beta: n }, c = T.runKernel("LRN", p, u); return i ? W(c, [c.shape[1], c.shape[2], c.shape[3]]) : c; } var R2 = N({ localResponseNormalization_: LK }); function BK(r15) { let t10 = { x: v(r15, "x", "log", "float32") }; - return T.runKernel(Fn, t10); + return T.runKernel("Log", t10); } var pi = N({ log_: BK }); function zK(r15) { @@ -6158,7 +6157,7 @@ function Nd(r15) { } function HK(r15) { let t10 = { x: v(r15, "x", "neg") }; - return T.runKernel(pa, t10); + return T.runKernel("Neg", t10); } var pr = N({ neg_: HK }); function KK(r15) { @@ -6175,7 +6174,7 @@ function jK(r15, e) { let t10 = v(r15, "a", "sub"), o = v(e, "b", "sub"); [t10, o] = Oe(t10, o); let n = { a: t10, b: o }; - return T.runKernel(Ts, n); + return T.runKernel("Sub", n); } var Te = N({ sub_: jK }); function XK(r15, e = -1) { @@ -6314,7 +6313,7 @@ function pq(r15, e) { let t10 = v(r15, "a", "mod"), o = v(e, "b", "mod"); [t10, o] = Oe(t10, o); let n = { a: t10, b: o }; - return T.runKernel(qn, n); + return T.runKernel("Mod", n); } var z2 = N({ mod_: pq }); function cq(r15, e = null, t10 = false) { @@ -6712,7 +6711,7 @@ function l6(r15) { var F1 = N({ sign_: l6 }); function m6(r15) { let t10 = { x: v(r15, "x", "sin", "float32") }; - return T.runKernel(gs, t10); + return T.runKernel("Sin", t10); } var P1 = N({ sin_: m6 }); function d6(r15) { @@ -6750,7 +6749,7 @@ var V1 = N({ softmax_: y6 }); function b6(r15) { E(r15.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${r15.dtype}.`); let e = { input: r15 }; - return T.runKernel(zi, e); + return T.runKernel("FFT", e); } var uc = N({ fft_: b6 }); function C6(r15) { @@ -6825,7 +6824,7 @@ function _6(r15, e, t10, o, n = 0, s = 0, a = 0, i = 0, p = 0) { var W1 = N({ stridedSlice_: _6 }); function E6(r15) { let t10 = { x: v(r15, "x", "tan", "float32") }; - return T.runKernel(_s, t10); + return T.runKernel("Tan", t10); } var U1 = N({ tan_: E6 }); function Jt(r15, e) { @@ -8599,9 +8598,9 @@ function AX(r15, e, t10) { } function FX(r15, e, t10, o, n, s, a, i, p) { let u; - if (o == null ? (u = new Array(e.length), u.fill(1)) : u = o, a != null && a & a - 1) throw new Error("Multiple ellipses in slice is not allowed."); + if (o == null ? (u = new Array(e.length), u.fill(1)) : u = o, a != null && (a & a - 1) !== 0) throw new Error("Multiple ellipses in slice is not allowed."); let c = false, l = { dims: u.length, numAddAxisAfterEllipsis: 0, begin: e.slice(), end: t10.slice(), strides: u.slice(), beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p }; - for (let C = 0; C < l.dims; C++) c && 1 << C & i && l.numAddAxisAfterEllipsis++, 1 << C & a && (c = true); + for (let C = 0; C < l.dims; C++) c && (1 << C & i) !== 0 && l.numAddAxisAfterEllipsis++, 1 << C & a && (c = true); c || (l.ellipsisMask |= 1 << l.dims, l.dims++); let m = { dims: r15.length, beginMask: 0, endMask: 0, beginValid: false, endValid: false }; PX(l, m); @@ -11290,7 +11289,7 @@ var W8 = (r15) => { let o = new Float32Array(y.sizeFromShape(e.shape)), n = t10.data.get(e.dataId).values; return o = BS(n), t10.makeOutput(o, e.shape, e.dtype); }; -var t_ = { kernelName: Xs, backendName: "cpu", kernelFunc: W8 }; +var t_ = { kernelName: "Abs", backendName: "cpu", kernelFunc: W8 }; function Ve(r15) { return (e, t10, o, n, s) => { let a = w.assertAndGetBroadcastShape(e, t10), i = a.length, p = y.computeStrides(a), u = y.sizeFromShape(a), c = y.getTypedArrayFromDType(s, u), l = e.length, m = t10.length, d = y.computeStrides(e), f = y.computeStrides(t10), h = w.getBroadcastDims(e, a), g = w.getBroadcastDims(t10, a); @@ -11394,10 +11393,10 @@ function bc(r15) { return [l, m, i]; }; } -var VS = Ve((r15, e) => r15 + e); -var U8 = bc((r15, e, t10, o) => ({ real: r15 + t10, imag: e + o })); -var Pa = Ye(uo, VS, U8); -var a_ = { kernelName: uo, backendName: "cpu", kernelFunc: Pa }; +var VS = Ve(((r15, e) => r15 + e)); +var U8 = bc(((r15, e, t10, o) => ({ real: r15 + t10, imag: e + o }))); +var Pa = Ye("Add", VS, U8); +var a_ = { kernelName: "Add", backendName: "cpu", kernelFunc: Pa }; function Cc(r15, e, t10, o, n) { let s = y.sizeFromShape(o), a = y.makeZerosTypedArray(n, t10); for (let i = 0; i < r15.length; i++) { @@ -11416,7 +11415,7 @@ function Nf(r15, e, t10, o = false) { } return a; } -var WS = Ve((r15, e) => r15 & e); +var WS = Ve(((r15, e) => r15 & e)); var G8 = Ye(qa, WS); var i_ = { kernelName: qa, backendName: "cpu", kernelFunc: G8 }; function jt(r15) { @@ -11471,8 +11470,8 @@ var GS = Ve((r15, e) => r15 === e ? 1 : 0); var HS = Ye(xn, GS, null, "bool"); var p_ = { kernelName: xn, backendName: "cpu", kernelFunc: HS }; var KS = jt((r15) => Math.exp(r15)); -var qS = Ar(yn, KS, "float32"); -var c_ = { kernelName: yn, backendName: "cpu", kernelFunc: qS }; +var qS = Ar("Exp", KS, "float32"); +var c_ = { kernelName: "Exp", backendName: "cpu", kernelFunc: qS }; var jS = jt((r15) => Math.expm1(r15)); var K8 = Ar(bn, jS); var l_ = { kernelName: bn, backendName: "cpu", kernelFunc: K8 }; @@ -11524,8 +11523,8 @@ function Ef(r15, e, t10) { return n; } var tI = jt((r15) => Math.log(r15)); -var J8 = Ar(Fn, tI); -var y_ = { kernelName: Fn, backendName: "cpu", kernelFunc: J8 }; +var J8 = Ar("Log", tI); +var y_ = { kernelName: "Log", backendName: "cpu", kernelFunc: J8 }; function $f(r15, e, t10, o) { let n = y.getTypedArrayFromDType(o, y.sizeFromShape(t10)); for (let s = 0; s < n.length; ++s) { @@ -11538,14 +11537,14 @@ function $f(r15, e, t10, o) { } return n; } -var rI = Ve((r15, e) => Math.max(r15, e)); +var rI = Ve(((r15, e) => Math.max(r15, e))); var eY = Ye(Vn, rI); var b_ = { kernelName: Vn, backendName: "cpu", kernelFunc: eY }; -var oI = Ve((r15, e) => Math.min(r15, e)); +var oI = Ve(((r15, e) => Math.min(r15, e))); var tY = Ye(Hn, oI); var C_ = { kernelName: Hn, backendName: "cpu", kernelFunc: tY }; -var zl = Ve((r15, e) => r15 * e); -var rY = bc((r15, e, t10, o) => ({ real: r15 * t10 - e * o, imag: r15 * o + e * t10 })); +var zl = Ve(((r15, e) => r15 * e)); +var rY = bc(((r15, e, t10, o) => ({ real: r15 * t10 - e * o, imag: r15 * o + e * t10 }))); var ip = Ye(Xn, zl, rY); var w_ = { kernelName: Xn, backendName: "cpu", kernelFunc: ip }; function nI(r15, e, t10) { @@ -11558,8 +11557,8 @@ function oY(r15) { let n = t10.data.get(o.dataId).values, [s, a] = nI(n, o.shape, o.dtype); return t10.makeTensorInfo(a, o.dtype, s); } -var S_ = { kernelName: pa, backendName: "cpu", kernelFunc: oY }; -var sI = Ve((r15, e) => r15 !== e ? 1 : 0); +var S_ = { kernelName: "Neg", backendName: "cpu", kernelFunc: oY }; +var sI = Ve(((r15, e) => r15 !== e ? 1 : 0)); var nY = Ye(Yn, sI, null, "bool"); var I_ = { kernelName: Yn, backendName: "cpu", kernelFunc: nY }; function wc(r15, e, t10, o, n) { @@ -12048,10 +12047,10 @@ function Sc(r15, e, t10, o, n, s = false, a = 0) { var F_ = jt((r15) => Math.sqrt(r15)); var dY = Ie(ws, (r15) => Math.sqrt(r15)); var P_ = { kernelName: ws, backendName: "cpu", kernelFunc: dY }; -var cI = Ve((r15, e) => { +var cI = Ve(((r15, e) => { let t10 = r15 - e; return t10 * t10; -}); +})); var fY = Ye(ks, cI); var O_ = { kernelName: ks, backendName: "cpu", kernelFunc: fY }; var lI = jt((r15, e) => { @@ -12182,10 +12181,10 @@ function mp(r15, e) { for (let o = 0; o < r15.length; ++o) t10[o] = y.fingerPrint64(r15[o]).modulo(e).getLowBitsUnsigned(); return t10; } -var dI = Ve((r15, e) => r15 - e); -var xY = bc((r15, e, t10, o) => ({ real: r15 - t10, imag: e - o })); -var Vl = Ye(Ts, dI, xY); -var L_ = { kernelName: Ts, backendName: "cpu", kernelFunc: Vl }; +var dI = Ve(((r15, e) => r15 - e)); +var xY = bc(((r15, e, t10, o) => ({ real: r15 - t10, imag: e - o }))); +var Vl = Ye("Sub", dI, xY); +var L_ = { kernelName: "Sub", backendName: "cpu", kernelFunc: Vl }; function Mf(r15, e) { let t10 = new Array(r15.rank); for (let n = 0; n < t10.length; n++) t10[n] = r15.shape[n] * e[n]; @@ -12259,8 +12258,8 @@ function dp(r15, e, t10, o) { } var yY = "4.22.0"; tu("cpu", () => new xc(), 1); -var fI = Ie(hn, (r15) => r15 >= 0 ? r15 : Math.exp(r15) - 1); -var z_ = { kernelName: hn, backendName: "cpu", kernelFunc: fI }; +var fI = Ie("Elu", (r15) => r15 >= 0 ? r15 : Math.exp(r15) - 1); +var z_ = { kernelName: "Elu", backendName: "cpu", kernelFunc: fI }; function hI(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { alpha: s } = o; Q([n], "leakyRelu"); @@ -12375,7 +12374,7 @@ function vY(r15) { } return g; } -var Q_ = { kernelName: Go, backendName: "cpu", kernelFunc: vY }; +var Q_ = { kernelName: "All", backendName: "cpu", kernelFunc: vY }; function kY(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { axis: s, keepDims: a } = o; Q(n, "any"); @@ -12398,7 +12397,7 @@ function kY(r15) { } return g; } -var Z_ = { kernelName: Ho, backendName: "cpu", kernelFunc: kY }; +var Z_ = { kernelName: "Any", backendName: "cpu", kernelFunc: kY }; function NY(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { axis: s } = o; Q(n, "argMax"); @@ -12872,8 +12871,8 @@ function jY(r15) { return t10.makeTensorInfo(m.shape, m.dtype, m.values); } var vE = { kernelName: nn, backendName: "cpu", kernelFunc: jY }; -var XY = Ie(sn, (r15) => Math.cos(r15)); -var kE = { kernelName: sn, backendName: "cpu", kernelFunc: XY }; +var XY = Ie("Cos", (r15) => Math.cos(r15)); +var kE = { kernelName: "Cos", backendName: "cpu", kernelFunc: XY }; var YY = Ie(an, (r15) => Math.cosh(r15)); var NE = { kernelName: an, backendName: "cpu", kernelFunc: YY }; function QY(r15) { @@ -13218,7 +13217,7 @@ function fi(r15) { } return t10.disposeIntermediateTensorInfo(i), c != null && t10.disposeIntermediateTensorInfo(m), g; } -var zE = { kernelName: Ss, backendName: "cpu", kernelFunc: fi }; +var zE = { kernelName: "Sum", backendName: "cpu", kernelFunc: fi }; function aQ(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = w.decodeEinsumEquation(n, s.length); w.checkEinsumDimSizes(a.length, p, s); @@ -13254,11 +13253,11 @@ var cQ = w.ERF_A2; var lQ = w.ERF_A3; var mQ = w.ERF_A4; var dQ = w.ERF_A5; -var fQ = Ie(gn, (r15) => { +var fQ = Ie("Erf", (r15) => { let e = Math.sign(r15), t10 = Math.abs(r15), o = 1 / (1 + uQ * t10); return e * (1 - ((((dQ * o + mQ) * o + lQ) * o + cQ) * o + pQ) * o * Math.exp(-t10 * t10)); }); -var UE = { kernelName: gn, backendName: "cpu", kernelFunc: fQ }; +var UE = { kernelName: "Erf", backendName: "cpu", kernelFunc: fQ }; function kc(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { input: n } = e, { dim: s } = o, a = n.shape.length, i = n.shape.slice(), p = s; return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), We({ inputs: { x: n }, backend: t10, attrs: { shape: i } }); @@ -13318,7 +13317,7 @@ function bQ(r15) { let { inputs: e, backend: t10 } = r15, { input: o } = e, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = We({ inputs: { x: o }, backend: t10, attrs: { shape: [a, s] } }), p = Vf(i, false, t10), u = We({ inputs: { x: p }, backend: t10, attrs: { shape: o.shape } }); return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(p), u; } -var HE = { kernelName: zi, backendName: "cpu", kernelFunc: bQ }; +var HE = { kernelName: "FFT", backendName: "cpu", kernelFunc: bQ }; function Hl(r15) { let { backend: e, attrs: t10 } = r15, { shape: o, value: n, dtype: s } = t10, a = s || y.inferDtype(n), i = y.getArrayFromDType(a, y.sizeFromShape(o)); return CQ(i, n, a), e.makeTensorInfo(o, a, i); @@ -13447,7 +13446,7 @@ function OQ(r15) { } return t10.makeTensorInfo(n.shape, n.dtype, d); } -var i$ = { kernelName: Bn, backendName: "cpu", kernelFunc: OQ }; +var i$ = { kernelName: "LRN", backendName: "cpu", kernelFunc: OQ }; function MQ(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o; Q(a, "LRNGrad"); @@ -13475,7 +13474,7 @@ function II(r15) { let [f, h] = w.computeOutAndReduceShapes(p, l), g = y.sizeFromShape(h), x = $f(d, g, f, n.dtype), b = i.write(x, f, n.dtype), C = f; return a && (C = w.expandShapeToKeepDim(f, c)), { dataId: b, shape: C, dtype: n.dtype }; } -var p$ = { kernelName: zn, backendName: "cpu", kernelFunc: II }; +var p$ = { kernelName: "Max", backendName: "cpu", kernelFunc: II }; function LQ(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e; Q(n, "maxPool"); @@ -13587,7 +13586,7 @@ function UQ(r15) { } return g; } -var x$ = { kernelName: Gn, backendName: "cpu", kernelFunc: UQ }; +var x$ = { kernelName: "Min", backendName: "cpu", kernelFunc: UQ }; function GQ(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { paddings: s, mode: a } = o; Q(n, "mirrorPad"); @@ -13602,12 +13601,12 @@ function GQ(r15) { return { dataId: t10.write(x, i, n.dtype), shape: i, dtype: n.dtype }; } var y$ = { kernelName: Kn, backendName: "cpu", kernelFunc: GQ }; -var HQ = Ve((r15, e) => { +var HQ = Ve(((r15, e) => { let t10 = r15 % e; return r15 < 0 && e < 0 || r15 >= 0 && e >= 0 ? t10 : (t10 + e) % e; -}); -var KQ = Ye(qn, HQ); -var b$ = { kernelName: qn, backendName: "cpu", kernelFunc: KQ }; +})); +var KQ = Ye("Mod", HQ); +var b$ = { kernelName: "Mod", backendName: "cpu", kernelFunc: KQ }; var w$ = zp(jw()); function vI(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { logits: n } = e, { dim: s } = o, a = n.shape.length, i = s; @@ -13716,8 +13715,8 @@ function t7(r15) { } var Wf = { kernelName: es, backendName: "cpu", kernelFunc: t7 }; var r72 = Ve((r15, e) => Math.pow(r15, e)); -var o7 = Ye(ts, r72); -var R$ = { kernelName: ts, backendName: "cpu", kernelFunc: o7 }; +var o7 = Ye("Pow", r72); +var R$ = { kernelName: "Pow", backendName: "cpu", kernelFunc: o7 }; function n7(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((x) => t10.data.get(x.dataId).values), u = n.map((x) => x.shape), c = t10.data.get(s.dataId).values, l = t10.data.get(a.dataId).values, [m, d, f] = Rf(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => t10.makeTensorInfo([x.length], "int32", x)), g = t10.makeTensorInfo(f, s.dtype, d); return h.concat([g]); @@ -13920,8 +13919,8 @@ var S7 = Ie(hs, (r15) => r15 >= 0 ? w7 * r15 : C7 * (Math.exp(r15) - 1)); var j$ = { kernelName: hs, backendName: "cpu", kernelFunc: S7 }; var I7 = Ie(ys, (r15) => r15 < 0 ? -1 : r15 > 0 ? 1 : 0); var X$ = { kernelName: ys, backendName: "cpu", kernelFunc: I7 }; -var v7 = Ie(gs, (r15) => Math.sin(r15)); -var Y$ = { kernelName: gs, backendName: "cpu", kernelFunc: v7 }; +var v7 = Ie("Sin", (r15) => Math.sin(r15)); +var Y$ = { kernelName: "Sin", backendName: "cpu", kernelFunc: v7 }; var k7 = Ie(xs, (r15) => Math.sinh(r15)); var Q$ = { kernelName: xs, backendName: "cpu", kernelFunc: k7 }; var N7 = 11920928955078125e-23; @@ -14082,8 +14081,8 @@ function B7(r15) { return t10.makeTensorInfo(s.shape, "int32", i); } var mR = { kernelName: Xi, backendName: "cpu", kernelFunc: B7 }; -var z7 = Ie(_s, (r15) => Math.tan(r15)); -var dR = { kernelName: _s, backendName: "cpu", kernelFunc: z7 }; +var z7 = Ie("Tan", (r15) => Math.tan(r15)); +var dR = { kernelName: "Tan", backendName: "cpu", kernelFunc: z7 }; var V7 = Ie(Es, (r15) => Math.tanh(r15)); var fR = { kernelName: Es, backendName: "cpu", kernelFunc: V7 }; function W7(r15) { @@ -17855,7 +17854,7 @@ function wp(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { axis: s, keepDims: a } = o; return JD(n, s, a, t10); } -var eA = { kernelName: Ss, backendName: "webgl", kernelFunc: wp }; +var eA = { kernelName: "Sum", backendName: "webgl", kernelFunc: wp }; function bt(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { perm: s } = o, a = t10, i = n.shape.length, p = new Array(i); for (let c = 0; c < p.length; c++) p[c] = n.shape[s[c]]; @@ -17911,7 +17910,7 @@ function C9(r15) { let n; return A().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Fr(o.shape, oA) : n = new tr(o.shape, oA), t10.runWebGLProgram(n, [o], o.dtype); } -var nA = { kernelName: Xs, backendName: "webgl", kernelFunc: C9 }; +var nA = { kernelName: "Abs", backendName: "webgl", kernelFunc: C9 }; var w9 = Wt + ` if (abs(x) > 1.) { return NAN; @@ -17927,7 +17926,7 @@ var v9 = xe({ opSnippet: I9 }); var aA = { kernelName: Wo, backendName: "webgl", kernelFunc: v9 }; var iA = "return a + b;"; var k9 = nt({ opSnippet: iA, packedOpSnippet: iA, supportsComplex: true, cpuKernelImpl: LR }); -var uA = { kernelName: uo, backendName: "webgl", kernelFunc: k9 }; +var uA = { kernelName: "Add", backendName: "webgl", kernelFunc: k9 }; var bh = class { constructor(e, t10) { this.outputShape = [], this.outputShape = e, this.variableNames = t10.map((s, a) => `T${a}`); @@ -17987,7 +17986,7 @@ function N9(r15) { } else x = te({ inputs: { x: g }, backend: t10, attrs: { shape: m } }); return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), x; } -var cA = { kernelName: Go, backendName: "webgl", kernelFunc: N9 }; +var cA = { kernelName: "All", backendName: "webgl", kernelFunc: N9 }; function T9(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = w.getAxesPermutation(u, i), l = n; c != null && (l = bt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = w.getInnerMostAxes(u.length, i)), w.assertAxesAreInnerMostDims("any", u, i); @@ -17998,7 +17997,7 @@ function T9(r15) { } else x = te({ inputs: { x: g }, backend: t10, attrs: { shape: m } }); return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), x; } -var lA = { kernelName: Ho, backendName: "webgl", kernelFunc: T9 }; +var lA = { kernelName: "Any", backendName: "webgl", kernelFunc: T9 }; var Sh = class { constructor(e, t10, o) { this.variableNames = ["A"]; @@ -18192,7 +18191,6 @@ var Us = class { this.outputShape = e.outShape; let h = t10 === "avg", g = `((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`, x = `(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`, b = "0.0"; if (h || (b = "-1.0 / 1e-20"), o) { - let R = ">="; this.userCode = ` const ivec2 strides = ivec2(${i}, ${p}); const ivec2 pads = ivec2(${d}, ${f}); @@ -18235,7 +18233,7 @@ var Us = class { // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); - if (value ${R} currMinMaxValue) { + if (value >= currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n ? s ? g : x : `wR * ${m} + wC`}; @@ -18350,7 +18348,6 @@ var bu = class { this.outputShape = e.outShape; let C = t10 === "avg", S = "0.0"; if (C || (S = "-1.0 / 1e-20"), o) { - let P = ">="; this.userCode = ` const ivec3 strides = ivec3(${i}, ${p}, ${u}); @@ -18402,7 +18399,7 @@ var bu = class { // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); - if (value ${P} currMinMaxValue) { + if (value >= currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n ? s ? `(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch` : `((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch` : `wD * ${f} * ${h} + @@ -20010,7 +20007,7 @@ var fJ = ` return result; `; var hJ = xe({ opSnippet: dJ, packedOpSnippet: fJ }); -var XA = { kernelName: sn, backendName: "webgl", kernelFunc: hJ }; +var XA = { kernelName: "Cos", backendName: "webgl", kernelFunc: hJ }; var gJ = ` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; @@ -20703,7 +20700,7 @@ var $J = ` return result; `; var RJ = xe({ opSnippet: EJ, packedOpSnippet: $J }); -var cF = { kernelName: hn, backendName: "webgl", kernelFunc: RJ }; +var cF = { kernelName: "Elu", backendName: "webgl", kernelFunc: RJ }; var DJ = "return (b >= 0.0) ? a : a * (b + 1.0);"; var AJ = ` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); @@ -20737,7 +20734,7 @@ var LJ = ` return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `; var BJ = xe({ opSnippet: LJ }); -var dF = { kernelName: gn, backendName: "webgl", kernelFunc: BJ }; +var dF = { kernelName: "Erf", backendName: "webgl", kernelFunc: BJ }; var zJ = Fo + ` return exp(x); `; @@ -20752,7 +20749,7 @@ var VJ = ` return result; `; var kv = xe({ opSnippet: zJ, packedOpSnippet: VJ, cpuKernelImpl: HR, dtype: "float32" }); -var fF = { kernelName: yn, backendName: "webgl", kernelFunc: kv }; +var fF = { kernelName: "Exp", backendName: "webgl", kernelFunc: kv }; function eg(r15) { let { inputs: e, attrs: t10, backend: o } = r15, { dim: n } = t10, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n; return n < 0 && (y.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), te({ inputs: { x: s }, backend: o, attrs: { shape: i } }); @@ -20816,7 +20813,7 @@ function UJ(r15) { let { inputs: e, backend: t10 } = r15, { input: o } = e; return tg(o, false, t10); } -var yF = { kernelName: zi, backendName: "webgl", kernelFunc: UJ }; +var yF = { kernelName: "FFT", backendName: "webgl", kernelFunc: UJ }; var rg = class { constructor(e, t10) { this.outputShape = [], this.customUniforms = [{ name: "value", type: "float" }], this.variableNames = ["x"], this.outputShape = e, this.userCode = ` @@ -21157,7 +21154,7 @@ var Cee = ` return result; `; var wee = xe({ opSnippet: bee, packedOpSnippet: Cee, cpuKernelImpl: tD }); -var LF = { kernelName: Fn, backendName: "webgl", kernelFunc: wee }; +var LF = { kernelName: "Log", backendName: "webgl", kernelFunc: wee }; var See = Fo + ` return log(1.0 + x); `; @@ -21286,7 +21283,7 @@ var Dee = (r15) => { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u = A().getBool("WEBGL_PACK_NORMALIZATION") ? new pg(n.shape, s, a, i, p) : new ug(n.shape, s, a, i, p); return t10.runWebGLProgram(u, [n], n.dtype); }; -var UF = { kernelName: Bn, backendName: "webgl", kernelFunc: Dee }; +var UF = { kernelName: "LRN", backendName: "webgl", kernelFunc: Dee }; var cg = class { constructor(e, t10, o, n, s) { this.variableNames = ["inputImage", "outputImage", "dy"], this.outputShape = [], this.outputShape = e, this.depth = e[3], this.depthRadius = t10, this.bias = o, this.alpha = n, this.beta = s, this.userCode = ` @@ -21381,7 +21378,7 @@ function _v(r15) { } else x = HF(d, h, g, t10); return l && t10.disposeIntermediateTensorInfo(d), x; } -var KF = { kernelName: zn, backendName: "webgl", kernelFunc: _v }; +var KF = { kernelName: "Max", backendName: "webgl", kernelFunc: _v }; var Fee = Bc + ` return max(a, b); `; @@ -21595,7 +21592,7 @@ function Vee(r15) { } else x = te({ inputs: { x: g }, backend: t10, attrs: { shape: m } }); return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), x; } -var r32 = { kernelName: Gn, backendName: "webgl", kernelFunc: Vee }; +var r32 = { kernelName: "Min", backendName: "webgl", kernelFunc: Vee }; var Wee = Bc + ` return min(a, b); `; @@ -21733,7 +21730,7 @@ var qee = ` return result; `; var jee = nt({ opSnippet: Kee, packedOpSnippet: qee }); -var s3 = { kernelName: qn, backendName: "webgl", kernelFunc: jee }; +var s3 = { kernelName: "Mod", backendName: "webgl", kernelFunc: jee }; var hg = class { constructor(e, t10, o) { this.variableNames = ["probs"], this.customUniforms = [{ name: "seed", type: "float" }], this.outputShape = [e, o], this.userCode = ` @@ -21787,7 +21784,7 @@ var Ev = nt({ opSnippet: Xee, packedOpSnippet: Yee, checkOutOfBounds: true }); var a3 = { kernelName: fn, backendName: "webgl", kernelFunc: Ev }; var i3 = "return a - b;"; var $v = nt({ opSnippet: i3, packedOpSnippet: i3, supportsComplex: true, cpuKernelImpl: kD }); -var u3 = { kernelName: Ts, backendName: "webgl", kernelFunc: $v }; +var u3 = { kernelName: "Sub", backendName: "webgl", kernelFunc: $v }; function Rv(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { logits: n } = e, { dim: s } = o, a = y.parseAxisParam([s], n.shape), i = _v({ inputs: { x: n }, backend: t10, attrs: { reductionIndices: a, keepDims: false } }), p = w.expandShapeToKeepDim(i.shape, a), u = te({ inputs: { x: i }, backend: t10, attrs: { shape: p } }), c = $v({ inputs: { a: n, b: u }, backend: t10 }), l = kv({ inputs: { x: c }, backend: t10 }), m = wp({ inputs: { x: l }, backend: t10, attrs: { axis: a, keepDims: false } }), d = te({ inputs: { x: m }, backend: t10, attrs: { shape: p } }), f = Ev({ inputs: { a: l, b: d }, backend: t10 }); return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), f; @@ -21821,7 +21818,7 @@ function ete(r15) { let n; return A().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Fr(o.shape, Jee) : n = new tr(o.shape, Zee), t10.runWebGLProgram(n, [o], o.dtype); } -var l3 = { kernelName: pa, backendName: "webgl", kernelFunc: ete }; +var l3 = { kernelName: "Neg", backendName: "webgl", kernelFunc: ete }; var tte = Vt.nonMaxSuppressionV3Impl; function rte(r15) { w.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); @@ -22001,7 +21998,7 @@ var cte = ` return result; `; var lte = nt({ opSnippet: pte, packedOpSnippet: cte }); -var w3 = { kernelName: ts, backendName: "webgl", kernelFunc: lte }; +var w3 = { kernelName: "Pow", backendName: "webgl", kernelFunc: lte }; function mte(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = [], u = y.parseAxisParam(s, n.shape), c = u, l = w.getAxesPermutation(c, i), m = n; l != null && (m = bt({ inputs: { x: n }, backend: t10, attrs: { perm: l } }), c = w.getInnerMostAxes(c.length, i), p.push(m)), w.assertAxesAreInnerMostDims("prod", c, i); @@ -22810,7 +22807,7 @@ var Gte = ` return result; `; var Hte = xe({ opSnippet: Ute, packedOpSnippet: Gte }); -var G3 = { kernelName: gs, backendName: "webgl", kernelFunc: Hte }; +var G3 = { kernelName: "Sin", backendName: "webgl", kernelFunc: Hte }; var Kte = ` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; @@ -23000,7 +22997,7 @@ function mre(r15) { var cP = { kernelName: Xi, backendName: "webgl", kernelFunc: mre }; var dre = "return tan(x);"; var fre = xe({ opSnippet: dre }); -var lP = { kernelName: _s, backendName: "webgl", kernelFunc: fre }; +var lP = { kernelName: "Tan", backendName: "webgl", kernelFunc: fre }; var hre = ` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); @@ -23513,7 +23510,7 @@ function he(r15, e) { } return { kernelName: r15, backendName: "wasm", setupFunc: o, kernelFunc: n }; } -var IP = he(Xs); +var IP = he("Abs"); var vP = he(Vo); var kP = he(Wo); function Ge(r15, e, t10) { @@ -23530,7 +23527,7 @@ function Ge(r15, e, t10) { return { kernelName: r15, backendName: "wasm", setupFunc: n, kernelFunc: s }; } var Tre = true; -var NP = Ge(uo, Tre); +var NP = Ge("Add", Tre); var TP; function _re(r15) { TP = r15.wasm.cwrap(Uo, null, ["array", "number", "number", "number"]); @@ -23593,7 +23590,7 @@ function Tr(r15, e, t10) { } var DP; function Are(r15) { - DP = r15.wasm.cwrap(Go, null, ["number, number, number"]); + DP = r15.wasm.cwrap("All", null, ["number, number, number"]); } function Fre(r15) { let { backend: e, inputs: t10, attrs: o } = r15, { axis: n, keepDims: s } = o, { x: a } = t10, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e); @@ -23614,10 +23611,10 @@ function Fre(r15) { } return b; } -var AP = { kernelName: Go, backendName: "wasm", setupFunc: Are, kernelFunc: Fre }; +var AP = { kernelName: "All", backendName: "wasm", setupFunc: Are, kernelFunc: Fre }; var FP; function Pre(r15) { - FP = r15.wasm.cwrap(Ho, null, ["number, number, number"]); + FP = r15.wasm.cwrap("Any", null, ["number, number, number"]); } function Ore(r15) { let { backend: e, inputs: t10, attrs: o } = r15, { axis: n, keepDims: s } = o, { x: a } = t10, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e); @@ -23638,7 +23635,7 @@ function Ore(r15) { } return b; } -var PP = { kernelName: Ho, backendName: "wasm", setupFunc: Pre, kernelFunc: Ore }; +var PP = { kernelName: "Any", backendName: "wasm", setupFunc: Pre, kernelFunc: Ore }; function Mg(r15) { let e; function t10(n) { @@ -23888,7 +23885,7 @@ function moe(r15) { return bO(t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(c.dataId).id, u.batchSize, u.inDepth, u.inHeight, u.inWidth, u.inChannels, u.outDepth, u.outHeight, u.outWidth, u.outChannels, u.strideDepth, u.strideHeight, u.strideWidth, u.dilationDepth, u.dilationHeight, u.dilationWidth, u.filterDepth, u.filterHeight, u.filterWidth, u.padInfo.front, u.padInfo.top, u.padInfo.left), c; } var CO = { kernelName: nn, backendName: "wasm", setupFunc: loe, kernelFunc: moe }; -var wO = he(sn); +var wO = he("Cos"); var SO = he(an); var Ov; (function(r15) { @@ -24021,7 +24018,7 @@ function Doe(r15) { return VO(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(s.dataId).id, t10.dataIdMap.get(a.dataId).id, t10.dataIdMap.get(l.dataId).id, we[n.dtype], c.batchSize, c.inChannels, c.inHeight, c.inWidth, c.outHeight, c.outWidth, c.strideHeight, c.strideWidth, c.dilationHeight, c.dilationWidth, c.filterHeight, c.filterWidth, c.padInfo.top, c.padInfo.left), l; } var WO = { kernelName: Mi, backendName: "wasm", setupFunc: Roe, kernelFunc: Doe }; -var UO = he(hn); +var UO = he("Elu"); var GO; function Aoe(r15) { GO = r15.wasm.cwrap(Xa, null, ["number", "number", "number"]); @@ -24033,8 +24030,8 @@ function Foe(r15) { var HO = { kernelName: Xa, backendName: "wasm", setupFunc: Aoe, kernelFunc: Foe }; var Poe = false; var KO = Ge(xn, Poe, "bool"); -var qO = he(gn); -var jO = he(yn, "float32"); +var qO = he("Erf"); +var jO = he("Exp", "float32"); function Lg(r15) { let { inputs: e, attrs: t10, backend: o } = r15, { input: n } = e, { dim: s } = t10, a = n.shape.length, i = n.shape.slice(), p = s; return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), zt({ inputs: { x: n }, backend: o, attrs: { shape: i } }); @@ -24171,7 +24168,7 @@ function rne(r15) { return wM(t10.dataIdMap.get(i.dataId).id, o, n, a), i; } var SM = { kernelName: An, backendName: "wasm", setupFunc: tne, kernelFunc: rne }; -var IM = he(Fn); +var IM = he("Log"); var vM = he(Pn); var one = false; var kM = Ge(On, one, "bool"); @@ -24182,7 +24179,7 @@ var sne = false; var _M = Ge(R0, sne, "bool"); var EM; function ane(r15) { - EM = r15.wasm.cwrap(Bn, null, ["number", "number", "number", "number", "number", "number", "number"]); + EM = r15.wasm.cwrap("LRN", null, ["number", "number", "number", "number", "number", "number", "number"]); } function ine(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o; @@ -24190,7 +24187,7 @@ function ine(r15) { let u = t10.makeOutput(n.shape, n.dtype); return EM(t10.dataIdMap.get(n.dataId).id, t10.dataIdMap.get(u.dataId).id, n.shape[3], s, a, i, p), u; } -var $M = { kernelName: Bn, backendName: "wasm", setupFunc: ane, kernelFunc: ine }; +var $M = { kernelName: "LRN", backendName: "wasm", setupFunc: ane, kernelFunc: ine }; var RM; function une(r15) { RM = r15.wasm.cwrap(Ya, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]); @@ -24204,7 +24201,7 @@ function pne(r15) { var DM = { kernelName: Ya, backendName: "wasm", setupFunc: une, kernelFunc: pne }; var AM; function cne(r15) { - AM = r15.wasm.cwrap(zn, null, ["number", "number", "number", "number"]); + AM = r15.wasm.cwrap("Max", null, ["number", "number", "number", "number"]); } function lne(r15) { let { backend: e, inputs: t10, attrs: o } = r15, { reductionIndices: n, keepDims: s } = o, { x: a } = t10, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e); @@ -24225,7 +24222,7 @@ function lne(r15) { } return b; } -var FM = { kernelName: zn, backendName: "wasm", setupFunc: cne, kernelFunc: lne }; +var FM = { kernelName: "Max", backendName: "wasm", setupFunc: cne, kernelFunc: lne }; var mne = false; var PM = Ge(Vn, mne); var OM; @@ -24308,7 +24305,7 @@ function vne(r15) { var qM = { kernelName: Un, backendName: "wasm", setupFunc: Ine, kernelFunc: vne }; var jM; function kne(r15) { - jM = r15.wasm.cwrap(Gn, null, ["number", "number", "number", "number"]); + jM = r15.wasm.cwrap("Min", null, ["number", "number", "number", "number"]); } function Nne(r15) { let { backend: e, inputs: t10, attrs: o } = r15, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e); @@ -24329,7 +24326,7 @@ function Nne(r15) { } return b; } -var XM = { kernelName: Gn, backendName: "wasm", setupFunc: kne, kernelFunc: Nne }; +var XM = { kernelName: "Min", backendName: "wasm", setupFunc: kne, kernelFunc: Nne }; var Tne = false; var YM = Ge(Hn, Tne); var Lv; @@ -24365,10 +24362,10 @@ function Dne(r15) { return tL(t10.dataIdMap.get(p.dataId).id, u, c, s, a, t10.dataIdMap.get(l.dataId).id), i || t10.disposeData(p.dataId), l; } var rL = { kernelName: jn, backendName: "wasm", setupFunc: Rne, kernelFunc: Dne }; -var oL = Ge(qn, true); +var oL = Ge("Mod", true); var Ane = true; var nL = Ge(Xn, Ane); -var sL = he(pa); +var sL = he("Neg"); function qc(r15, e) { let t10 = new Int32Array(r15.wasm.HEAPU8.buffer, e, 4), o = t10[0], n = t10[1], s = t10[2], a = t10[3]; return r15.wasm._free(e), { pSelectedIndices: o, selectedSize: n, pSelectedScores: s, pValidOutputs: a }; @@ -24446,7 +24443,7 @@ function Kne(r15) { } var Bg = { kernelName: es, backendName: "wasm", kernelFunc: Kne, setupFunc: Hne }; var qne = false; -var yL = Ge(ts, qne); +var yL = Ge("Pow", qne); var bL; function jne(r15) { bL = r15.wasm.cwrap(rs, null, ["number", "number", "number"]); @@ -24604,7 +24601,7 @@ function bse(r15) { } var YL = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: yse, kernelFunc: bse }; var QL = he(ys); -var ZL = he(gs); +var ZL = he("Sin"); var JL = he(xs); var eB = he(Cs); function Cse(r15) { @@ -24799,10 +24796,10 @@ function Mse(r15) { } var wB = { kernelName: Xi, backendName: "wasm", kernelFunc: Mse }; var Lse = true; -var SB = Ge(Ts, Lse); +var SB = Ge("Sub", Lse); var IB; function Bse(r15) { - IB = r15.wasm.cwrap(Ss, null, ["number", "number", "number", "number"]); + IB = r15.wasm.cwrap("Sum", null, ["number", "number", "number", "number"]); } function zse(r15) { let { backend: e, inputs: t10, attrs: o } = r15, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = Tr(a, n, e), f = l; @@ -24822,8 +24819,8 @@ function zse(r15) { } return b; } -var vB = { kernelName: Ss, backendName: "wasm", setupFunc: Bse, kernelFunc: zse }; -var kB = he(_s); +var vB = { kernelName: "Sum", backendName: "wasm", setupFunc: Bse, kernelFunc: zse }; +var kB = he("Tan"); var NB = he(Es); var TB; function Vse(r15) { @@ -27242,13 +27239,13 @@ function et({ opType: r15, cpuKernelImpl: e, supportsComplex: t10 = false, dtype } var { addImpl: mz, castImpl: dz, ceilImpl: fz, concatImpl: hz, equalImpl: gz, expImpl: xz, expm1Impl: yz, floorImpl: bz, floorDivImpl: Cz, gatherNdImpl: wz, gatherV2Impl: Sz, greaterEqualImpl: Iz, greaterImpl: vz, lessEqualImpl: kz, lessImpl: Nz, logImpl: Tz, maxImpl: _z, maximumImpl: Ez, minimumImpl: $z, multiplyImpl: Rz, negImpl: Dz, notEqualImpl: Az, prodImpl: Fz, rangeImpl: Pz, rsqrtImpl: Oz, scatterImpl: Mz, simpleAbsImpl: Lz, sliceImpl: Bz, stridedSliceImpl: zz, stringNGramsImpl: Vz, subImpl: Wz, tileImpl: Uz, topKImpl: Gz, transposeImpl: Hz, uniqueImpl: rOt } = Ic; var Qie = ye({ opType: Z.ABS, cpuKernelImpl: Lz }); -var Kz = { kernelName: Xs, backendName: "webgpu", kernelFunc: Qie }; +var Kz = { kernelName: "Abs", backendName: "webgpu", kernelFunc: Qie }; var Zie = ye({ opType: Z.ACOS }); var qz = { kernelName: Vo, backendName: "webgpu", kernelFunc: Zie }; var Jie = ye({ opType: Z.ACOSH }); var jz = { kernelName: Wo, backendName: "webgpu", kernelFunc: Jie }; var eue = et({ opType: fe.ADD, cpuKernelImpl: mz, supportsComplex: true }); -var Xz = { kernelName: uo, backendName: "webgpu", kernelFunc: eue }; +var Xz = { kernelName: "Add", backendName: "webgpu", kernelFunc: eue }; var tx = class { constructor(e) { this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e[0], this.variableNames = e.map((t10, o) => `T${o}`), this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "addN"; @@ -27450,12 +27447,12 @@ function oue(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { keepDims: s, axis: a } = o; return eo(n, a, s, "all", t10); } -var Zz = { kernelName: Go, backendName: "webgpu", kernelFunc: oue }; +var Zz = { kernelName: "All", backendName: "webgpu", kernelFunc: oue }; function nue(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { keepDims: s, axis: a } = o; return eo(n, a, s, "any", t10); } -var Jz = { kernelName: Ho, backendName: "webgpu", kernelFunc: nue }; +var Jz = { kernelName: "Any", backendName: "webgpu", kernelFunc: nue }; var Yc = class { constructor(e, t10, o) { this.workgroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "infinityValue : f32,", this.size = true; @@ -27705,7 +27702,7 @@ function t0(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { reductionIndices: s, keepDims: a } = o; return eo(n, s, a, "max", t10); } -var iV = { kernelName: zn, backendName: "webgpu", kernelFunc: t0 }; +var iV = { kernelName: "Max", backendName: "webgpu", kernelFunc: t0 }; function r0(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { keepDims: s, axis: a } = o; return eo(n, a, s, "mean", t10); @@ -28944,7 +28941,7 @@ function Oue(r15) { } var AV = { kernelName: nn, backendName: "webgpu", kernelFunc: Oue }; var Mue = ye({ opType: Z.COS }); -var FV = { kernelName: sn, backendName: "webgpu", kernelFunc: Mue }; +var FV = { kernelName: "Cos", backendName: "webgpu", kernelFunc: Mue }; var Lue = ye({ opType: Z.COSH }); var PV = { kernelName: an, backendName: "webgpu", kernelFunc: Lue }; var Nx = class { @@ -29707,7 +29704,7 @@ function u0(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { axis: s, keepDims: a } = o; return eo(n, s, a, "sum", t10); } -var ZV = { kernelName: Ss, backendName: "webgpu", kernelFunc: u0 }; +var ZV = { kernelName: "Sum", backendName: "webgpu", kernelFunc: u0 }; function Zue(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = w.decodeEinsumEquation(n, s.length); w.checkEinsumDimSizes(a.length, p, s); @@ -29727,7 +29724,7 @@ function Zue(r15) { } var JV = { kernelName: Bi, backendName: "webgpu", kernelFunc: Zue }; var Jue = ye({ opType: Z.ELU }); -var eW = { kernelName: hn, backendName: "webgpu", kernelFunc: Jue }; +var eW = { kernelName: "Elu", backendName: "webgpu", kernelFunc: Jue }; var epe = (r15) => { let { inputs: e, backend: t10 } = r15, { dy: o, y: n } = e, s = new Ii(fe.ELU_DER, o.shape, n.shape); return t10.runWebGPUProgram(s, [o, n], o.dtype); @@ -29736,9 +29733,9 @@ var tW = { kernelName: Xa, backendName: "webgpu", kernelFunc: epe }; var tpe = et({ opType: fe.EQUAL, dtype: "bool", cpuKernelImpl: gz }); var rW = { kernelName: xn, backendName: "webgpu", kernelFunc: tpe }; var rpe = ye({ opType: Z.ERF }); -var oW = { kernelName: gn, backendName: "webgpu", kernelFunc: rpe }; +var oW = { kernelName: "Erf", backendName: "webgpu", kernelFunc: rpe }; var ope = ye({ opType: Z.EXP, cpuKernelImpl: xz, dtype: "float32" }); -var nW = { kernelName: yn, backendName: "webgpu", kernelFunc: ope }; +var nW = { kernelName: "Exp", backendName: "webgpu", kernelFunc: ope }; function Mx(r15) { let { inputs: e, attrs: t10, backend: o } = r15, { dim: n } = t10, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n; return n < 0 && (y.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), pe({ inputs: { x: s }, backend: o, attrs: { shape: i } }); @@ -29803,7 +29800,7 @@ function spe(r15) { let { inputs: e, backend: t10 } = r15, { input: o } = e; return Lx(o, false, t10); } -var iW = { kernelName: zi, backendName: "webgpu", kernelFunc: spe }; +var iW = { kernelName: "FFT", backendName: "webgpu", kernelFunc: spe }; var Bx = class { constructor(e) { this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "flipLeftRight"; @@ -30042,7 +30039,7 @@ function Spe(r15) { } var NW = { kernelName: An, backendName: "webgpu", kernelFunc: Spe }; var Ipe = ye({ opType: Z.LOG, cpuKernelImpl: Tz }); -var TW = { kernelName: Fn, backendName: "webgpu", kernelFunc: Ipe }; +var TW = { kernelName: "Log", backendName: "webgpu", kernelFunc: Ipe }; var vpe = ye({ opType: Z.LOG1P }); var _W = { kernelName: Pn, backendName: "webgpu", kernelFunc: vpe }; var kpe = et({ opType: fe.LOGICAL_AND, dtype: "bool" }); @@ -30139,7 +30136,7 @@ function _pe(r15) { let c = [{ type: "int32", data: [s] }, { type: "float32", data: [a] }, { type: "float32", data: [i] }, { type: "float32", data: [p] }]; return t10.runWebGPUProgram(u, [n], n.dtype, c); } -var AW = { kernelName: Bn, backendName: "webgpu", kernelFunc: _pe }; +var AW = { kernelName: "LRN", backendName: "webgpu", kernelFunc: _pe }; var qx = class { constructor(e) { this.outputShape = [], this.variableNames = ["inputImage", "outputImage", "dy"], this.uniforms = "depthRadius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "lrn_grad"; @@ -30365,7 +30362,7 @@ function Ope(r15) { let { inputs: e, backend: t10, attrs: o } = r15, { x: n } = e, { axis: s, keepDims: a } = o; return eo(n, s, a, "min", t10); } -var VW = { kernelName: Gn, backendName: "webgpu", kernelFunc: Ope }; +var VW = { kernelName: "Min", backendName: "webgpu", kernelFunc: Ope }; var Mpe = et({ opType: fe.MIN, cpuKernelImpl: $z }); var WW = { kernelName: Hn, backendName: "webgpu", kernelFunc: Mpe }; var Yx = class { @@ -30401,7 +30398,7 @@ var UW = { kernelName: Kn, backendName: "webgpu", kernelFunc: ({ inputs: r15, at return a.runWebGPUProgram(p, [o], o.dtype, i); } }; var Lpe = et({ opType: fe.MOD }); -var GW = { kernelName: qn, backendName: "webgpu", kernelFunc: Lpe }; +var GW = { kernelName: "Mod", backendName: "webgpu", kernelFunc: Lpe }; var Qx = class { constructor(e, t10) { this.variableNames = ["probs"], this.outputShape = [], this.uniforms = "seed : f32, numOutcomes: i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, t10], this.dispatchLayout = X(this.outputShape), this.dispatch = H(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "multinomial"; @@ -30529,7 +30526,7 @@ function zpe(r15) { let n = new Jr(o.shape, Z.NEG); return t10.runWebGPUProgram(n, [o], o.dtype); } -var qW = { kernelName: pa, backendName: "webgpu", kernelFunc: zpe }; +var qW = { kernelName: "Neg", backendName: "webgpu", kernelFunc: zpe }; function Vpe(r15) { console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); let { inputs: e, backend: t10, attrs: o } = r15, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = t10.readSync(n.dataId), c = t10.readSync(s.dataId), { selectedIndices: l } = Vt.nonMaxSuppressionV3Impl(u, c, a, i, p); @@ -30640,7 +30637,7 @@ var Hpe = (r15) => { }; var tU = { kernelName: es, backendName: "webgpu", kernelFunc: Hpe }; var Kpe = et({ opType: fe.POW }); -var rU = { kernelName: ts, backendName: "webgpu", kernelFunc: Kpe }; +var rU = { kernelName: "Pow", backendName: "webgpu", kernelFunc: Kpe }; function qpe(r15) { let { inputs: e, backend: t10 } = r15, { x: o, alpha: n } = e, s = new Ii(fe.PRELU, o.shape, n.shape); return t10.runWebGPUProgram(s, [o, n], "float32"); @@ -31148,7 +31145,7 @@ var SU = { kernelName: bs, backendName: "webgpu", kernelFunc: lce }; var mce = ye({ opType: Z.SIGN }); var IU = { kernelName: ys, backendName: "webgpu", kernelFunc: mce }; var dce = ye({ opType: Z.SIN }); -var vU = { kernelName: gs, backendName: "webgpu", kernelFunc: dce }; +var vU = { kernelName: "Sin", backendName: "webgpu", kernelFunc: dce }; var fce = ye({ opType: Z.SINH }); var kU = { kernelName: xs, backendName: "webgpu", kernelFunc: fce }; var hce = ye({ opType: Z.SOFTPLUS }); @@ -31402,9 +31399,9 @@ function Nce(r15) { } var LU = { kernelName: Ca, backendName: "webgpu", kernelFunc: Nce }; var Tce = et({ opType: fe.SUB, cpuKernelImpl: Wz, supportsComplex: true }); -var BU = { kernelName: Ts, backendName: "webgpu", kernelFunc: Tce }; +var BU = { kernelName: "Sub", backendName: "webgpu", kernelFunc: Tce }; var _ce = ye({ opType: Z.TAN }); -var zU = { kernelName: _s, backendName: "webgpu", kernelFunc: _ce }; +var zU = { kernelName: "Tan", backendName: "webgpu", kernelFunc: _ce }; var Ece = ye({ opType: Z.TANH }); var VU = { kernelName: Es, backendName: "webgpu", kernelFunc: Ece }; function $ce(r15) { @@ -33561,7 +33558,7 @@ async function loadModel(modelPath) { } // package.json -var version = "3.3.5"; +var version = "3.3.6"; // src/tfjs/humangl.ts var config2 = { diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map index c5f93946..b2202bac 100644 --- a/dist/human.esm.js.map +++ b/dist/human.esm.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["tfjs.esm.js", "../src/util/util.ts", "../src/config.ts", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/util/webcam.ts", "../models/models.json", "../src/tfjs/load.ts", "../package.json", "../src/tfjs/humangl.ts", "../src/tfjs/constants.ts", "../src/tfjs/backend.ts", "../src/draw/draw.ts", "../src/draw/primitives.ts", "../src/draw/options.ts", "../src/face/facemeshcoords.ts", "../src/face/constants.ts", "../src/draw/face.ts", "../src/draw/body.ts", "../src/draw/hand.ts", "../src/draw/object.ts", "../src/draw/gesture.ts", "../src/draw/labels.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/face/iris.ts", "../src/face/attention.ts", "../src/face/facemesh.ts", "../src/gear/emotion.ts", "../src/face/faceres.ts", "../src/face/mask.ts", "../src/face/antispoof.ts", "../src/face/liveness.ts", "../src/gear/gear.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/mobilefacenet.ts", "../src/face/insightface.ts", "../src/face/angles.ts", "../src/face/anthropometry.ts", "../src/face/face.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/gesture/gesture.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/result.ts", "../src/body/movenetcoords.ts", "../src/util/interpolate.ts", "../src/segmentation/meet.ts", "../src/face/match.ts", "../src/models.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/rvm.ts", "../src/segmentation/selfie.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"], - "sourcesContent": ["/*\n Human\n homepage: \n author: '\n*/\n\nvar _G=Object.create;var QC=Object.defineProperty;var EG=Object.getOwnPropertyDescriptor;var $G=Object.getOwnPropertyNames;var RG=Object.getPrototypeOf,DG=Object.prototype.hasOwnProperty;var Kt=(r,e)=>()=>(e||r((e={exports:{}}).exports,e),e.exports),qe=(r,e)=>{for(var t in e)QC(r,t,{get:e[t],enumerable:!0})},AG=(r,e,t,o)=>{if(e&&typeof e==\"object\"||typeof e==\"function\")for(let n of $G(e))!DG.call(r,n)&&n!==t&&QC(r,n,{get:()=>e[n],enumerable:!(o=EG(e,n))||o.enumerable});return r};var 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yw;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(yw||(yw={}));var bw;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(bw||(bw={}));var Cw;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(Cw||(Cw={}));var f4={float32:bw,int32:xw,bool:yw,complex64:Cw};function dt(r,e){if(r===\"string\"||e===\"string\"){if(r===\"string\"&&e===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${e}`)}return f4[r][e]}function oi(r){return dt(r,\"int32\")}function rd(r){return r!=null&&typeof r==\"object\"&&\"texture\"in r&&r.texture instanceof WebGLTexture}function od(r){return typeof GPUBuffer!=\"undefined\"&&r!=null&&typeof r==\"object\"&&\"buffer\"in r&&r.buffer instanceof GPUBuffer}function Oe(r,e){if(r.dtype===e.dtype)return[r,e];let t=dt(r.dtype,e.dtype);return[r.cast(t),e.cast(t)]}function ww(r,e){E(r.dtype===e.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`)}function h4(r,e){return e.some(t=>t.id===r.id)}function Cl(r){let e=[];return tk(r,e,new Set),e}function tk(r,e,t){if(r==null)return;if(r instanceof mt){e.push(r);return}if(!g4(r))return;let o=r;for(let n in o){let s=o[n];t.has(s)||(t.add(s),tk(s,e,t))}}function g4(r){return Array.isArray(r)||typeof r==\"object\"}function Sw(r){return r.kernelName!=null}var nd=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},wl=class r{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new nd}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Ym(e).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let o=t.factory();if(o&&!(o instanceof ao)&&typeof o.then==\"function\"){let n=++this.pendingBackendInitId,s=o.then(a=>n(nthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(o),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),n))}scopedRun(e,t,o){e();try{let n=o();return t(),n}catch(n){throw t(),n}}nextTensorId(){return r.nextTensorId++}nextVariableId(){return r.nextVariableId++}clone(e){let t=T.runKernel(Co,{x:e}),o={x:e},n=a=>({x:()=>{let i=\"float32\",p={x:a},u={dtype:i};return T.runKernel(yo,p,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[t],n,s,{}),t}runKernel(e,t,o){if(this.backendName==null&&this.backend,!(Xp(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(p=>{s+=p.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=Sw(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(Sw(e)){let{kernelName:f,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=Xp(f,this.backendName);E(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let S=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,S);o=this.saveTensorsForBackwardMode(k)}return S}}else{let{forwardFunc:f}=e,h=g=>{n&&(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=e,m=Sw(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(u,c,t,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:t.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=iw(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?(E(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let p=o.filter((u,c)=>a[c]);return i.concat(p)}return[]}makeTensor(e,t,o,n){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");o=o||\"float32\",n=n||this.backend;let s=e;o===\"string\"&&zo(e[0])&&(s=e.map(p=>Ji(p)));let a=n.write(s,t,o),i=new mt(t,o,a,this.nextTensorId());if(this.trackTensor(i,n),o===\"string\"){let p=this.state.tensorInfo.get(a),u=ow(s);this.state.numBytes+=u-p.bytes,p.bytes=u}return i}makeTensorFromDataId(e,t,o,n){o=o||\"float32\";let s={dataId:e,shape:t,dtype:o};return this.makeTensorFromTensorInfo(s,n)}makeTensorFromTensorInfo(e,t){let{dataId:o,shape:n,dtype:s}=e,a=new mt(n,s,o,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let s=new ri(e,t,o,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let o=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(o=e.size*Wp(e.dtype)),this.state.numBytes+=o,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:o})),e instanceof ri||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let o=e.size*Wp(e.dtype);this.state.numBytes-=o}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,o=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,o,n,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:o,saved:s},p=iw(e);p!=null&&(n=p.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,l)=>{if(c==null){let m=o[l],d=Gp(m.size,m.dtype);return this.makeTensor(d,m.shape,m.dtype)}return c}),n(u.length>1?u:u[0],s,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Cl(e),o=new Set(t.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(e,t,o,n=!1){if(E(t.length>0,()=>\"gradients() received an empty list of xs.\"),o!=null&&o.dtype!==\"float32\")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",e));E(s instanceof mt,()=>\"The result y returned by f() must be a tensor.\");let a=q0(this.state.activeTape,t,s);if(!n&&a.length===0&&t.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");return this.tidy(\"backward\",()=>{let i={};i[s.id]=o==null?x4(s.shape):o,j0(i,a,u=>this.tidy(u),y4);let p=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let c of u.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:p}})}customGrad(e){return E(qs(e),()=>\"The f passed in customGrad(f) must be a function.\"),(...t)=>{E(t.every(i=>i instanceof mt),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let o,n={};t.forEach((i,p)=>{n[p]=i});let s=(i,p)=>(o=e(...t,p),E(o.value instanceof mt,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),E(qs(o.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),o.value),a=(i,p)=>{let u=o.gradFunc(i,p),c=Array.isArray(u)?u:[u];E(c.length===t.length,()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),E(c.every(m=>m instanceof mt),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");let l={};return c.forEach((m,d)=>{l[d]=()=>m}),l};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,inputs:n})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=Mu(),o=await this.backend.time(e);return o.wallMs=Mu()-t,o}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new nd;for(let e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};wl.nextTensorId=0;wl.nextVariableId=0;function x4(r){let e=ml(ze(r),\"float32\");return T.makeTensor(e,r,\"float32\")}function Iw(){let r=aw();if(r._tfengine==null){let e=new dl(r);r._tfengine=new wl(e)}return $0(r._tfengine.ENV),Z0(()=>r._tfengine),r._tfengine}var T=Iw();function y4(r,e){let t={a:r,b:e};return T.runKernel(uo,t)}var eu={};qe(eu,{isBrowser:()=>kw,isMobile:()=>w4,mockIsMobile:()=>C4});function b4(){return typeof navigator!=\"undefined\"&&navigator!=null}var vw;function C4(r){vw=r}function w4(r){if(vw!==void 0)return vw;if(r||b4()){if(r||(r=navigator),r.product===\"ReactNative\")return!0;let e=r.userAgent||r.vendor||(typeof window!=\"undefined\"?window.opera:\"\");if(!e){let t=r;return 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a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),E(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),E(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),nu(a,i,p,c,u,s)}var Xk=N({batchNorm3d_:kH});function NH(r,e,t,o,n,s){let a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),E(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),E(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),nu(a,i,p,c,u,s)}var Yk=N({batchNorm4d_:NH});function TH(r,e,t){let o=v(r,\"x\",\"bincount\"),n=v(e,\"weights\",\"bincount\");E(o.dtype===\"int32\",()=>`Error in bincount: input dtype must be int32, but got ${o.dtype}`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(n.size===o.size||n.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`);let s={x:o,weights:n},a={size:t};return T.runKernel(Jo,s,a)}var hd=N({bincount_:TH});function _H(r,e){let t=v(r,\"x\",\"bitwiseAnd\"),o=v(e,\"y\",\"bitwiseAnd\");if(!br(t.shape,o.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${t.shape}, y: ${o.shape}`);if(t.dtype!==\"int32\"||o.dtype!==\"int32\")throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${t.dtype} and type of y: ${o.dtype}`);let n={a:t,b:o};return T.runKernel(qa,n)}var Qk=N({bitwiseAnd_:_H});function EH(r,e){let t=v(r,\"s0\",\"broadcastArgs\",\"int32\"),o=v(e,\"s1\",\"broadcastArgs\",\"int32\");if(t.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${t.rank}`);if(o.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${o.rank}`);let n={s0:t,s1:o};return T.runKernel(ea,n)}var Zk=N({broadcastArgs_:EH});function $H(r,e){let t=v(r,\"broadcastTo\",\"x\"),o=t.shape;if(Ct(e),e.lengtht.rank){let u=t.shape.slice();for(;u.length=0;u--)if(n[u]===e[u])s[u]=1;else if(t.shape[u]!==1)throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`);if(s.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Ur(t);let i={x:t},p={reps:s};return T.runKernel(po,i,p)}var su=N({broadcastTo_:$H});function RH(r){let t={x:v(r,\"x\",\"ceil\",\"float32\")};return T.runKernel(en,t)}var Jk=N({ceil_:RH});function $a(r,e,t){Ct(r),t=t||Ei(e);let o={shape:r,value:e,dtype:t};return T.runKernel(sa,{},o)}function DH(r,e,t){let o=v(r,\"x\",\"clipByValue\");if(E(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`),e===t)return $a(o.shape,e,o.dtype);let n={x:o},s={clipValueMin:e,clipValueMax:t};return T.runKernel(bo,n,s)}var e2=N({clipByValue_:DH});function AH(r){return yt(r,0)}var t2=N({concat1d_:AH});function FH(r,e){return yt(r,e)}var r2=N({concat2d_:FH});function PH(r,e){return yt(r,e)}var o2=N({concat3d_:PH});function OH(r,e){return yt(r,e)}var n2=N({concat4d_:OH});function MH(r,e,t,o,n=\"NHWC\",s=[1,1],a){let i=v(r,\"x\",\"conv2d\",\"float32\"),p=v(e,\"filter\",\"conv2d\",\"float32\"),u=i,c=!1;i.rank===3&&(c=!0,u=W(i,[1,i.shape[0],i.shape[1],i.shape[2]])),E(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),E(p.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`),Lt(\"conv2d\",o,a);let l=n===\"NHWC\"?u.shape[3]:u.shape[1];E(l===p.shape[2],()=>`Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`),E(gr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),E(Ta(s),()=>\"Error in conv2D: Dilated rates should be larger than 0.\"),E(Ta(t),()=>\"Error in conv2D: Strides should be larger than 0.\");let m={x:u,filter:p},d={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=T.runKernel(tn,m,d);return c?W(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var au=N({conv2d_:MH});function LH(r,e,t,o,n=\"NWC\",s=1,a){let i=v(r,\"x\",\"conv1d\"),p=v(e,\"filter\",\"conv1d\"),u=i,c=!1;i.rank===2&&(c=!0,u=W(i,[1,i.shape[0],i.shape[1]])),E(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),E(p.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${p.rank}.`),Lt(\"conv1d\",o,a),E(u.shape[2]===p.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${p.shape[1]}.`),E(gr(t,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${t} and dilation '${s}'`),E(Ta(s),()=>\"Error in conv1D: Dilated rates should be larger than 0.\"),E(Ta(t),()=>\"Error in conv1D: Stride should be larger than 0.\"),E(n===\"NWC\",()=>`Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`);let l=W(p,[1,p.shape[0],p.shape[1],p.shape[2]]),m=W(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=au(m,l,[1,t],o,\"NHWC\",[1,s],a);return c?W(g,[g.shape[2],g.shape[3]]):W(g,[g.shape[0],g.shape[2],g.shape[3]])}var s2=N({conv1d_:LH});function BH(r,e,t,o,n,s=\"NHWC\",a){E(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let i=r,p=e,u=!1;e.rank===3&&(u=!0,p=W(e,[1,e.shape[0],e.shape[1],e.shape[2]]),i=[1,r[0],r[1],r[2]]),E(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),E(p.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${p.rank}`),E(t.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${t.rank}`);let c=s===\"NHWC\"?i[3]:i[1],l=s===\"NHWC\"?p.shape[3]:p.shape[1];E(c===t.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t.shape[2]}.`),E(l===t.shape[3],()=>`Error in conv2dDerInput: depth of output (${l}) must match output depth for filter ${t.shape[3]}.`),Lt(\"conv2dDerInput\",n,a);let m={dy:p,filter:t},d={strides:o,pad:n,dataFormat:s,dimRoundingMode:a,inputShape:i},f=T.runKernel(rn,m,d);return u?W(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var gd=N({conv2DBackpropInput_:BH});function zH(r,e,t,o,n,s){let a=v(r,\"x\",\"conv2dTranspose\"),i=v(e,\"filter\",\"conv2dTranspose\");return gd(t,a,i,o,n,\"NHWC\",s)}var a2=N({conv2dTranspose_:zH});function VH(r,e,t,o,n=\"NDHWC\",s=[1,1,1]){let a=v(r,\"x\",\"conv3d\"),i=v(e,\"filter\",\"conv3d\"),p=a,u=!1;a.rank===4&&(u=!0,p=W(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),E(p.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${p.rank}.`),E(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),E(p.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${p.shape[4]}) must match input depth for filter ${i.shape[3]}.`),E(gr(t,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),E(n===\"NDHWC\",()=>`Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`),E(Ta(s),()=>\"Error in conv3D: Dilated rates should be larger than 0.\"),E(Ta(t),()=>\"Error in conv3D: Strides should be larger than 0.\");let c={x:p,filter:i},l={strides:t,pad:o,dataFormat:n,dilations:s},m=T.runKernel(on,c,l);return u?W(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var i2=N({conv3d_:VH});function WH(r,e,t,o,n){E(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let s=r,a=e,i=!1;e.rank===4&&(i=!0,a=W(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]),s=[1,r[0],r[1],r[2],r[3]]);let p=s[4],u=a.shape[4];E(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),E(a.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`),E(t.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${t.rank}`),E(p===t.shape[3],()=>`Error in conv3dDerInput: depth of input (${p}) must match input depth for filter ${t.shape[3]}.`),E(u===t.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t.shape[4]}.`);let c={dy:a,filter:t},l={pad:n,strides:o,inputShape:s},m=T.runKernel(nn,c,l);return i?W(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var u2=N({conv3DBackpropInput_:WH});function UH(r,e,t,o,n){let s=v(r,\"x\",\"conv3dTranspose\"),a=v(e,\"filter\",\"conv3dTranspose\");return u2(t,s,a,o,n)}var p2=N({conv3dTranspose_:UH});function GH(r){let t={x:v(r,\"x\",\"cos\",\"float32\")};return T.runKernel(sn,t)}var c2=N({cos_:GH});function HH(r){let t={x:v(r,\"x\",\"cosh\",\"float32\")};return T.runKernel(an,t)}var l2=N({cosh_:HH});function KH(r,e=0,t=!1,o=!1){let s={x:v(r,\"x\",\"cumprod\")},a={axis:e,exclusive:t,reverse:o};return T.runKernel(un,s,a)}var m2=N({cumprod_:KH});function qH(r,e=0,t=!1,o=!1){let s={x:v(r,\"x\",\"cumsum\")},a={axis:e,exclusive:t,reverse:o};return T.runKernel(pn,s,a)}var d2=N({cumsum_:qH});function jH(r,e,t,o=!1){let n=v(r,\"x\",\"denseBincount\"),s=v(e,\"weights\",\"denseBincount\");E(n.dtype===\"int32\",()=>`Error in denseBincount: input dtype must be int32, but got ${n.dtype}`),E(n.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(s.size===n.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`);let a={x:n,weights:s},i={size:t,binaryOutput:o};return T.runKernel(ra,a,i)}var f2=N({denseBincount_:jH});function XH(r,e,t=\"NHWC\"){let o=v(r,\"x\",\"depthToSpace\",\"float32\"),n=t===\"NHWC\"?o.shape[1]:o.shape[2],s=t===\"NHWC\"?o.shape[2]:o.shape[3],a=t===\"NHWC\"?o.shape[3]:o.shape[1];E(e>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${e}`),E(n*e>=0,()=>`Negative dimension size caused by overflow when multiplying\n ${n} and ${e} for depthToSpace with input shape\n ${o.shape}`),E(s*e>=0,()=>`Negative dimension size caused by overflow when multiplying\n ${s} and ${e} for depthToSpace with input shape\n ${o.shape}`),E(a%(e*e)===0,()=>`Dimension size must be evenly divisible by ${e*e} but is ${a} for depthToSpace with input shape ${o.shape}`);let i={x:o},p={blockSize:e,dataFormat:t};return T.runKernel(ln,i,p)}var h2=N({depthToSpace_:XH});function YH(r,e,t,o,n=\"NHWC\",s=[1,1],a){let i=v(r,\"x\",\"depthwiseConv2d\",\"float32\"),p=v(e,\"filter\",\"depthwiseConv2d\",\"float32\"),u=i,c=!1;i.rank===3&&(c=!0,u=W(i,[1,i.shape[0],i.shape[1],i.shape[2]])),E(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),E(p.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`);let l=n===\"NHWC\"?u.shape[3]:u.shape[1];E(l===p.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${l}) must match the inChannels dimension in filter ${p.shape[2]}.`),Lt(\"depthwiseConv2d\",o,a);let m={x:u,filter:p},d={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=T.runKernel(mn,m,d);return c?W(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var sc=N({depthwiseConv2d_:YH});function QH(r){let t={x:v(r,\"x\",\"diag\")};return T.runKernel(oa,t)}var g2=N({diag_:QH});function ZH(r,e,t,o,n=[1,1],s=\"NHWC\"){let a=v(r,\"x\",\"dilation2d\"),i=v(e,\"filter\",\"dilation2d\");E(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),E(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),E(s===\"NHWC\",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let p=a,u=!1;a.rank===3&&(p=W(a,[1,a.shape[0],a.shape[1],a.shape[2]]),u=!0),E(p.shape[3]===i.shape[2],()=>`Error in dilation2d: input and filter must have the same depth: ${p.shape[3]} vs 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Eo(r,e,t,o,n,s){o==null&&(o=.5),n==null&&(n=Number.NEGATIVE_INFINITY),s==null&&(s=0);let a=r.shape[0];return t=Math.min(t,a),E(0<=o&&o<=1,()=>`iouThreshold must be in [0, 1], but was '${o}'`),E(r.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${r.rank}'`),E(r.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`),E(e.rank===1,()=>\"scores must be a 1D tensor\"),E(e.shape[0]===a,()=>`scores has incompatible shape with boxes. 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g=0;gn&&u.push({score:e[g],boxIndex:g,suppressBeginIndex:0});u.sort(yN);let c=s>0?-.5/s:0,l=[],m=[];for(;l.length0;){let g=u.pop(),{score:x,boxIndex:b,suppressBeginIndex:C}=g;if(x=C;--k){let _=yj(r,b,l[k]);if(_>=o){S=!0;break}if(g.score=g.score*bj(o,c,_),g.score<=n)break}g.suppressBeginIndex=l.length,S||(g.score===x?(l.push(b),m.push(g.score)):g.score>n&&xN(u,g,yN))}let d=l.length,f=t-d;i&&f>0&&(l.push(...new Array(f).fill(0)),m.push(...new Array(f).fill(0)));let h={selectedIndices:l};return a&&(h.selectedScores=m),p&&(h.validOutputs=d),h}function yj(r,e,t){let o=r.subarray(e*4,e*4+4),n=r.subarray(t*4,t*4+4),s=Math.min(o[0],o[2]),a=Math.min(o[1],o[3]),i=Math.max(o[0],o[2]),p=Math.max(o[1],o[3]),u=Math.min(n[0],n[2]),c=Math.min(n[1],n[3]),l=Math.max(n[0],n[2]),m=Math.max(n[1],n[3]),d=(i-s)*(p-a),f=(l-u)*(m-c);if(d<=0||f<=0)return 0;let h=Math.max(s,u),g=Math.max(a,c),x=Math.min(i,l),b=Math.min(p,m),C=Math.max(x-h,0)*Math.max(b-g,0);return C/(d+f-C)}function bj(r,e,t){let o=Math.exp(e*t*t);return t<=r?o:0}function yN(r,e){return r.score-e.score||r.score===e.score&&e.boxIndex-r.boxIndex}async function Cj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY){let s=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),a=v(e,\"scores\",\"nonMaxSuppressionAsync\"),i=Eo(s,a,t,o,n);t=i.maxOutputSize,o=i.iouThreshold,n=i.scoreThreshold;let p=await Promise.all([s.data(),a.data()]),u=p[0],c=p[1],{selectedIndices:l}=Jd(u,c,t,o,n);return s!==r&&s.dispose(),a!==e&&a.dispose(),Jt(l,\"int32\")}var bN=Cj;function wj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=0){let a=v(r,\"boxes\",\"nonMaxSuppression\"),i=v(e,\"scores\",\"nonMaxSuppression\"),p=Eo(a,i,t,o,n,s);t=p.maxOutputSize,o=p.iouThreshold,n=p.scoreThreshold,s=p.softNmsSigma;let u={boxes:a,scores:i},c={maxOutputSize:t,iouThreshold:o,scoreThreshold:n,softNmsSigma:s},l=T.runKernel(Zn,u,c);return{selectedIndices:l[0],selectedScores:l[1]}}var CN=N({nonMaxSuppressionWithScore_:wj});async function Sj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=0){let a=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),i=v(e,\"scores\",\"nonMaxSuppressionAsync\"),p=Eo(a,i,t,o,n,s);t=p.maxOutputSize,o=p.iouThreshold,n=p.scoreThreshold,s=p.softNmsSigma;let u=await Promise.all([a.data(),i.data()]),c=u[0],l=u[1],{selectedIndices:m,selectedScores:d}=tf(c,l,t,o,n,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:Jt(m,\"int32\"),selectedScores:Jt(d)}}var wN=Sj;function Ij(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=v(r,\"boxes\",\"nonMaxSuppression\"),i=v(e,\"scores\",\"nonMaxSuppression\"),p=Eo(a,i,t,o,n,null),u=p.maxOutputSize,c=p.iouThreshold,l=p.scoreThreshold,m={boxes:a,scores:i},d={maxOutputSize:u,iouThreshold:c,scoreThreshold:l,padToMaxOutputSize:s},f=T.runKernel(Qa,m,d);return{selectedIndices:f[0],validOutputs:f[1]}}var SN=N({nonMaxSuppressionPadded_:Ij});async function vj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),i=v(e,\"scores\",\"nonMaxSuppressionAsync\"),p=Eo(a,i,t,o,n,null),u=p.maxOutputSize,c=p.iouThreshold,l=p.scoreThreshold,[m,d]=await Promise.all([a.data(),i.data()]),{selectedIndices:f,validOutputs:h}=ef(m,d,u,c,l,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:Jt(f,\"int32\"),validOutputs:ke(h,\"int32\")}}var IN=vj;function kj(r,e,t=!1,o=!1){let n=v(r,\"images\",\"resizeBilinear\");E(n.rank===3||n.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`),E(e.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${e}.`),E(o===!1||t===!1,()=>\"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.\");let s=n,a=!1;n.rank===3&&(a=!0,s=W(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},p={alignCorners:t,halfPixelCenters:o,size:e},u=T.runKernel(is,i,p);return a?W(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var vN=N({resizeBilinear_:kj});function Nj(r,e,t=!1,o=!1){let n=v(r,\"images\",\"resizeNearestNeighbor\");E(n.rank===3||n.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`),E(e.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`),E(n.dtype===\"float32\"||n.dtype===\"int32\",()=>\"`images` must have `int32` or `float32` as dtype\"),E(o===!1||t===!1,()=>\"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.\");let s=n,a=!1;n.rank===3&&(a=!0,s=W(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},p={alignCorners:t,halfPixelCenters:o,size:e},u=T.runKernel(as,i,p);return a?W(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var kN=N({resizeNearestNeighbor_:Nj});function Tj(r,e=\"binary\",t=!1,o=.5){let n=v(r,\"image\",\"threshold\"),s=.2989,a=.587,i=.114,p=n.shape[0]*n.shape[1],u=se(Jt([o]),255),c,l,m,d;if(E(n.rank===3,()=>`Error in threshold: image must be rank 3,but got rank 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p={image:a,transforms:i},u={interpolation:t,fillMode:o,fillValue:n,outputShape:s};return T.runKernel(Rs,p,u)}var TN=N({transform_:Ej});function $j(r,e,t){let o=v(r,\"a\",\"bandPart\");E(o.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${o.rank}.`);let n=o.shape,[s,a]=o.shape.slice(-2),i,p;typeof e==\"number\"?(E(e%1===0,()=>`bandPart(): numLower must be an integer, got ${e}.`),E(e<=s,()=>`bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`),i=v(e<0?s:e,\"numLower\",\"bandPart\")):(E(e.dtype===\"int32\",()=>\"bandPart(): numLower's dtype must be an int32.\"),i=lo(Tl(e,0),s,Hu(e,s))),typeof t==\"number\"?(E(t%1===0,()=>`bandPart(): numUpper must be an integer, got ${t}.`),E(t<=a,()=>`bandPart(): numUpper (${t}) must not be greater than the number of columns (${a}).`),p=v(t<0?a:t,\"numUpper\",\"bandPart\")):(E(t.dtype===\"int32\",()=>\"bandPart(): numUpper's dtype must be an int32.\"),p=lo(Tl(t,0),a,Hu(t,a)));let u=W(cu(0,s,1,\"int32\"),[-1,1]),c=cu(0,a,1,\"int32\"),l=Te(u,c),m=Uu(ac(l,i),Id(l,pr(p))),d=Gr([s,a],o.dtype);return W(vr(fo(W(o,[-1,s,a])).map(f=>lo(m,f,d))),n)}var _N=N({bandPart_:$j});function Rj(r){let e;if(Array.isArray(r)){e=!1,E(r!=null&&r.length>0,()=>\"Gram-Schmidt process: input must not be null, undefined, or empty\");let n=r[0].shape[0];for(let s=1;s`Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${n})`)}else e=!0,r=li(r,r.shape[0],0).map(n=>cc(n,[0]));E(r.length<=r[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);let t=[],o=r;for(let n=0;n{let s=o[n];if(n>0)for(let a=0;a=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`),r.rank===2)return $N(r,e);{let t=r.shape.slice(0,r.shape.length-2).reduce((p,u)=>p*u),o=fo(W(r,[t,r.shape[r.shape.length-2],r.shape[r.shape.length-1]]),0),n=[],s=[];o.forEach(p=>{let[u,c]=$N(p,e);n.push(u),s.push(c)});let a=W(vr(n,0),r.shape),i=W(vr(s,0),r.shape);return[a,i]}}function $N(r,e=!1){return T.tidy(()=>{E(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let t=r.shape[0],o=r.shape[1],n=Cd(t),s=Ur(r),a=mu([[1]],[1,1]),i=Ur(a),p=t>=o?o:t;for(let u=0;u{let d=Xe(s,[u,u],[t-u,1]),f=Vu(d),h=Xe(s,[u,u],[1,1]),g=lo(Wu(h,0),mu([[-1]]),mu([[1]])),x=Te(h,se(g,f)),b=je(d,x);b.shape[0]===1?i=Ur(a):i=yt([a,Xe(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let C=pr(je(Ze(g,x),f)),S=Xe(s,[u,0],[t-u,o]),k=se(C,i),_=mc(i);if(u===0)s=Te(S,Ze(k,Ze(_,S)));else{let D=Te(S,Ze(k,Ze(_,S)));s=yt([Xe(s,[0,0],[u,o]),D],0)}let $=mc(k),R=Xe(n,[0,u],[t,n.shape[1]-u]);if(u===0)n=Te(R,Ze(Ze(R,i),$));else{let D=Te(R,Ze(Ze(R,i),$));n=yt([Xe(n,[0,0],[t,u]),D],1)}return[i,s,n]}),Ot([c,l,m])}return!e&&t>o&&(n=Xe(n,[0,0],[t,o]),s=Xe(s,[0,0],[o,o])),[n,s]})}var RN=N({qr_:Dj});var $t;(function(r){r[r.NONE=0]=\"NONE\",r[r.MEAN=1]=\"MEAN\",r[r.SUM=2]=\"SUM\",r[r.SUM_BY_NONZERO_WEIGHTS=3]=\"SUM_BY_NONZERO_WEIGHTS\"})($t||($t={}));function Aj(r,e,t=$t.SUM_BY_NONZERO_WEIGHTS){let o=v(r,\"losses\",\"computeWeightedLoss\"),n=null;e!=null&&(n=v(e,\"weights\",\"computeWeightedLoss\"));let s=n==null?o:se(o,n);if(t===$t.NONE)return s;if(t===$t.SUM)return ot(s);if(t===$t.MEAN){if(n==null)return Gu(s);{let a=o.size/n.size,i=je(ot(s),ot(n));return a>1?je(i,ke(a)):i}}if(t===$t.SUM_BY_NONZERO_WEIGHTS){if(n==null)return je(ot(s),ke(o.size));{let a=se(n,Da(o.shape)),i=Ue(ot(Fd(a,ke(0))),\"float32\");return je(ot(s),i)}}throw Error(`Unknown reduction: ${t}`)}var cr=N({computeWeightedLoss_:Aj});function Fj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"absoluteDifference\"),s=v(e,\"predictions\",\"absoluteDifference\"),a=null;t!=null&&(a=v(t,\"weights\",\"absoluteDifference\")),xt(n.shape,s.shape,\"Error in absoluteDifference: \");let i=Qt(Te(n,s));return cr(i,a,o)}var DN=N({absoluteDifference_:Fj});function Pj(r,e,t,o,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"cosineDistance\"),a=v(e,\"predictions\",\"cosineDistance\"),i=null;o!=null&&(i=v(o,\"weights\",\"cosineDistance\")),xt(s.shape,a.shape,\"Error in cosineDistance: \");let p=ke(1),u=Te(p,ot(se(s,a),t,!0));return cr(u,i,n)}var AN=N({cosineDistance_:Pj});function Oj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"hingeLoss\"),s=v(e,\"predictions\",\"hingeLoss\"),a=null;t!=null&&(a=v(t,\"weights\",\"hingeLoss\")),xt(n.shape,s.shape,\"Error in hingeLoss: \");let i=ke(1);n=Te(se(ke(2),n),i);let p=lu(Te(i,se(n,s)));return cr(p,a,o)}var FN=N({hingeLoss_:Oj});function Mj(r,e,t,o=1,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"huberLoss\"),a=v(e,\"predictions\",\"huberLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"huberLoss\")),xt(s.shape,a.shape,\"Error in huberLoss: \");let p=ke(o),u=Qt(Te(a,s)),c=Hu(u,p),l=Te(u,c),m=Ce(se(ke(.5),Zt(c)),se(p,l));return cr(m,i,n)}var PN=N({huberLoss_:Mj});function Lj(r,e,t,o=1e-7,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"logLoss\"),a=v(e,\"predictions\",\"logLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"logLoss\")),xt(s.shape,a.shape,\"Error in logLoss: \");let p=ke(1),u=ke(o),c=pr(se(s,pi(Ce(a,u)))),l=se(Te(p,s),pi(Ce(Te(p,a),u))),m=Te(c,l);return cr(m,i,n)}var ON=N({logLoss_:Lj});function Bj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"meanSquaredError\"),s=v(e,\"predictions\",\"meanSquaredError\"),a=null;t!=null&&(a=v(t,\"weights\",\"meanSquaredError\")),xt(n.shape,s.shape,\"Error in meanSquaredError: \");let i=Kd(n,s);return cr(i,a,o)}var MN=N({meanSquaredError_:Bj});function zj(r,e){let t=v(r,\"labels\",\"sigmoidCrossEntropyWithLogits\"),o=v(e,\"logits\",\"sigmoidCrossEntropyWithLogits\");xt(t.shape,o.shape,\"Error in sigmoidCrossEntropyWithLogits: \");let n=lu(o),s=se(o,t),a=kd(_o(pr(Qt(o))));return Ce(Te(n,s),a)}function Vj(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let 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Labels / logits was rank ${e.rank} and dim was ${t}`);return Ir((n,s,a)=>{let p=_d(s,[t],!0),u=Te(Ue(s,\"float32\"),p);a([n,u]);let c=pr(se(u,n));return{value:ot(c,[t]),gradFunc:(d,f)=>{let[h,g]=f,x=ii(d.shape,[t]);return[se(W(d,x),Te(Ue(h,\"float32\"),_o(g))),se(W(d,x),Te(_o(g),Ue(h,\"float32\")))]}}})(r,e)}function Uj(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"onehotLabels\",\"softmaxCrossEntropy\"),a=v(e,\"logits\",\"softmaxCrossEntropy\"),i=null;if(t!=null&&(i=v(t,\"weights\",\"softmaxCrossEntropy\")),xt(s.shape,a.shape,\"Error in softmaxCrossEntropy: \"),o>0){let u=ke(o),c=ke(1),l=ke(s.shape[1]);s=Ce(se(s,Te(c,u)),je(u,l))}let p=Wj(s,a);return cr(p,i,n)}var BN=N({softmaxCrossEntropy_:Uj});function Gj(r,e,t,o){let n=v(r,\"indices\",\"sparseFillEmptyRows\",\"int32\"),s=v(e,\"values\",\"sparseFillEmptyRows\"),a=v(t,\"denseShape\",\"sparseFillEmptyRows\",\"int32\"),i=v(o,\"defaultValue\",\"sparseFillEmptyRows\",s.dtype);if(n.rank!==2)throw new Error(`Indices should be Tensor2D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(a.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let p={indices:n,values:s,denseShape:a,defaultValue:i},u=T.runKernel(Ki,p);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var zN=N({sparseFillEmptyRows_:Gj});function Hj(r,e,t){let o=v(r,\"inputIndices\",\"sparseReshape\",\"int32\"),n=v(e,\"inputShape\",\"sparseReshape\",\"int32\"),s=v(t,\"newShape\",\"sparseReshape\",\"int32\");if(o.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape\n ${o.shape}`);if(n.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let a={inputIndices:o,inputShape:n,newShape:s},i=T.runKernel(ei,a);return{outputIndices:i[0],outputShape:i[1]}}var VN=N({sparseReshape_:Hj});function Kj(r,e,t){let o=v(r,\"data\",\"sparseSegmentMean\"),n=v(e,\"indices\",\"sparseSegmentMean\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentMean\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(ya,a)}var WN=N({sparseSegmentMean_:Kj});function qj(r,e,t){let o=v(r,\"data\",\"sparseSegmentSum\"),n=v(e,\"indices\",\"sparseSegmentSum\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(ba,a)}var UN=N({sparseSegmentSum_:qj});function jj(r,e,t,o,n,s,a,i){let p=v(r,\"data\",\"stringNGrams\",\"string\");if(p.dtype!==\"string\")throw new Error(\"Data must be of datatype string\");if(p.shape.length!==1)throw new Error(`Data must be a vector, saw: ${p.shape}`);let u=v(e,\"dataSplits\",\"stringNGrams\");if(u.dtype!==\"int32\")throw new Error(\"Data splits must be of datatype int32\");let c={separator:t,nGramWidths:o,leftPad:n,rightPad:s,padWidth:a,preserveShortSequences:i},l={data:p,dataSplits:u},m=T.runKernel(Ca,l,c);return{nGrams:m[0],nGramsSplits:m[1]}}var GN=N({stringNGrams_:jj});function Xj(r,e,t=!0){let o=v(r,\"input\",\"stringSplit\",\"string\"),n=v(e,\"delimiter\",\"stringSplit\",\"string\");if(o.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${o.shape}`);if(n.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);let s={skipEmpty:t},a={input:o,delimiter:n},i=T.runKernel(ji,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var HN=N({stringSplit_:Xj});function Yj(r,e){let t=v(r,\"input\",\"stringToHashBucketFast\",\"string\"),o={numBuckets:e};if(e<=0)throw new Error(\"Number of buckets must be at least 1\");let n={input:t};return T.runKernel(Xi,n,o)}var KN=N({stringToHashBucketFast_:Yj});function Qj(r,e,t,o=!0){let n=v(r,\"input\",\"staticRegexReplace\",\"string\"),s={pattern:e,rewrite:t,replaceGlobal:o};return T.runKernel(Ru,{x:n},s)}var qN=N({staticRegexReplace_:Qj});var Zj={fft:uc,ifft:ju,rfft:pc,irfft:Hd},Jj={hammingWindow:pN,hannWindow:Qd,frame:Zd,stft:cN},eX={flipLeftRight:mN,grayscaleToRGB:dN,resizeNearestNeighbor:kN,resizeBilinear:vN,rgbToGrayscale:fN,rotateWithOffset:hN,cropAndResize:lN,nonMaxSuppression:gN,nonMaxSuppressionAsync:bN,nonMaxSuppressionWithScore:CN,nonMaxSuppressionWithScoreAsync:wN,nonMaxSuppressionPadded:SN,nonMaxSuppressionPaddedAsync:IN,threshold:NN,transform:TN},tX={bandPart:_N,gramSchmidt:EN,qr:RN},rX={absoluteDifference:DN,computeWeightedLoss:cr,cosineDistance:AN,hingeLoss:FN,huberLoss:PN,logLoss:ON,meanSquaredError:MN,sigmoidCrossEntropy:LN,softmaxCrossEntropy:BN},oX={sparseFillEmptyRows:zN,sparseReshape:VN,sparseSegmentMean:WN,sparseSegmentSum:UN},nX={stringNGrams:GN,stringSplit:HN,stringToHashBucketFast:KN,staticRegexReplace:qN};var jN={};qe(jN,{Serializable:()=>Rl,SerializationMap:()=>rf,getRegisteredName:()=>aX,registerClass:()=>rS});var sX=new Map,tS=new Map,Rl=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},rf=class r{constructor(){this.classNameMap={}}static getMap(){return r.instance==null&&(r.instance=new r),r.instance}static register(e){r.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function rS(r,e,t){E(r.className!=null,()=>\"Class being registered does not have the static className property defined.\"),E(typeof r.className==\"string\",()=>\"className is required to be a string, but got type \"+typeof r.className),E(r.className.length>0,()=>\"Class being registered has an empty-string as its className, which is disallowed.\"),typeof e==\"undefined\"&&(e=\"Custom\"),typeof t==\"undefined\"&&(t=r.className);let o=t,n=e+\">\"+o;return rf.register(r),sX.set(n,r),tS.set(r,n),r}function aX(r){return tS.has(r)?tS.get(r):r.className}var kr=class extends Rl{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Ot(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Vw(e,t)}dispose(){this.iterations_!=null&&Ot(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:\"iter\",tensor:ke(this.iterations_,\"int32\")}}async getWeights(){throw new Error(\"getWeights() is not implemented for this optimizer yet.\")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(kr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var Ju=class extends kr{static get className(){return\"Adadelta\"}constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:De(()=>Gt(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;De(()=>{let c=Ce(se(p,this.rho),se(Zt(i),1-this.rho)),l=se(je(Rr(Ce(u,this.epsilon)),Rr(Ce(p,this.epsilon))),i),m=Ce(se(u,this.rho),se(Zt(l),1-this.rho));p.assign(c),u.assign(m);let d=Ce(se(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(e=>e.variable)),Ot(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};var ep=class extends kr{static get className(){return\"Adagrad\"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:De(()=>$a(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;De(()=>{let p=Ce(i,Zt(a));i.assign(p);let u=Ce(se(je(a,Rr(Ce(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};var tp=class extends kr{static get className(){return\"Adam\"}constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=Te(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:De(()=>Gt(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Gt(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=Ce(se(l,this.beta2),se(Zt(u),1-this.beta2)),f=je(m,o),h=je(d,n);c.assign(m),l.assign(d);let g=Ce(se(je(f,Ce(Rr(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(se(this.accBeta1,this.beta1)),this.accBeta2.assign(se(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ot(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),De(()=>{this.accBeta1.assign(ui(this.beta1,this.iterations_+1)),this.accBeta2.assign(ui(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};var rp=class extends kr{static get className(){return\"Adamax\"}constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=je(-this.learningRate,Ce(se(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Gt(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Gt(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=se(l,this.beta2),f=Qt(u),h=Ad(d,f);c.assign(m),l.assign(h);let g=Ce(se(je(n,o),je(m,Ce(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Ce(this.iteration,1)),this.accBeta1.assign(se(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(e){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};var mi=class extends kr{static get className(){return\"SGD\"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];De(()=>{let i=Ce(se(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=$r(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};var op=class extends mi{static get className(){return\"Momentum\"}constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:De(()=>Gt(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&De(()=>{let p,u=Ce(se(this.m,a),i);this.useNesterov?p=Ce(se(this.c,Ce(i,se(u,this.m))),s):p=Ce(se(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ot(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};var np=class extends kr{static get className(){return\"RMSProp\"}constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:De(()=>Gt(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;De(()=>{let c=Ce(se(p,this.decay),se(Zt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=Ce(se(l,this.decay),se(i,1-this.decay)),d=je(se(i,this.learningRate),Rr(Te(c,Ce(Zt(m),this.epsilon)))),f=Ce(se(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Te(s,f);s.assign(h)}else{let l=Ce(se(p,this.decay),se(Zt(i),1-this.decay)),m=Ce(se(u,this.momentum),je(se(i,this.learningRate),Rr(Ce(l,this.epsilon))));p.assign(l),u.assign(m);let d=Te(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ot(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ot(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ot(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};var iX=[Ju,ep,tp,rp,op,np,mi];function XN(){for(let r of iX)rS(r)}var 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o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=I(\"numElements\",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case\"TensorListFromTensor\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=$T(o,n,s);return t.addTensorList(a),[a.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id),s=I(\"dtype\",r,e,t),a=I(\"elementShape\",r,e,t);return[n.concat(s,a)]}case\"TensorListPushBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"tensor\",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case\"TensorListPopBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case\"TensorListSplit\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"lengths\",r,e,t),a=AT(o,s,n);return t.addTensorList(a),[a.idTensor]}case\"TensorListLength\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id);return[ke(n.size(),\"int32\")]}case\"TensorListResize\":{let o=I(\"tensorListId\",r,e,t),n=I(\"size\",r,e,t),a=t.getTensorList(o.id).resize(n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function PT(r,e,t){let[o,n]=I(\"fusedOps\",r,e,t),s=o===\"biasadd\",a=!s,i=n===\"prelu\",p=o===\"fusedbatchnorm\",u=I(\"numArgs\",r,e,t);if(s){if(i&&u!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&s&&u!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(p)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",r,e,t),l=Pl(r,e,t),m=I(\"dataFormat\",r,e,t).toUpperCase(),d=I(\"dilations\",r,e,t),[f,h]=I(\"args\",r,e,t);a&&(h=f,f=void 0);let g=I(\"leakyreluAlpha\",r,e,t);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var OT=(r,e,t,o=Je)=>{switch(r.op){case\"Conv1D\":{let n=I(\"stride\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilation\",r,e,t);return[o.conv1d(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,s,a,i)]}case\"Conv2D\":{let n=I(\"strides\",r,e,t),s=Pl(r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PT(r,e,t);return[o.fused.conv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"FusedDepthwiseConv2dNative\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PT(r,e,t);return[o.fused.depthwiseConv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let n=I(\"outputShape\",r,e,t),s=I(\"strides\",r,e,t),a=Pl(r,e,t);return[o.conv2dTranspose(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,[s[1],s[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let n=I(\"strides\",r,e,t),s=Pl(r,e,t),a=I(\"dilations\",r,e,t),i=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthwiseConv2d(I(\"input\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,i,[a[1],a[2]])]}case\"Conv3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv3d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPoolWithArgmax\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t),i=I(\"includeBatchInIndex\",r,e,t),{result:p,indexes:u}=o.maxPoolWithArgmax(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case\"AvgPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"MaxPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"Dilation2D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dilations\",r,e,t),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[i,p],s,[u,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MT=(r,e,t,o=Je)=>{switch(r.op){case\"Fill\":{let n=I(\"shape\",r,e,t),s=I(\"dtype\",r,e,t),a=I(\"value\",r,e,t);return[o.fill(n,a,s)]}case\"LinSpace\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"num\",r,e,t);return[o.linspace(n,s,a)]}case\"Multinomial\":{let n=I(\"logits\",r,e,t),s=I(\"numSamples\",r,e,t),a=I(\"seed\",r,e,t);return[o.multinomial(n,s,a)]}case\"OneHot\":{let n=I(\"indices\",r,e,t),s=I(\"depth\",r,e,t),a=I(\"onValue\",r,e,t),i=I(\"offValue\",r,e,t),p=I(\"dtype\",r,e,t);return[o.oneHot(n,s,a,i,p)]}case\"Ones\":return[o.ones(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"OnesLike\":return[o.onesLike(I(\"x\",r,e,t))];case\"RandomStandardNormal\":return[o.randomStandardNormal(I(\"shape\",r,e,t),I(\"dtype\",r,e,t),I(\"seed\",r,e,t))];case\"RandomUniform\":return[o.randomUniform(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"dtype\",r,e,t))];case\"RandomUniformInt\":return[o.randomUniformInt(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"seed\",r,e,t))];case\"Range\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"step\",r,e,t);return[o.range(n,s,a,I(\"dtype\",r,e,t))]}case\"TruncatedNormal\":{let n=I(\"shape\",r,e,t),s=I(\"mean\",r,e,t),a=I(\"stdDev\",r,e,t),i=I(\"seed\",r,e,t);return[o.truncatedNormal(n,s,a,I(\"dtype\",r,e,t),i)]}case\"Zeros\":return[o.zeros(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"ZerosLike\":return[o.zerosLike(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function OS(r,e,t){let o=I(\"boxes\",r,e,t),n=I(\"scores\",r,e,t),s=I(\"maxOutputSize\",r,e,t),a=I(\"iouThreshold\",r,e,t),i=I(\"scoreThreshold\",r,e,t),p=I(\"softNmsSigma\",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var LT=async(r,e,t,o,n=Je)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=OS(r,e,t),l=await 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n=I(\"x\",r,e,t),s=I(\"k\",r,e,t),a=I(\"sorted\",r,e,t),i=o.topk(n,s,a);return[i.values,i.indices]}case\"UpperBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.upperBound(n,s)]}case\"Unique\":{let n=I(\"x\",r,e,t),s=o.unique(n);return[s.values,s.indices]}case\"UniqueV2\":{let n=I(\"x\",r,e,t),s=I(\"axis\",r,e,t),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var zT=(r,e,t,o=Je)=>{switch(r.op){case\"Const\":return e[r.name];case\"PlaceholderWithDefault\":let n=I(\"default\",r,e,t);return[Bt(r.name,e,t)||n];case\"Placeholder\":return[Bt(r.name,e,t)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=I(\"x\",r,e,t);return[Bs(c)]}case\"IdentityN\":return I(\"x\",r,e,t).map(c=>Bs(c));case\"Snapshot\":let s=I(\"x\",r,e,t);return[Bs(s)];case\"Shape\":return[o.tensor1d(I(\"x\",r,e,t).shape,\"int32\")];case\"ShapeN\":return I(\"x\",r,e,t).map(c=>o.tensor1d(c.shape));case\"Size\":return[o.scalar(I(\"x\",r,e,t).size,\"int32\")];case\"Rank\":return[o.scalar(I(\"x\",r,e,t).rank,\"int32\")];case\"NoOp\":return[o.scalar(1)];case\"Print\":let a=I(\"x\",r,e,t),i=I(\"data\",r,e,t),p=I(\"message\",r,e,t),u=I(\"summarize\",r,e,t);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(p);for(let c=0;ce.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ke(this.size(),\"int32\")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),De(()=>{let n=fo(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I(\"keyDType\",r,e,t),a=I(\"valueDType\",r,e,t),i=new vf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"values\",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"defaultValue\",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let n=I(\"tableHandle\",r,e,t,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var WT=(r,e,t,o=Je)=>{switch(r.op){case\"ResizeBilinear\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case\"ResizeNearestNeighbor\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case\"CropAndResize\":{let n=I(\"image\",r,e,t),s=I(\"boxes\",r,e,t),a=I(\"boxInd\",r,e,t),i=I(\"cropSize\",r,e,t),p=I(\"method\",r,e,t),u=I(\"extrapolationValue\",r,e,t);return[o.image.cropAndResize(n,s,a,i,p,u)]}case\"ImageProjectiveTransformV3\":{let n=I(\"images\",r,e,t),s=I(\"transforms\",r,e,t),a=I(\"outputShape\",r,e,t),i=I(\"fillValue\",r,e,t),p=I(\"interpolation\",r,e,t),u=I(\"fillMode\",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UT=(r,e,t,o=Je)=>{switch(r.op){case\"Equal\":return[o.equal(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"NotEqual\":return[o.notEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Greater\":return[o.greater(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"GreaterEqual\":return[o.greaterEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Less\":return[o.less(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LessEqual\":return[o.lessEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalAnd\":return[o.logicalAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalNot\":return[o.logicalNot(I(\"a\",r,e,t))];case\"LogicalOr\":return[o.logicalOr(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Select\":case\"SelectV2\":return[o.where(I(\"condition\",r,e,t),I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"BitwiseAnd\":return[o.bitwiseAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GT=(r,e,t,o=Je)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[o.matMul(I(\"a\",r,e,t),I(\"b\",r,e,t),I(\"transposeA\",r,e,t),I(\"transposeB\",r,e,t))];case\"Einsum\":return[o.einsum(I(\"equation\",r,e,t),...I(\"tensors\",r,e,t))];case\"Transpose\":return[o.transpose(I(\"x\",r,e,t),I(\"perm\",r,e,t))];case\"_FusedMatMul\":let[n,s]=I(\"fusedOps\",r,e,t),a=n===\"biasadd\",i=s===\"prelu\",p=I(\"numArgs\",r,e,t),u=I(\"leakyreluAlpha\",r,e,t);if(a){if(i&&p!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&p!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,l]=I(\"args\",r,e,t);return[o.fused.matMul({a:I(\"a\",r,e,t),b:I(\"b\",r,e,t),transposeA:I(\"transposeA\",r,e,t),transposeB:I(\"transposeB\",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];case\"MatrixBandPart\":return[o.linalg.bandPart(I(\"a\",r,e,t),I(\"numLower\",r,e,t),I(\"numUpper\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HT=(r,e,t,o=Je)=>{switch(r.op){case\"EuclideanNorm\":return[o.euclideanNorm(I(\"x\",r,e,t),I(\"axis\",r,e,t),I(\"keepDims\",r,e,t))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"FusedBatchNormV3\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"LRN\":return[o.localResponseNormalization(I(\"x\",r,e,t),I(\"radius\",r,e,t),I(\"bias\",r,e,t),I(\"alpha\",r,e,t),I(\"beta\",r,e,t))];case\"Softmax\":return[o.softmax(I(\"x\",r,e,t))];case\"LogSoftmax\":return[o.logSoftmax(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KT=(r,e,t,o=Je)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I(\"paramsNestedSplits\",r,e,t),I(\"paramsDenseValues\",r,e,t),I(\"indices\",r,e,t),I(\"outputRaggedRank\",r,e,t));return n.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I(\"starts\",r,e,t),I(\"limits\",r,e,t),I(\"splits\",r,e,t));return[n,s]}case\"RaggedTensorToTensor\":return[o.raggedTensorToTensor(I(\"shape\",r,e,t),I(\"values\",r,e,t),I(\"defaultValue\",r,e,t),I(\"rowPartitionTensors\",r,e,t),I(\"rowPartitionTypes\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var qT=(r,e,t,o=Je)=>{switch(r.op){case\"Max\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.max(I(\"x\",r,e,t),i,p)]}case\"Mean\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.mean(I(\"x\",r,e,t),i,p)]}case\"Min\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.min(I(\"x\",r,e,t),i,p)]}case\"Sum\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.sum(I(\"x\",r,e,t),i,p)]}case\"All\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.all(I(\"x\",r,e,t),i,p)]}case\"Any\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.any(I(\"x\",r,e,t),i,p)]}case\"ArgMax\":{let i=I(\"axis\",r,e,t);return[o.argMax(I(\"x\",r,e,t),i)]}case\"ArgMin\":{let i=I(\"axis\",r,e,t);return[o.argMin(I(\"x\",r,e,t),i)]}case\"Prod\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.prod(I(\"x\",r,e,t),i,p)]}case\"Cumprod\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumprod(I(\"x\",r,e,t),i,p,u)]}case\"Cumsum\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumsum(I(\"x\",r,e,t),i,p,u)]}case\"Bincount\":let n=I(\"x\",r,e,t),s=I(\"weights\",r,e,t),a=I(\"size\",r,e,t);return[o.bincount(n,s,a)];case\"DenseBincount\":{let i=I(\"x\",r,e,t),p=I(\"weights\",r,e,t),u=I(\"size\",r,e,t),c=I(\"binaryOutput\",r,e,t);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var jT=(r,e,t,o=Je)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let n=I(\"n\",r,e,t),s=I(\"axis\",r,e,t),a=I(\"tensors\",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case\"Gather\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gather(n,o.cast(s,\"int32\"),0)]}case\"GatherV2\":{let n=I(\"axis\",r,e,t),s=I(\"batchDims\",r,e,t),a=I(\"x\",r,e,t),i=I(\"indices\",r,e,t);return[o.gather(a,o.cast(i,\"int32\"),n,s)]}case\"Reverse\":{let n=I(\"dims\",r,e,t),s=[];for(let i=0;i{let n=I(\"axis\",r,e,t),s=I(\"tensors\",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error(\"the input tensors shape does not match\");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case\"Unpack\":{let n=I(\"axis\",r,e,t),s=I(\"tensor\",r,e,t);return o.unstack(s,n)}case\"Tile\":{let n=I(\"reps\",r,e,t);return[o.tile(I(\"x\",r,e,t),n)]}case\"Split\":case\"SplitV\":{let n=I(\"axis\",r,e,t),s=I(\"numOrSizeSplits\",r,e,t),a=I(\"x\",r,e,t);return o.split(a,s,n)}case\"ScatterNd\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"shape\",r,e,t);return[o.scatterND(n,s,a)]}case\"GatherNd\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gatherND(n,s)]}case\"SparseToDense\":{let n=I(\"sparseIndices\",r,e,t),s=I(\"outputShape\",r,e,t),a=I(\"sparseValues\",r,e,t),i=I(\"defaultValue\",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}case\"TensorScatterUpdate\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"tensor\",r,e,t);return[o.tensorScatterUpdate(a,n,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var XT=(r,e,t,o=Je)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I(\"indices\",r,e,t),I(\"values\",r,e,t),I(\"denseShape\",r,e,t),I(\"defaultValue\",r,e,t));return[n,s,a,i]}case\"SparseReshape\":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I(\"inputIndices\",r,e,t),I(\"inputShape\",r,e,t),I(\"newShape\",r,e,t));return[n,s]}case\"SparseSegmentMean\":return[o.sparse.sparseSegmentMean(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];case\"SparseSegmentSum\":return[o.sparse.sparseSegmentSum(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YT=(r,e,t,o=Je)=>{switch(r.op){case\"FFT\":return[o.fft(I(\"x\",r,e,t))];case\"IFFT\":return[o.ifft(I(\"x\",r,e,t))];case\"RFFT\":return[o.rfft(I(\"x\",r,e,t))];case\"IRFFT\":return[o.irfft(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QT=(r,e,t,o=Je)=>{switch(r.op){case\"StaticRegexReplace\":return[o.string.staticRegexReplace(I(\"input\",r,e,t),I(\"pattern\",r,e,t),I(\"rewrite\",r,e,t),I(\"replaceGlobal\",r,e,t))];case\"StringNGrams\":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I(\"data\",r,e,t),I(\"dataSplits\",r,e,t),I(\"separator\",r,e,t),I(\"nGramWidths\",r,e,t),I(\"leftPad\",r,e,t),I(\"rightPad\",r,e,t),I(\"padWidth\",r,e,t),I(\"preserveShortSequences\",r,e,t));return[n,s]}case\"StringSplit\":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I(\"input\",r,e,t),I(\"delimiter\",r,e,t),I(\"skipEmpty\",r,e,t));return[n,s,a]}case\"StringToHashBucketFast\":return[o.string.stringToHashBucketFast(I(\"input\",r,e,t),I(\"numBuckets\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZT=(r,e,t,o=Je)=>{switch(r.op){case\"Cast\":return[o.cast(I(\"x\",r,e,t),I(\"dtype\",r,e,t))];case\"ExpandDims\":{let n=I(\"axis\",r,e,t);return[o.expandDims(I(\"x\",r,e,t),n)]}case\"Squeeze\":{let n=I(\"axis\",r,e,t);return[o.squeeze(I(\"x\",r,e,t),n)]}case\"Reshape\":return[o.reshape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"EnsureShape\":return[o.ensureShape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"MirrorPad\":return[o.mirrorPad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"mode\",r,e,t))];case\"PadV2\":case\"Pad\":return[o.pad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"constantValue\",r,e,t))];case\"SpaceToBatchND\":{let n=I(\"blockShape\",r,e,t),s=I(\"paddings\",r,e,t);return[o.spaceToBatchND(I(\"x\",r,e,t),n,s)]}case\"BatchToSpaceND\":{let n=I(\"blockShape\",r,e,t),s=I(\"crops\",r,e,t);return[o.batchToSpaceND(I(\"x\",r,e,t),n,s)]}case\"DepthToSpace\":{let n=I(\"blockSize\",r,e,t),s=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthToSpace(I(\"x\",r,e,t),n,s)]}case\"BroadcastTo\":return[o.broadcastTo(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"BroadcastArgs\":return[o.broadcastArgs(I(\"s0\",r,e,t),I(\"s1\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function MS(r,e,t,o,n=De){let s=((a,i,p)=>{switch(a.category){case\"arithmetic\":return n(()=>TT(a,i,p));case\"basic_math\":return n(()=>_T(a,i,p));case\"control\":return FT(a,i,p);case\"convolution\":return n(()=>OT(a,i,p));case\"creation\":return n(()=>MT(a,i,p));case\"dynamic\":return LT(a,i,p);case\"evaluation\":return n(()=>BT(a,i,p));case\"image\":return n(()=>WT(a,i,p));case\"graph\":return n(()=>zT(a,i,p));case\"logical\":return n(()=>UT(a,i,p));case\"matrices\":return n(()=>GT(a,i,p));case\"normalization\":return n(()=>HT(a,i,p));case\"ragged\":return n(()=>KT(a,i,p));case\"reduction\":return n(()=>qT(a,i,p));case\"slice_join\":return n(()=>jT(a,i,p));case\"sparse\":return n(()=>XT(a,i,p));case\"spectral\":return n(()=>YT(a,i,p));case\"string\":return n(()=>QT(a,i,p));case\"transformation\":return n(()=>ZT(a,i,p));case\"hash_table\":return VT(a,i,p,o);case\"custom\":let u=pf(a.op);if(u&&u.customExecutor)return u.customExecutor(new wf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var Ml=class{constructor(e={},t={},o={},n={},s){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function LS(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=new Set(Object.keys(r).map(m=>Nr(m)[0]));o=o||[];let c=new Set(o.map(m=>Nr(m.name)[0])),l=[...e];for(;l.length>0;){let m=l.pop();if((fu(m)||A8(m)||F8(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&!u.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function JT(r,e){let{usedNodes:t,inputs:o}=e,n=Object.keys(o).map(g=>Nr(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],a=g=>t.has(typeof g==\"string\"?g:g.name);function i(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let p=i([...n,...r.weights,...s]).filter(a),u=i([...p,...Object.values(r.nodes)]).filter(a),c=new Map(u.map(g=>[g.name,g])),l={};for(let g of u){l[g.name]=l[g.name]||0;for(let x of g.children)a(x)||(l[x.name]=Number.POSITIVE_INFINITY),l[x.name]=(l[x.name]||0)+1}let m=Object.entries(l).filter(([,g])=>g===0).map(([g])=>g),d=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(a))--l[b.name]===0&&(d.push(b.name),m.push(b.name))}let f=d.map(g=>c.get(g)),h=_8(f,p);return E8(h,p),h}function _8(r,e){let t=new Map(r.map(a=>[a.name,a])),o=e.map(a=>a.name),n=new Set(o);for(;o.length>0;){let a=o.pop(),i=t.get(a);for(let p of i.children)!t.has(p.name)||n.has(p.name)||(n.add(p.name),o.push(p.name))}return r.filter(a=>n.has(a.name))}var gc=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function E8(r,e){let t=new Map(r.map((i,p)=>[i.name,p])),o=new Set(e.map(i=>i.name)),n=i=>o.has(typeof i==\"string\"?i:i.name),s=new Set(r.map(i=>i.name)),a=i=>s.has(typeof i==\"string\"?i:i.name);for(let i of r){for(let p of i.children.filter(a)){if(!t.has(p.name))throw new gc(`Child ${p.name} of node ${i.name} is unreachable.`);if(t.get(i.name)>t.get(p.name))throw new gc(`Node ${i.name} is scheduled to run after its child ${p.name}.`)}if(!n(i))for(let p of i.inputs){if(!t.has(p.name))throw new gc(`Input ${p.name} of node ${i.name} is unreachable.`);if(t.get(p.name)>t.get(i.name))throw new gc(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function e_(r){let e=new Map(r.map((i,p)=>[i.name,p])),t=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>fu(i)?t:p),n=i=>{let p=o[e.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,c)=>Math.max(u,c),o[p])),a=new Map;for(let i=0;ie[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new r(e.functions[o],this)})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPARATOR)+\"--\"+n.join(this.SEPARATOR)}compile(e,t){let o=LS(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let u=t.map(l=>l.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`)}let i=JT(this.graph,o),p=e_(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return $r(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(m=>this.graph.nodes[Nr(m)[0]]),s=t.map(m=>Nr(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(e,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=A().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},l={};return De(()=>{let m=new Ml(this.weightMap,c,l,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(x=>{let[b,C]=Nr(x,m),S=[];S[C]=e[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=MS(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);d[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,d,m,f,a,g.get(x.name))}return this.parent==null&&m.dispose(f),t.map(x=>Bt(x,d,m))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){if(!(fu(t)||a.has(e))){for(let p of o[e])p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length);for(let p of t.inputs){if(fu(p))continue;let u=hS(p.name,o,n);if(u!=null)for(let c of u){if(!c||c.kept||s.has(c.id))continue;let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,o,n,s,a){function i(p){return fu(p)||s.has(p.name)}if(!(fu(e)||a==null))for(let p of a){if(i(p))continue;let u=hS(p.name,t,o);for(let c of u)!c||c.kept||n.has(c.id)||c.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=A().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new Ml(this.weightMap,n,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Bt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(S=>this.graph.nodes[Nr(S)[0]]),i=o.map(S=>Nr(S)[0]),p=new Set(i),u=i.map(S=>this.graph.nodes[S]);u.length===0&&(u=this._outputs);let{usedNodes:c,missingInputs:l,dynamicNode:m,syncInputs:d}=LS(e,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(S=>({node:S,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(S=>{let[k,_]=Nr(S),$=[];$[_]=e[S],h[k]=$});let g={},x=this.getFrozenTensorIds(h),b={};for(;f.length>0;){let S=this.processStack(a,f,t,h,b,x,p,g,c);await Promise.all(S)}m==null&&!n&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let C=u.filter(S=>!fu(S)&&!Bt(S.name,h,t)).map(S=>S.name);if(C.length>0){let S=\"\";throw m!=null&&(S=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${C}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`)}return h}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m=\"\";if(l.node.op===\"Enter\"&&I(\"isConstant\",l.node,n,o)&&([m]=Ls(l.node.name,o)),n[l.node.name]==null){let d=MS(l.node,n,o,this._resourceManager);m||([m]=Ls(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=Ls(i.name,o);s[p]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Nr(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Nr(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Nr(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var kf=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let o=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=n,this.version=`${o.versions.producer}.${o.versions.minConsumer}`,this.executor=new Ll(Ol.Instance.transformGraph(o,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=Ol.Instance.transformGraph(e.modelInitializer);this.initializer=new Ll(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e==\"string\"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[0]}if(e.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof mt?[e]:e,o={};return t.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return e}predict(e,t){let o=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(e,t){let o=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(e){var t;if(!(e instanceof mt)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let 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this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,o=Object.keys(t);for(let n=0;n1?o:o[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return 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FY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Q(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat===\"channelsLast\",S=new tt(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),$=k[0],R=C?k[1]:k[2],D=C?k[2]:1,P=C?1:k[1],O=S.strides[0],M=C?S.strides[1]:S.strides[2],L=C?S.strides[2]:1,B=C?1:S.strides[1],z=t.data.get(n.dataId).values,U=t.data.get(s.dataId).values,j=S.values;for(let q=0;q=m.inHeight)continue;let le=oe*_[0],be=Y+ie*R;for(let _e=0;_e=m.inWidth)continue;let ct=le+Pe*_[1],He=be+st*D,lt=ct;for(let it=0;it=u.inDepth)continue;let q=U*D[0],Y=O+j*R[1];for(let J=0;J=u.inHeight)continue;let ie=q+ee*D[1],le=Y+oe*R[2];for(let be=0;be=u.inWidth)continue;let st=ie+Fe*D[2],ct=le+Pe*u.inChannels,He=st;for(let lt=0;ltMath.cos(r)),kE={kernelName:sn,backendName:\"cpu\",kernelFunc:XY};var YY=Ie(an,r=>Math.cosh(r)),NE={kernelName:an,backendName:\"cpu\",kernelFunc:YY};function QY(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=me([f,h,g,d],\"float32\"),b=t.data.get(s.dataId).values,C=t.data.get(a.dataId).values,S=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let $=0;$=c)continue;let B=h>1?(O-D)*(l-1)/(h-1):0,z=g>1?(M-P)*(m-1)/(g-1):0;for(let U=0;U1?D*(l-1)+U*B:.5*(D+O)*(l-1);if(j<0||j>l-1){for(let q=0;q1?P*(m-1)+re*z:.5*(P+M)*(m-1);if(ne<0||ne>m-1){for(let le=0;le1?P*(m-1)+q*z:.5*(P+M)*(m-1);if(Y<0||Y>m-1){for(let ne=0;nex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=t.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. 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Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p),l;if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))l=lr({inputs:{x:n},backend:t});else{let m=t.data.get(n.dataId).values,d=y.computeStrides(n.shape),f=vc(m,n.shape,n.dtype,d,c,\"max\");l=t.makeTensorInfo(c.outShape,n.dtype,f.values)}return l}var c$={kernelName:Wn,backendName:\"cpu\",kernelFunc:LQ};function BQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o;Q(n,\"maxPool3d\");let c=w.computePool3DInfo(n.shape,s,a,1,i,p,u),l=t.data.get(n.dataId).values,m=zf(l,n.shape,n.dtype,y.computeStrides(n.shape),c,\"max\");return t.makeTensorInfo(m.shape,\"float32\",m.values)}var l$={kernelName:ia,backendName:\"cpu\",kernelFunc:BQ};function zQ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o;Q([n,s],\"maxPool3DGrad\");let c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=t.bufferSync(s),m=aE(l,c),d=c.strideDepth,f=c.strideHeight,h=c.strideWidth,g=c.dilationDepth,x=c.dilationHeight,b=c.dilationWidth,C=c.effectiveFilterDepth,S=c.effectiveFilterHeight,k=c.effectiveFilterWidth,_=C-1-c.padInfo.front,$=k-1-c.padInfo.left,R=S-1-c.padInfo.top,D=me(s.shape,\"float32\"),P=t.bufferSync(n);for(let O=0;O=c.outDepth||Math.floor(re)!==re))for(let ne=0;ne=c.outHeight||Math.floor(ee)!==ee))for(let oe=0;oe=c.outWidth||Math.floor(ie)!==ie)continue;let le=C*S*k-1-m.get(O,re,ee,ie,M),be=J*S*k+ne*k+oe,_e=le===be?1:0;if(_e===0)continue;let ve=P.get(O,re,ee,ie,M);Y+=ve*_e}}}D.set(Y,O,L,B,z,M)}return t.makeTensorInfo(D.shape,D.dtype,D.values)}var m$={kernelName:Gi,backendName:\"cpu\",kernelFunc:zQ};function VQ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Q([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=t.data.get(i.dataId).values,f=me(m.outShape,i.dtype,Bf(d,i.shape,i.dtype,m).values),h=m.strideHeight,g=m.strideWidth,x=m.dilationHeight,b=m.dilationWidth,C=m.effectiveFilterHeight,S=m.effectiveFilterWidth,k=S-1-m.padInfo.left,_=C-1-m.padInfo.top,$=me(i.shape,\"float32\"),R=t.data.get(n.dataId).values,D=me(n.shape,\"float32\",R);for(let P=0;P=m.outHeight||Math.floor(q)!==q))for(let Y=0;Y=m.outWidth||Math.floor(J)!==J)continue;let re=C*S-1-f.get(P,q,J,O),ne=j*S+Y,ee=re===ne?1:0;if(ee===0)continue;let oe=D.get(P,q,J,O);U+=oe*ee}}$.set(U,P,M,L,O)}return t.makeTensorInfo($.shape,$.dtype,$.values)}var d$={kernelName:Ui,backendName:\"cpu\",kernelFunc:VQ};function f$(r,e,t,o,n){let s=y.computeStrides(e),a=vc(r,e,t,s,n,\"max\"),i=Bf(r,e,t,n,!0,o);return[a.values,i.values]}var h$={kernelName:ua,backendName:\"cpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;Q(o,\"MaxPoolWithArgmax\");let u=p.data.get(o.dataId).values,c=w.computePool2DInfo(o.shape,n,s,[1,1],a),[l,m]=f$(u,o.shape,o.dtype,i,c),d=p.write(l,c.outShape,o.dtype),f=p.write(m,c.outShape,o.dtype);return[{dataId:d,shape:c.outShape,dtype:o.dtype},{dataId:f,shape:c.outShape,dtype:\"int32\"}]}};function WQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=y.parseAxisParam(s,n.shape),u=w.computeOutAndReduceShapes(n.shape,i)[1],c=y.sizeFromShape(u),l=[],m=t.makeTensorInfo([],\"float32\",new Float32Array([c]));l.push(m);let d=Ro({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}});l.push(d);let f=Ul({inputs:{a:d,b:m},backend:t});l.push(f);let h=fi({inputs:{x:f},backend:t,attrs:{axis:s,keepDims:a}});return l.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}var g$={kernelName:Un,backendName:\"cpu\",kernelFunc:WQ};function UQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;Q(n,\"min\");let i=y.parseAxisParam(s,n.shape),p=i,u=w.getAxesPermutation(p,n.shape.length),c=n;u!=null&&(c=St({inputs:{x:n},backend:t,attrs:{perm:u}}),p=w.getInnerMostAxes(p.length,n.shape.length)),w.assertAxesAreInnerMostDims(\"min\",p,c.shape.length);let[l,m]=w.computeOutAndReduceShapes(c.shape,p),d=y.sizeFromShape(m),f=y.makeZerosTypedArray(y.sizeFromShape(l),c.dtype),h=t.data.get(c.dataId).values;for(let x=0;xC[0]+n.shape[S]+C[1]),p=s.map(C=>C[0]),u=s.map((C,S)=>C[0]+n.shape[S]),c=a===\"reflect\"?0:1,l=t.data.get(n.dataId).values,m=n.shape.length,d=y.computeStrides(n.shape),f=y.sizeFromShape(i),h=i.length,g=y.computeStrides(i),x=y.getTypedArrayFromDType(n.dtype,f);for(let C=0;C=u[_]&&(S[_]=(u[_]-1)*2-S[_]+c);S=S.map((_,$)=>_-p[$]);let k=y.locToIndex(S,m,d);x[C]=l[k]}return{dataId:t.write(x,i,n.dtype),shape:i,dtype:n.dtype}}var y$={kernelName:Kn,backendName:\"cpu\",kernelFunc:GQ};var HQ=Ve((r,e)=>{let t=r%e;return r<0&&e<0||r>=0&&e>=0?t:(t+e)%e}),KQ=Ye(qn,HQ),b$={kernelName:qn,backendName:\"cpu\",kernelFunc:KQ};var w$=zp(jw());function vI(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=n.shape.length,i=s;if(i===-1&&(i=a-1),i!==a-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${a} and dim was ${i}`);let p=y.parseAxisParam([i],n.shape),u=II({inputs:{x:n},backend:t,attrs:{reductionIndices:p,keepDims:!1}}),c=w.expandShapeToKeepDim(u.shape,p),l=We({inputs:{x:u},backend:t,attrs:{shape:c}}),m=Vl({inputs:{a:n,b:l},backend:t}),d=qS({inputs:{x:m},backend:t}),f=fi({inputs:{x:d},backend:t,attrs:{axis:p,keepDims:!1}}),h=We({inputs:{x:f},backend:t,attrs:{shape:c}}),g=Ul({inputs:{a:d,b:h},backend:t});return t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var C$={kernelName:Is,backendName:\"cpu\",kernelFunc:vI};function qQ(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o;Q(n,\"multinomial\");let p=i?n:vI({inputs:{logits:n},backend:t,attrs:{dim:-1}}),u=p.shape[0],c=p.shape[1],l=t.data.get(p.dataId).values,m=[u,s],d=y.makeZerosTypedArray(y.sizeFromShape(m),\"int32\");for(let f=0;f=0&&l[m]{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=kc({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=hu({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var $$={kernelName:la,backendName:\"cpu\",kernelFunc:kI};function t7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;Q(n,\"pad\");let i=s.map((b,C)=>b[0]+n.shape[C]+b[1]),p=s.map(b=>b[0]),u=t.data.get(n.dataId).values,c=y.sizeFromShape(n.shape),l=n.shape.length,m=y.computeStrides(n.shape),d=y.sizeFromShape(i),f=i.length,h=y.computeStrides(i),g=y.getTypedArrayFromDType(n.dtype,d);a!==0&&g.fill(a);for(let 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0:return AR();case 1:return TZ(r,e,t);case 2:return PZ(r,e,t);case 3:return EZ(r,e,t);case 4:return RZ(r,e,t);case 5:return DZ(r,e);case 6:return AZ(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function yZ(r){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${r.texture2D}(textureSampler, uv).r;\n }\n `}function bZ(r){return`\n void setOutput(float val) {\n ${r.output} = vec4(val, 0, 0, 0);\n }\n `}function CZ(r){return`\n void setOutput(vec4 val) {\n ${r.output} = val;\n }\n `}function wZ(r){return`${r.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${r.varyingFs} vec2 resultUV;\n ${r.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${r.defineSpecialNaN}\n ${r.defineSpecialInf}\n ${r.defineRound}\n\n int 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Zf(r,e,t){let{newShape:o,keptDims:n}=y.squeezeShape(e),s=e.length,a=r&&s===3&&e[0]===1,i=a?e.slice(1):o,p=!r&&s>1&&!y.arraysEqual(e,t)&&o.lengthr[t]).join(\", \")}function PR(r,e,t,o){let n=t.map((c,l)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:e.variableNames[l],shapeInfo:m}}),s=n.map(c=>c.shapeInfo),a={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},i=RR(n,a,e),p=RI(r.gl,i),u=r.createProgram(p);return A().get(\"ENGINE_COMPILE_ONLY\")?{program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(u),Object.assign({program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a},XI(r,e,u)))}function XI(r,e,t){let o=[],n=[],s,a,i,p=null,u=null;u=r.getUniformLocation(t,\"NAN\",!1),A().getNumber(\"WEBGL_VERSION\")===1&&(p=r.getUniformLocation(t,\"INFINITY\",!1));let c=!1;for(let l of e.variableNames){let 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Shape ${i} and ${p} must match`)})}function OR(r,e,t,o,n){e.program.enableShapeUniforms||(FR(e.inShapeInfos,t),FR([e.outShapeInfo],[o]));let s=o.texData.texture,a=o.texData.texShape;o.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,a[0],a[1]):r.setOutputMatrixTexture(s.texture,a[0],a[1]),r.setProgram(e.webGLProgram),r.bindVertexArray(e.webGLProgram.vao),A().getNumber(\"WEBGL_VERSION\")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,1/0),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN);for(let p=0;p{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!a.isUniform){let p=a.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:l}=Zf(r.packedInputs,a.shape,p),m=\"\",d=\"\",f=\"\";if(c.length===1&&r.packedInputs){let k=[Math.ceil(p[0]/2),Math.ceil(p[1]/2)];m=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!r.packedInputs)d=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let k=y.computeStrides(c);f=`${k[0]===p[1]}_${k[k.length-1]===p[1]}`}let h=a.shape.length,g=c.length===2&&y.arraysEqual(a.shape,p),x=y.sizeFromShape(a.shape)===1,b=w.getBroadcastDims(a.shape,t.shape),C=!r.packedInputs&&h===t.shape.length&&y.arraysEqual(p,t.texData.texShape),S=r.packedInputs||c.length>2?\"\":`${p[0]>1}_${p[1]>1}`;o+=`${h}_${C}_${u?l:\"\"}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${S}_${i}`}else{let p=a.isUniform?\"uniform\":a.texData.texShape;o+=`${a.shape}_${p}_${i}`}});let n=r.userCode,s=r.constructor.name;return s+=\"_\"+o+\"_\"+n+`${A().getNumber(\"WEBGL_VERSION\")}`,s}function ut(r){return A().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&r<=4}var Jf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=gu.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=It();this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n 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texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${t.output} = result;\n }\n `}};var th=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=mr.DOWNLOAD;let t=It();this.outputShape=e,this.userCode=`\n ${Qf}\n\n void main() {\n float x = getAAtOutCoords();\n ${t.output} = encode_float(x);\n }\n `}};var rh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=mr.DOWNLOAD;let t=It();this.outputShape=e,this.userCode=`\n ${Qf}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${t.output} = encode_float(x);\n }\n `}};var ZZ={R:0,G:1,B:2,A:3},Zl=class{constructor(e,t=!1,o=\"RGBA\"){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=It();this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let s=\"result\";t&&(s=\"floor(result * 255. + 0.5)\");let a=\"\";for(let i=0;inv,createBufferFromOutputTexture:()=>iv,createFloat16MatrixTexture:()=>ev,createFloat16PackedMatrixTexture:()=>ov,createFloat32MatrixTexture:()=>JI,createIndexBuffer:()=>ZI,createPackedMatrixTexture:()=>rv,createUnsignedBytesMatrixTexture:()=>tv,createVertexBuffer:()=>QI,createVertexShader:()=>YI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>pv,downloadFloat32MatrixFromBuffer:()=>uv,downloadMatrixFromPackedOutputTexture:()=>lv,downloadPackedMatrixFromBuffer:()=>cv,getInternalFormatForFloat16MatrixTexture:()=>sh,getInternalFormatForFloat16PackedMatrixTexture:()=>uh,getInternalFormatForFloat32MatrixTexture:()=>nh,getInternalFormatForPackedMatrixTexture:()=>ih,getInternalFormatForUnsignedBytesMatrixTexture:()=>ah,uploadDenseMatrixToTexture:()=>sv,uploadPixelDataToTexture:()=>av});function YI(r){let e=It(),t=`${e.version}\n precision highp float;\n ${e.attribute} vec3 clipSpacePos;\n ${e.attribute} vec2 uv;\n ${e.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = 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Jl(r,n,s,sh(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function ah(r){return r.downloadTextureFormat}function tv(r,e,t,o){let[n,s]=gp(e,t);return Jl(r,n,s,ah(o),r.RGBA,r.UNSIGNED_BYTE)}function ih(r){return r.internalFormatPackedFloat}function rv(r,e,t,o){let[n,s]=Ma(e,t);return Jl(r,n,s,ih(o),r.RGBA,r.FLOAT)}function uh(r){return r.internalFormatPackedHalfFloat}function ov(r,e,t,o){let[n,s]=Ma(e,t);return Jl(r,n,s,uh(o),r.RGBA,o.textureTypeHalfFloat)}function nv(r,e,t){return ce(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),jf(r,e,\"clipSpacePos\",t,3,20,0)&&jf(r,e,\"uv\",t,2,20,12)}function sv(r,e,t,o,n,s){ce(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,p;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,p=r.RGBA):(a=new 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ce(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),ce(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),ce(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function uv(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function pv(r,e,t,o){let[n,s]=gp(e,t),a=4,i=new Uint8Array(IR(e*t,a));return ce(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function cv(r,e,t,o,n,s,a,i){let p=r,u=new Float32Array(vR(s,a));return p.bindBuffer(p.PIXEL_PACK_BUFFER,e),p.getBufferSubData(p.PIXEL_PACK_BUFFER,0,u),p.bindBuffer(p.PIXEL_PACK_BUFFER,null),u}function lv(r,e,t){let o=new Float32Array(e*t*4);return ce(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var bp=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=A().getNumber(\"WEBGL_VERSION\");if(e!=null?(this.gl=e,NI(t,e)):this.gl=Kr(t),e=this.gl,A().getNumber(\"WEBGL_VERSION\")===2){let s=e;this.createVertexArray=()=>ce(s,()=>s.createVertexArray()),this.bindVertexArray=a=>ce(s,()=>s.bindVertexArray(a)),this.deleteVertexArray=a=>ce(s,()=>s.deleteVertexArray(a)),this.getVertexArray=()=>ce(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(e!=null){let s=e.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>ce(e,()=>s.createVertexArrayOES()),this.bindVertexArray=a=>ce(e,()=>s.bindVertexArrayOES(a)),this.deleteVertexArray=a=>ce(e,()=>s.deleteVertexArrayOES(a)),this.getVertexArray=()=>ce(e,()=>e.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let o=\"WEBGL_color_buffer_float\",n=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),A().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=Nc(this.gl,s),qr(this.gl,a))this.textureHalfFloatExtension=Nc(this.gl,a);else if(A().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(o),qr(this.gl,n))this.colorBufferHalfFloatExtension=Nc(this.gl,n);else if(A().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(o=\"EXT_color_buffer_float\",qr(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(qr(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=QI(this.gl),this.indexBuffer=ZI(this.gl),this.framebuffer=LI(this.gl),this.textureConfig=Xl(this.gl,this.textureHalfFloatExtension)}get debug(){return A().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),this.outputTexture!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let e=this.gl;ce(e,()=>e.finish()),ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ce(e,()=>e.deleteFramebuffer(this.framebuffer)),ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ce(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),JI(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),ev(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),tv(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),av(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,o,n){this.throwIfDisposed(),sv(this.gl,e,t,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),ov(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),rv(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Xf(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>pv(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return cv(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return uv(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=iv(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(A().getBool(\"WEBGL_FENCE_API_ENABLED\")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let a=n.clientWaitSync(s,0,0);return a===n.ALREADY_SIGNALED||a===n.CONDITION_SATISFIED},t=s}else A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(t=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(t,A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):o=()=>!0;return{query:t,isFencePassed:o}}downloadMatrixFromPackedTexture(e,t,o){return this.downloadMatrixDriver(e,()=>lv(this.gl,t,o))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=YI(t));let o=DI(t);ce(t,()=>t.attachShader(o,this.vertexShader)),ce(t,()=>t.attachShader(o,e)),AI(t,o);let n=Object.assign(o,{vao:this.createVertexArray()});return this.debug&&Yl(t,n),n}buildVao(e){this.setProgram(e),this.bindVertexArray(e.vao);let t=this.gl;ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),nv(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Yl(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?BI(this.gl,e,t):zI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),VI(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Ma(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&Yl(this.gl,this.program),Tc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}ce(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ce(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Nc(this.gl,A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let t=this.gl,o=this.getQueryTimerExtensionWebGL2();t.endQuery(o.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(e,A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let o=this.gl;return o.getQueryParameter(e,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(e,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(e,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=JZ(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let o;\"setTimeoutCustom\"in A().platform&&(o=A().platform.setTimeoutCustom.bind(A().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Ql(this.gl,e,this.framebuffer),this.debug&&Tc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Ql(this.gl,this.outputTexture,this.framebuffer),this.debug&&Tc(this.gl)):Xf(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;Ql(n,e,this.framebuffer),this.debug&&Tc(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,o)),ce(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,o,n))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function JZ(r){let e=0;for(;e`${r}.${t}`)}function Rt(r,e){return e===1?[r]:dv(r,e)}function ED(r,e){if(r===1)return\"rc\";let t=\"\";for(let o=0;o ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let o=this.rank-2;o= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o= ${o};\n bool rEdge = rp1 >= ${n};\n `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n cEdge ? 0. : getA(${t[1]}),\n rEdge ? 0. : getA(${t[2]}),\n rEdge || cEdge ? 0. : getA(${t[3]})`}};var Mc=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let o=\"\";for(let n=0;n<4;n++){let s=\"thisRC = rc;\";n%2===1&&(s+=\"thisRC.z += 1;\"),n>1&&(s+=\"thisRC.y += 1;\"),o+=`\n ${s}\n ${n>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${n}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${n>0?\"}\":\"\"}\n `}this.userCode=`\n ${e9(t,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?Rc():$c(e)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n ${o}\n\n setOutput(result);\n }\n `}};function e9(r,e){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${e?ER([\"r\",\"c\",\"d\"],\"inputShape\"):Ws([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var dh=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,o){let n=RD(t,o),s=DD(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=$D(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].pop();return this.usedTextures[s].push(p),p}let i;return n===er.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===er.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===er.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===er.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===er.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=RD(o,n),a=DD(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=$D(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=A().getNumber(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u&&u.indexOf(e);if(c==null||c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u[c]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function t9(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function $D(r,e,t,o,n){let s=r9(e,o),a;if(n){let[p,u]=Ma(r[0],r[1]);a=p*u}else{let[p,u]=gp(r[0],r[1]);a=p*u}let i=t9(t,s);return a*i}function r9(r,e){switch(r){case er.PACKED_2X2_FLOAT32:return ih(e);case er.PACKED_2X2_FLOAT16:return uh(e);case er.UNPACKED_FLOAT32:return nh(e);case er.UNPACKED_FLOAT16:return sh(e);case er.PACKED_4X1_UNSIGNED_BYTE:return ah(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function o9(r){return A().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?er.PACKED_2X2_FLOAT32:er.UNPACKED_FLOAT32:r?er.PACKED_2X2_FLOAT16:er.UNPACKED_FLOAT16}function RD(r,e){if(r===mr.UPLOAD)return er.PACKED_2X2_FLOAT32;if(r===mr.RENDER||r==null)return o9(e);if(r===mr.DOWNLOAD||r===mr.PIXELS)return er.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function DD(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var tr=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${t}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},Wt=\"if (isnan(x)) return x;\",AD=\"return x;\",fv=\"return abs(x);\";var FD=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",PD=Wt+`\n return (x < 0.0) ? 0.0 : x;\n`,OD=Wt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,La=\"return x;\",MD=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var BD=\"return x;\",zD=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,VD=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,WD=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,UD=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Fr=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${t}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var fh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let t=e.length,o=Rt(\"rc\",t),n=Re(t),s=ED(t,o),a=o.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n void main() {\n ${n} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${i}));\n }\n `}};var s9=Vt.whereImpl,a9=1e-7,i9=1e-4,hh={};function u9(r){return r in hh||(hh[r]={}),hh[r]}var p9=A().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),c9=600;function l9(){return A().global.screen==null?1024:A().global.screen.height*A().global.screen.width*window.devicePixelRatio*c9/1024/1024}var Lc=class r extends ao{nextDataId(){return r.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!A().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof bp)t=e;else{let o=Kr(A().getNumber(\"WEBGL_VERSION\"),e);t=new bp(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Kr(A().getNumber(\"WEBGL_VERSION\"));t=new bp(o),this.binaryCache=u9(A().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new dh(this.gpgpu),this.numMBBeforeWarning=l9(),this.texData=new Bo(this,ur())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=_c(t),c=new Zl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((A().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||A().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:mr.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(A().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:o,dtype:n,values:t,usage:mr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Fr(i,La):m=new tr(i,La);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n===\"string\")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n===\"complex64\"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=w.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Fr(n,La):f=new tr(n,La);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(A().getBool(\"DEBUG\")&&!A().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&A().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,c;if(a!==\"complex64\"&&A().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...jl(n))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let l;if(a===\"complex64\"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=w.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ur().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;p?d=new Fr(s,La):d=new tr(s,La);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(e,t.customTexShape),l=ur().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=p9){return A().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return ur().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new fh(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new mh(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){let o=[gi(e.shape),...xi(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[gi(t),...xi(t)],a=new Mc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=_c(s),p;n?p=new eh(i):p=new Jf(i);let u=!0,c=[t!=null?t:jl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===gu.DENSE){let x=a!=null?a:jl(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=A().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!xu(b.shape,x.shape)){let C=x,S=x.shape;x.shape=b.shape,x=this.packedReshape(x,S),u.push(x),b=this.texData.get(x.dataId),C.shape=S}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=MR(e,c,l),d=this.getAndSaveBinary(m,()=>PR(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),A().get(\"ENGINE_COMPILE_ONLY\")||OR(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=A().getNumber(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!A().getBool(\"WEBGL_LAZILY_UNPACK\")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(A().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!A().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=A().getBool(\"DEBUG\");A().set(\"DEBUG\",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(A().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?a9:i9}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=WI(o,p),t.texShape=l),s!=null){let m=_c(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Ma(l[0],l[1])),p?d=new oh(m,g):d=new Zl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=mr.PIXELS:C.usage=mr.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let S=[[h,f]],_=this.runWebGLProgram(d,[b],n,S,!0),$=this.texData.get(_.dataId);t.texShape=$.texShape,t.isPacked=$.isPacked,t.usage=$.usage,A().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(_.dataId):(t.texture=$.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=m9(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await cS(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(qf(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);let{variablesLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,outShapeLocation:a,outShapeStridesLocation:i,outTexShapeLocation:p}=XI(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.outShapeLocation=a,e.outShapeStridesLocation=i,e.outTexShapeLocation=p}}createTensorFromGPUData(e,t,o){e.channels=e.channels||\"RGBA\";let{texture:n,height:s,width:a,channels:i}=e,p=ur().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let u=p.writeTexture(n,t,o,s,a,i);return ur().makeTensorFromDataId(u,t,o,p)}};Lc.nextDataId=0;function m9(r,e){if(e===\"float32\"||e===\"complex64\")return r;if(e===\"int32\"||e===\"bool\"){let t=e===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew Lc,2);var $at={forceHalfFloat:GD};var Bc=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var Pr=class{constructor(e,t,o){this.variableNames=[\"A\",\"B\"],this.outputShape=w.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${e}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Xr=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var jr=class{constructor(e,t,o,n=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=w.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=ut(s);let a=\"\";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(a=`\n ${Re(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?a+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:a+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let p=Rt(\"coords\",s);this.enableShapeUniforms?a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${e}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${a}\n\n setOutput(result);\n }\n `}};function Dt(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var HD={kernelName:Co,backendName:\"webgl\",kernelFunc:Dt};function Or(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.texData.get(s.dataId),i=Dt({inputs:{x:o},backend:t}),p=Dt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var KD={kernelName:Di,backendName:\"webgl\",kernelFunc:Or};var hv=\"return (a < 0.) ? b * a : a;\",gv=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function f9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),i=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(gv,n.shape,a.shape):new Pr(hv,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],\"float32\");return t.disposeIntermediateTensorInfo(a),p}var qD={kernelName:$n,backendName:\"webgl\",kernelFunc:f9};var xv=\"return (a < 0.) ? b * a : a;\",yv=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function h9(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(yv,o.shape,n.shape):new Pr(xv,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],\"float32\")}var jD={kernelName:rs,backendName:\"webgl\",kernelFunc:h9};var Fo=\"if (isnan(x)) return x;\";function xe({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=A().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&e!=null,c;return u?c=new Fr(a.shape,e):c=new tr(a.shape,r),i.runWebGLProgram(c,[a],p)}}function nt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype===\"complex64\"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[S,k]=C,_={dataId:S.dataId,dtype:S.dtype,shape:p.shape},$={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new Pr(r,p.shape,u.shape);return c.runWebGLProgram(R,[_,$],dt(S.dtype,k.dtype))}),b=Or({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype===\"string\"||u.dtype===\"string\"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype===\"string\"?w.fromUint8ToStringArray(f):f,x=p.dtype===\"string\"?w.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),S=c.makeTensorInfo(C,l),k=c.texData.get(S.dataId);return k.values=b,S}let m=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&e!=null,d;return m?d=new jr(e,p.shape,u.shape,t):d=new Pr(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function yi(r,e=!1){if(r===\"linear\")return e?BD:AD;if(r===\"relu\")return e?VD:PD;if(r===\"elu\")return e?zD:FD;if(r===\"relu6\")return e?WD:OD;if(r===\"prelu\")return e?yv:xv;if(r===\"leakyrelu\")return e?gv:hv;if(r===\"sigmoid\")return e?UD:MD;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var zc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=ut(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?\"i * 2, rc.y\":\"rc.y, i * 2\",d=s?\"rc.z, i * 2\":\"i * 2, rc.z\",f=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";i&&(p?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${i}\n }`:u?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${i}\n }`:g=`vec4 activation(vec4 x) {\n ${i}\n }`,x=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let C=\"rc.x\",S=\"rc.x\";e[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!xu(n.shape,p)&&!(c.texture!==null&&xu(c.shape,p))?QD(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var ZD={kernelName:da,backendName:\"webgl\",kernelFunc:te};var rm=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c=\"\";s%o>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${i}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${u}\n }\n\n int inIdx = inOffset + ${i};\n if (${p===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${u}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${u}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${u}\n }\n setOutput(sumValue);\n }\n `}};var gh=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=\"0.0\",p=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",p=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",p=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let c=Math.floor(o/4)*4,l=o%4,m=`\n if (${t===\"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${t===\"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${p}(values, minMaxValue);\n if (${t===\"min\"} || ${t===\"max\"}) {\n minMaxValue = ${p}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,d=\"vec4\";t===\"all\"?(i=\"1.0\",m=`\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",m=`\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n `,d=\"bvec4\");let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${i};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n vec4 minMaxValue = vec4(${i});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===2}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===3}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function x9(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=w.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Yr(r,e,t,o){let n=x9(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Re(this.rank),s=dv(\"rc\",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=te({inputs:{x:r},backend:n,attrs:{shape:k}}),R=te({inputs:{x:e},backend:n,attrs:{shape:_}}),D=[$,R],P=Math.max(x,b),O=t?$.shape[1]:$.shape[2],M=s!=null,L=a!=null,B=p===\"leakyrelu\",z=p!=null?yi(p,!0):null,U=M||L||B||z!=null,j;if((d===1||f===1)&&O>Cv&&U===!1){let Y=$,J=R;t&&(Y=bt({inputs:{x:$},backend:n,attrs:{perm:[0,2,1]}}),D.push(Y)),o&&(J=bt({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),D.push(J));let re=f!==1,ne=f===1,ee=Y;re&&(ee=te({inputs:{x:Y},backend:n,attrs:{shape:[P,O,1]}}),D.push(ee));let oe=f===1?2:1,ie=J;ne&&(ie=te({inputs:{x:J},backend:n,attrs:{shape:[P,1,O]}}),D.push(ie));let le=tm({inputs:{a:ee,b:ie},backend:n});j=wp({inputs:{x:le},backend:n,attrs:{axis:oe,keepDims:!0}}),D.push(le)}else{let Y=dt(r.dtype,e.dtype),J=new zc(k,_,[P,d,f],t,o,M,z,L,B),re=[$,R];if(s!=null&&re.push(s),L&&re.push(a),B){let ne=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));re.push(ne),D.push(ne)}j=n.runWebGLProgram(J,re,Y)}let q=te({inputs:{x:j},backend:n,attrs:{shape:S}});D.push(j);for(let Y of D)n.disposeIntermediateTensorInfo(Y);return q}function b9(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Sp({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var rA={kernelName:So,backendName:\"webgl\",kernelFunc:b9};var oA=\"return abs(x);\";function C9(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!==\"complex64\"){let s=t.texData.get(o.dataId),a=ch(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return A().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?n=new Fr(o.shape,oA):n=new tr(o.shape,oA),t.runWebGLProgram(n,[o],o.dtype)}var nA={kernelName:Xs,backendName:\"webgl\",kernelFunc:C9};var w9=Wt+`\n if 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o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n vec4 result = ${n};\n setOutput(result);\n }\n `}};function wh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Dt({inputs:{x:o[0]},backend:t});if(o.length>A().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let p=Math.floor(o.length/2),u=wh({inputs:o.slice(0,p),backend:t}),c=wh({inputs:o.slice(p),backend:t});return wh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=A().getBool(\"WEBGL_PACK\")?new Ch(o[0].shape,s):new bh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var pA={kernelName:Uo,backendName:\"webgl\",kernelFunc:wh};function N9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims(\"all\",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,\"all\",t),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var cA={kernelName:Go,backendName:\"webgl\",kernelFunc:N9};function 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i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${n}; i++) {\n int inIdx = ${p};\n float candidate = getA(batch, inIdx);\n if (candidate ${i} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var Ih=class{constructor(e,t,o,n){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,p=i.length,u=Re(p),c=Rt(\"coords\",p),l,m;if(a===1){m=p+1;let R=Re(m);l=`\n ${R} sourceLocR = 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n=[t];if(w.assertAxesAreInnerMostDims(\"arg\"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!A().getBool(\"WEBGL_PACK_REDUCE\")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,c]=w.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=mA(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return dA(r,e,o)}function _9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=vh(t,p,a[0],\"max\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var fA={kernelName:Ys,backendName:\"webgl\",kernelFunc:_9};function E9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=vh(t,p,a[0],\"min\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var hA={kernelName:Qs,backendName:\"webgl\",kernelFunc:E9};var $9=Wt+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,R9=xe({opSnippet:$9}),gA={kernelName:Ko,backendName:\"webgl\",kernelFunc:R9};var D9=Wt+\"return log(x + sqrt(x * x + 1.0));\",A9=xe({opSnippet:D9}),xA={kernelName:qo,backendName:\"webgl\",kernelFunc:A9};var F9=Wt+`\n return atan(x);\n`,P9=xe({opSnippet:F9}),yA={kernelName:jo,backendName:\"webgl\",kernelFunc:P9};var O9=Bc+`\n return atan(a, b);\n`,M9=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+Xr+`\n return result;\n`,L9=nt({opSnippet:O9,packedOpSnippet:M9}),bA={kernelName:Yo,backendName:\"webgl\",kernelFunc:L9};var B9=Wt+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelName:Xo,backendName:\"webgl\",kernelFunc:z9};var Us=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t===\"avg\",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),o){let R=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${R} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let C=\"max\",S=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(S=\"avgValue / max(count, 1.0)\");let k=Math.floor(a/4)*4,_=a%4,$=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${C}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${k}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${$}\n }\n\n int xC = xCCorner + ${k};\n if (${_===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${$}\n } else if (${_===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${$}\n } else if (${_===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${$}\n }\n }\n setOutput(${S});\n }\n `}},bu=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t===\"avg\",S=\"0.0\";if(C||(S=\"-1.0 / 1e-20\"),o){let P=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${P} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${f} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let k=\"max\",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(_=\"avgValue / max(count, 1.0)\");let $=Math.floor(a/4)*4,R=a%4,D=`\n if (${C}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${k}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${S};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${S});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${$}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${D}\n }\n\n int xC = xCCorner + ${$};\n if (${R===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${D}\n }\n }\n }\n setOutput(${_});\n }\n `}};function V9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Vs(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Us(c,\"avg\",!1);return t.runWebGLProgram(l,[n],\"float32\")}var wA={kernelName:Qo,backendName:\"webgl\",kernelFunc:V9};function W9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new bu(l,\"avg\",!1);return t.runWebGLProgram(m,[n],\"float32\")}var SA={kernelName:Zs,backendName:\"webgl\",kernelFunc:W9};var kh=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${l});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${p};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC+= ${i}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},Nh=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=l-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`\n const ivec3 pads = ivec3(${f}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${l};\n wD += ${p}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${u}) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${d};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function U9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Nh(m);return t.runWebGLProgram(d,[n],a.dtype)}var IA={kernelName:Ri,backendName:\"webgl\",kernelFunc:U9};function G9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Vs([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new kh(c);return t.runWebGLProgram(l,[n],a.dtype)}var vA={kernelName:$i,backendName:\"webgl\",kernelFunc:G9};function H9(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Sp({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var kA={kernelName:Zo,backendName:\"webgl\",kernelFunc:H9};var Th=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],w.assertAndGetBroadcastShape(e,t),w.assertAndGetBroadcastShape(e,o);let i=\"0.0\";n!=null&&(w.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"1.0\";s!=null&&(w.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${i};\n float scale = ${p};\n float inv = scale * inversesqrt(variance + float(${a}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var _h=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],w.assertAndGetBroadcastShape(e,t),w.assertAndGetBroadcastShape(e,o);let i=\"vec4(0.0)\";n!=null&&(w.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"vec4(1.0)\";s!=null&&(w.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n vec4 offset = ${i};\n vec4 scale = ${p};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${a}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var K9=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||n.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(i==null||n.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=A().getBool(\"WEBGL_PACK_NORMALIZATION\")?new _h(o.shape,n.shape,s.shape,c,l,p):new Th(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},NA={kernelName:In,backendName:\"webgl\",kernelFunc:K9};var Eh=class{constructor(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;let t=Re(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let o=q9(this.rank),n,s=e.map((a,i)=>`sourceLoc.${wv[i]} = start[${i}] + coords.${wv[i]};`);n=`\n ${t} sourceLoc;\n ${t} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${n}\n setOutput(getSource(${o}));\n }\n `}},wv=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function q9(r){if(r===1)return\"sourceLoc\";if(r<=6)return wv.slice(0,r).map(e=>\"sourceLoc.\"+e).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var $h=class{constructor(e){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let t=Re(this.rank),o=Rt(\"coords\",this.rank),n=Rt(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`\n result.x = ${a};\n if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.y = ${a};\n --${n[this.rank-1]};\n }\n `,p=this.rank===1?\"\":`\n --${o[this.rank-1]};\n if (++${o[this.rank-2]} < ${e[this.rank-2]}) {\n ++${n[this.rank-2]};\n result.z = ${a};\n if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.w = ${a};\n }\n }\n `,u=this.rank<=4?`sourceLoc = coords +\n ${t}(${e.map((c,l)=>`start[${l}]`).join()});`:e.map((c,l)=>`${n[l]} = ${o[l]} + start[${l}];`).join(`\n`);this.userCode=`\n void main() {\n ${t} coords = getOutputCoords();\n ${t} sourceLoc;\n ${u}\n vec4 result = vec4(0.);\n ${i}\n ${p}\n setOutput(result);\n }\n `}};function j9(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=pt.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function Gs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=pt.parseSliceParams(n,s,a);if(pt.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.texData.get(n.dataId),m=gD(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=pt.isSliceContinous(n.shape,i,p);if(u||!c){let l=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new $h(p):new Eh(p),m=[i];return t.runWebGLProgram(l,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),j9(n,i,p,t)}var TA={kernelName:ha,backendName:\"webgl\",kernelFunc:Gs};var X9=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:t,attrs:{shape:p}}),h=bt({inputs:{x:f},backend:t,attrs:{perm:u}}),g=te({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Gs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},_A={kernelName:Js,backendName:\"webgl\",kernelFunc:X9};function Y9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),p=t.readSync(s.dataId),u=ph(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var EA={kernelName:Jo,backendName:\"webgl\",kernelFunc:Y9};var Q9=`\n int r = int(a.r) & int(b.r);\n int g = int(a.g) & int(b.g);\n int rb = int(a.b) & int(b.b);\n int ra = int(a.a) & int(b.a);\n return vec4(r, g, rb, ra);\n`,Z9=`\n return float(int(a.r) & int(b.r));\n`;function J9(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"),a=A().getNumber(\"WEBGL_VERSION\");if(t.shouldExecuteOnCPU([o,n])||a===1){let p=t.texData.get(o.dataId).values,u=t.texData.get(n.dataId).values,[c,l]=zR(o.shape,n.shape,p,u,o.dtype),m=t.makeTensorInfo(l,o.dtype),d=t.texData.get(m.dataId);return d.values=c,m}let i;return s?i=new jr(Q9,o.shape,n.shape,!1):i=new Pr(Z9,o.shape,n.shape),t.runWebGLProgram(i,[o,n],o.dtype)}var $A={kernelName:qa,backendName:\"webgl\",kernelFunc:J9};function eJ(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.readSync(o.dataId),a=t.readSync(n.dataId),i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],\"int32\",Int32Array.from(i))}var RA={kernelName:ea,backendName:\"webgl\",kernelFunc:eJ};var tJ=\"return float(a != b);\",Sv=nt({opSnippet:tJ,cpuKernelImpl:iD,dtype:\"bool\"}),DA={kernelName:Yn,backendName:\"webgl\",kernelFunc:Sv};function bi(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.real},backend:t})}var AA={kernelName:Hi,backendName:\"webgl\",kernelFunc:bi};var rJ=\"return float(int(x));\";function FA(r,e){let t=new tr(r.shape,rJ),o=e.runWebGLProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Iv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return Dt({inputs:{x:n},backend:t});let a=Gr(n.shape),i=Iv({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=Or({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),p}if(n.dtype===\"complex64\"){let a=bi({inputs:{input:n},backend:t}),i=Iv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Dt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.texData.get(n.dataId).values,[i,p,u]=VR(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return FA(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=Sv({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var PA={kernelName:yo,backendName:\"webgl\",kernelFunc:Iv};var OA=\"return ceil(x);\",oJ=xe({opSnippet:OA,packedOpSnippet:OA,cpuKernelImpl:WR}),MA={kernelName:en,backendName:\"webgl\",kernelFunc:oJ};var Rh=class{constructor(e){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var Dh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function nJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;A().getBool(\"WEBGL_PACK_CLIP\")?i=new Dh(n.shape):i=new Rh(n.shape);let p=[[s],[a]];return t.runWebGLProgram(i,[n],n.dtype,p)}var LA={kernelName:bo,backendName:\"webgl\",kernelFunc:nJ};var Ah=class{constructor(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). 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1;\n if (${a[n-2]} < ${o[n-2]} &&\n ${a[n-1]} < ${o[n-1]}) {\n result.b = getValue(${a});\n }\n setOutput(result);\n }\n `}};function Ph(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function Ip(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var VA={kernelName:Wi,backendName:\"webgl\",kernelFunc:Ip};function Vc(r,e,t){let o=r[0].dtype;if(o===\"complex64\"){let d=r.map(b=>bi({inputs:{input:b},backend:t})),f=r.map(b=>Ip({inputs:{input:b},backend:t})),h=Vc(d,e,t),g=Vc(f,e,t),x=Or({inputs:{real:h,imag:g},backend:t});return d.forEach(b=>t.disposeIntermediateTensorInfo(b)),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),x}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let d=r.map(S=>{let _=[-1,y.sizeFromShape(S.shape.slice(e))];return te({inputs:{x:S},backend:t,attrs:{shape:_}})}),f=d.map(S=>({vals:t.readSync(S.dataId),shape:S.shape})),h=w.computeOutShape(d.map(S=>S.shape),1),g=d[0].shape[0]===1,x=UR(f,h,o,g),b=w.computeOutShape(r.map(S=>S.shape),e),C=t.makeTensorInfo(b,o,x);return d.forEach(S=>t.disposeIntermediateTensorInfo(S)),C}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let d=a?new tr(r[0].shape,La):new Fr(r[0].shape,La);return t.runWebGLProgram(d,r,o)}let i=A().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>i){let d=[];for(let h=0;hf.shape),e);return t.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=aJ(s,e,t),c=new Fh(p.map(d=>d.shape)),l=t.runWebGLProgram(c,p,o);p.forEach(d=>t.disposeIntermediateTensorInfo(d));let m=te({inputs:{x:l},attrs:{shape:u},backend:t});return t.disposeIntermediateTensorInfo(l),m}function aJ(r,e,t){let o=w.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>te({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function vv(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Dt({inputs:{x:p[0]},backend:t}):Vc(p,s,t)}var WA={kernelName:ta,backendName:\"webgl\",kernelFunc:vv};var Wc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,p=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,C=g?3:1,S=\"\",k=\"\";o&&(n?S=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?S=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:S=`\n float activation(float x) {\n ${o}\n }\n `,k=\"result = activation(result);\");let _=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${S}\n\n const ivec2 strides = ivec2(${p}, ${u});\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${C}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${l};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${f}) *\n getW(wR, wC, ${f}, d2);\n } else {\n dotProd +=\n getX(batch, ${f}, xR, xC) *\n getW(wR, wC, ${f}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2),\n getW(wR, wC, ${f} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1),\n getX(batch, xR, xC, ${f} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC),\n getX(batch, ${f} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${k}\n setOutput(result);\n }\n `}},Mh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${a}, ${i});\n const ivec3 pads = ivec3(${t}, ${o}, ${n});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${l}; wF++) {\n int xF = xFCorner + wF * ${p};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${f}) *\n getW(wF, wR, wC, ${f}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1),\n getX(batch, xF, xR, xC, ${f} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2),\n getW(wF, wR, wC, ${f} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var Uc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*p};\n `,i===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,p===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,p>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${i}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+l}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+l}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${u}\n\n ${n.output} = result;\n }\n `}};function Bh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function zh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat===\"channelsLast\",f=!1,h=!1,g,x=[];if(s!=null){let S=Bh(s.shape,d);S!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:S}}),x.push(s))}if(n!=null){let S=Bh(n.shape,d);S!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:S}}),x.push(n))}if(!((l===1||m===1)&&c>Cv)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let S=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,S,t.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(xu(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let $=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push($);let R=Sp({a:k,b:$,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),D=o.texData.get(R.dataId);y.assert(D.isPacked,()=>\"batchMatMul result is expected to be packed\"),u.shape=_,D.shape=t.outShape,g=Dt({inputs:{x:R},backend:o}),g.shape=t.outShape,x.push(R)}else{let S=t.outHeight*t.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,S,t.inChannels]:[t.batchSize,t.inChannels,S]}}),_=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),$=Sp({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:$},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(_),x.push($)}for(let S of x)o.disposeIntermediateTensorInfo(S);return g}function Vh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=t,f=d===\"channelsLast\",h=p*u*c,g=m*l,x=[t.batchSize,h,g],b=!0,C=!1,S=[];if(s!=null){let q=Bh(s.shape,f);q!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:q}}),S.push(s))}if(n!=null){let q=Bh(n.shape,f);q!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:q}}),S.push(n))}let k=te({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});S.push(k);let _=new Lh(x,t),$=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],R=o.runWebGLProgram(_,[r],\"float32\",$),D=te({inputs:{x:R},backend:o,attrs:{shape:x}});S.push(R),S.push(D);let P=n!=null,O=s!=null,M=i===\"leakyrelu\",L=i?yi(i,!0):null,B=new zc(f?D.shape:k.shape,f?k.shape:D.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,C,P,L,O,M),z=f?[D,k]:[k,D];if(n&&z.push(n),O&&z.push(s),M){let q=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));z.push(q),S.push(q)}let U=o.runWebGLProgram(B,z,\"float32\"),j=te({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});S.push(U);for(let q of S)o.disposeIntermediateTensorInfo(q);return j}function iJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))d=zh({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l===\"channelsLast\"&&A().getBool(\"WEBGL_EXP_CONV\")){let h=new Uc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],\"float32\",g)}else if(A().getBool(\"WEBGL_CONV_IM2COL\"))d=Vh({x:n,filter:s,convInfo:m,backend:t});else{let h=new Wc(m);d=t.runWebGLProgram(h,[n,s],\"float32\")}let f=te({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var UA={kernelName:tn,backendName:\"webgl\",kernelFunc:iJ};var Wh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n ${a?`float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);`}\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Uh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,p=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${l}];\n\n ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n if (${a}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Gh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yF = 0; yF < ${e.outDepth}; yF++) {\n int xF = wF + yF * ${t} - ${s};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${o} - ${a};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${i};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Hh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${u}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${t}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${t} - 1 - wF;\n\n for (int wR = 0; wR < ${o}; wR++) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${o} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function uJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Wh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var GA={kernelName:Fi,backendName:\"webgl\",kernelFunc:uJ};var Kh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"strides\",type:\"vec2\"}],this.outputShape=e.inShape,this.enableShapeUniforms=ut(this.outputShape.length);let t=e.filterHeight,o=e.filterWidth,n=t-1-e.padInfo.top,s=o-1-e.padInfo.left;this.userCode=`\n const ivec2 pads = ivec2(${n}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n vec4 result = vec4(0.);\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / strides[0];\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n int wCPerm = ${o} - 1 - wC;\n\n float dyC = float(dyCCorner + wC) / strides[1];\n bool idyCVal = (dyC >= 0.0) && (dyC < ${e.outWidth}.0)\n && (fract(dyC) == 0.0);\n int idyC = int(dyC);\n\n float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${e.outWidth}.0)\n && (fract(dyC2) == 0.0);\n int idyC2 = int(dyC2);\n\n if (idyCVal && idyCVal2) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n dySample : getDy(batch, idyR, idyC2, d2);\n\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n\n dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample2.xy : dySample2.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal2) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC2, d2);\n vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n }\n }\n }\n setOutput(result);\n }\n `}};function pJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l);if(A().getBool(\"WEBGL_PACK_CONV2DTRANSPOSE\")&&l===\"channelsLast\"){let d=[[m.strideHeight,m.strideWidth]],f=new Kh(m);return t.runWebGLProgram(f,[n,s],\"float32\",d)}else{let d=new Uh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}}var HA={kernelName:rn,backendName:\"webgl\",kernelFunc:pJ};function cJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Mh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var KA={kernelName:on,backendName:\"webgl\",kernelFunc:cJ};function lJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Gh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var qA={kernelName:ja,backendName:\"webgl\",kernelFunc:lJ};function mJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,i,1,a),c=new Hh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var jA={kernelName:nn,backendName:\"webgl\",kernelFunc:mJ};var dJ=Fo+`\n return cos(x);\n`,fJ=`\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${Xr}\n return result;\n`,hJ=xe({opSnippet:dJ,packedOpSnippet:fJ}),XA={kernelName:sn,backendName:\"webgl\",kernelFunc:hJ};var gJ=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,xJ=xe({opSnippet:gJ}),YA={kernelName:an,backendName:\"webgl\",kernelFunc:xJ};var qh=class{constructor(e,t,o,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n===\"bilinear\"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,\"(y2-y1) * height_ratio\",`y1*${f} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${f}`],[C,S,k]=m>1?[`${(p-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${C});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${a}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${S};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${f} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${k};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${d} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var yJ=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new qh(n.shape,s.shape,i,p,u);return t.runWebGLProgram(c,[n,s,a],\"float32\")},QA={kernelName:cn,backendName:\"webgl\",kernelFunc:yJ};var vp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(vp||(vp={}));var om=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,a=this.op===vp.Prod?\"1.0\":\"0.0\",i=o?a:`getX(${ZA(s,\"coords\",this.op)})`,p=this.outputShape[this.outputShape.length-1],u=\"\",c=\"\";o?(u=n?`end != ${p-1}`:\"end != 0\",c=n?\"end + 1\":\"end - 1\"):(u=n?`end + pow2 < ${p}`:\"end >= pow2\",c=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${Re(s)} coords = getOutputCoords();\n int end = ${JA(s,\"coords\",this.op)};\n float val = ${i};\n int pow2 = int(pow(2.0, index));\n if (${u}) {\n int idx = ${c};\n ${JA(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${ZA(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function ZA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function JA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function jh(r,e,t,o,n,s){let a=e.shape.length,i=w.getAxesPermutation([o],a),p=e;i!=null&&(p=bt({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Dt({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new om(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new om(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=bt({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function bJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return jh(vp.Prod,n,t,s,a,i)}var eF={kernelName:un,backendName:\"webgl\",kernelFunc:bJ};function CJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return jh(vp.Sum,n,t,s,a,i)}var tF={kernelName:pn,backendName:\"webgl\",kernelFunc:CJ};function wJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=ph(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=BR(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var rF={kernelName:ra,backendName:\"webgl\",kernelFunc:wJ};var Xh=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${t};\n int offset_h = imod(h, ${t});\n int in_w = w / ${t};\n int offset_w = imod(w, ${t});\n int offset_d = (offset_h * ${t} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function SJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=new Xh(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var oF={kernelName:ln,backendName:\"webgl\",kernelFunc:SJ};var Gc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u=\"\",c=\"\";o&&(n?u=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?u=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:u=`\n float activation(float x) {\n ${o}\n }\n `,c=\"result = activation(result);\");let l=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${u}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${p};\n int q = d2 - d1 * ${p};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${a}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${i}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${l}\n ${c}\n setOutput(result);\n }\n `}};var Hc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=`\n xC = xCCorner + ${b*u};\n `,p===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,u===1&&b>0?d+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:d+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):d+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,u>1?d+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:d+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):C===1?d+=`\n xC${b+1} = xTexelC${b};\n `:d+=`\n xCOffset = xC + ${C};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(d+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=w.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;A().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new Hc(l):m=new Gc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],\"float32\",d)}var nF={kernelName:mn,backendName:\"webgl\",kernelFunc:IJ};var Yh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${a} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Qh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${p}; dm++) {\n int d2 = d1 * ${p} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function vJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Yh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var sF={kernelName:Pi,backendName:\"webgl\",kernelFunc:vJ};function kJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Qh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var aF={kernelName:Oi,backendName:\"webgl\",kernelFunc:kJ};var Zh=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function NJ(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Zh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var iF={kernelName:oa,backendName:\"webgl\",kernelFunc:NJ};var Jh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${a});\n const ivec2 pads = ivec2(${l}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${i}; h++) {\n int hIn = hBeg + h * ${u};\n\n if (hIn >= 0 && hIn < ${t}) {\n for (int w = 0; w < ${p}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${o}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function TJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c,l=new Jh(u);c=t.runWebGLProgram(l,[n,s],\"float32\");let m=te({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var uF={kernelName:dn,backendName:\"webgl\",kernelFunc:TJ};function _J(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=wp({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var pF={kernelName:Bi,backendName:\"webgl\",kernelFunc:_J};var EJ=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",$J=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,RJ=xe({opSnippet:EJ,packedOpSnippet:$J}),cF={kernelName:hn,backendName:\"webgl\",kernelFunc:RJ};var DJ=\"return (b >= 0.0) ? a : a * (b + 1.0);\",AJ=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,FJ=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(AJ,o.shape,n.shape):new Pr(DJ,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},lF={kernelName:Xa,backendName:\"webgl\",kernelFunc:FJ};var PJ=`\n return vec4(equal(a, b));\n`,OJ=\"return float(a == b);\",MJ=nt({opSnippet:OJ,packedOpSnippet:PJ,dtype:\"bool\",cpuKernelImpl:GR}),mF={kernelName:xn,backendName:\"webgl\",kernelFunc:MJ};var LJ=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${w.ERF_P};\n float a1 = ${w.ERF_A1};\n float a2 = ${w.ERF_A2};\n float a3 = ${w.ERF_A3};\n float a4 = ${w.ERF_A4};\n float a5 = ${w.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,BJ=xe({opSnippet:LJ}),dF={kernelName:gn,backendName:\"webgl\",kernelFunc:BJ};var zJ=Fo+`\n return exp(x);\n`,VJ=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,kv=xe({opSnippet:zJ,packedOpSnippet:VJ,cpuKernelImpl:HR,dtype:\"float32\"}),fF={kernelName:yn,backendName:\"webgl\",kernelFunc:kv};function eg(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var hF={kernelName:na,backendName:\"webgl\",kernelFunc:eg};var gF=\"return exp(x) - 1.0;\",WJ=xe({opSnippet:gF,packedOpSnippet:gF,cpuKernelImpl:KR}),xF={kernelName:bn,backendName:\"webgl\",kernelFunc:WJ};var nm=class{constructor(e,t,o){this.variableNames=[\"real\",\"imag\"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:\"1.0\",i;if(e===\"real\")i=\"return real * expR - imag * expI;\";else if(e===\"imag\")i=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${e}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${i}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${n});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${n}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${a};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function tg(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new nm(\"real\",p,e),c=new nm(\"imag\",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,\"float32\"),d=t.runWebGLProgram(c,l,\"float32\"),f=Or({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function UJ(r){let{inputs:e,backend:t}=r,{input:o}=e;return tg(o,!1,t)}var yF={kernelName:zi,backendName:\"webgl\",kernelFunc:UJ};var rg=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=e,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Ci(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new rg(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var bF={kernelName:sa,backendName:\"webgl\",kernelFunc:Ci};var og=class{constructor(e){this.variableNames=[\"Image\"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${t} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${t}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var CF={kernelName:Cn,backendName:\"webgl\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new og(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var wF=\"return floor(x);\",GJ=xe({opSnippet:wF,packedOpSnippet:wF,cpuKernelImpl:qR}),SF={kernelName:wn,backendName:\"webgl\",kernelFunc:GJ};var HJ=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,KJ=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,qJ=nt({opSnippet:HJ,packedOpSnippet:KJ,dtype:\"int32\"}),IF={kernelName:Sn,backendName:\"webgl\",kernelFunc:qJ};var ng=class{constructor(e){this.variableNames=[\"A\"];let t=It(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);\n\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var sg=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let t=It(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${n}.0, ${o}.0);\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${t.output} = result;\n }\n `}};var vF={kernelName:Du,backendName:\"webgl\",kernelFunc:jJ},Kc,Nv=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function jJ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Kc==null||h!==Nv)&&(Nv=h,Kc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:Nv})),Kc.canvas.width=p,Kc.canvas.height=u,Kc.drawImage(n,0,0,p,u),n=Kc.canvas}let m=t.makeTensorInfo(c,\"int32\");t.texData.get(m.dataId).usage=mr.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let d=A().getBool(\"WEBGL_PACK\")?new sg(l):new ng(l),f=t.runWebGLProgram(d,[m],\"int32\");return t.disposeData(m.dataId),f}function XJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],C=a!=null,S=i!=null,k=d===\"leakyrelu\",_=()=>{let R=[n,s],D=(P,O)=>{if(O===\"NCHW\"&&P.shape.length===1&&P.shape[0]!==1){let M=te({inputs:{x:P},backend:t,attrs:{shape:[P.shape[0],1,1]}});return b.push(M),M}return P};if(C&&R.push(D(a,c)),S&&R.push(D(i,c)),k){let P=t.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));R.push(P),b.push(P)}return R};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=zh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h===\"channelsLast\"&&A().getBool(\"WEBGL_EXP_CONV\")){let R=d?yi(d,!0):null,D=new Uc(g,C,R,S,k),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],O=_();x=t.runWebGLProgram(D,O,\"float32\",P)}else if(A().getBool(\"WEBGL_CONV_IM2COL\"))x=Vh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let R=d?yi(d,!1):null,D=new Wc(g,C,R,S,k),P=_();x=t.runWebGLProgram(D,P,\"float32\")}let $=te({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(R=>t.disposeIntermediateTensorInfo(R)),$}var kF={kernelName:Io,backendName:\"webgl\",kernelFunc:XJ};function YJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${a}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function QJ(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=te({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=te({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let x=t.readSync(n.dataId),b=t.bufferSync(o),C=jR(x,b,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,C.values)}let f=new ag(a,l,[u,c],o.shape),h=t.runWebGLProgram(f,[d,m],d.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:p}});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var TF={kernelName:vn,backendName:\"webgl\",kernelFunc:QJ};var ig=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let o=Re(this.rank),n=ZJ(e,2);this.userCode=`\n void main() {\n ${o} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${n}));\n }\n `}};function ZJ(r,e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=0,()=>`GatherV2: the index value ${k} is not in [0, ${C-1}]`)}}let u=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=te({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=te({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype===\"string\"){let b=t.bufferSync(d),C=t.bufferSync(m),S=XR(C,b,f);return l.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,S.dtype,S.values)}let h=new ig(m.shape,f),g=t.runWebGLProgram(h,[m,d],m.dtype);l.push(g);let x=te({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var _F={kernelName:aa,backendName:\"webgl\",kernelFunc:Tv};var JJ=\"return float(a > b);\",eee=`\n return vec4(greaterThan(a, b));\n`,tee=nt({opSnippet:JJ,packedOpSnippet:eee,cpuKernelImpl:YR,dtype:\"bool\"}),EF={kernelName:kn,backendName:\"webgl\",kernelFunc:tee};var ree=\"return float(a >= b);\",oee=`\n return vec4(greaterThanEqual(a, b));\n`,nee=nt({opSnippet:ree,packedOpSnippet:oee,dtype:\"bool\",cpuKernelImpl:QR}),$F={kernelName:Nn,backendName:\"webgl\",kernelFunc:nee};function see(r){let{inputs:e,backend:t}=r,{input:o}=e;return tg(o,!0,t)}var RF={kernelName:Vi,backendName:\"webgl\",kernelFunc:see};var aee=\"return float(!isnan(x) && !isinf(x));\",iee=xe({opSnippet:aee,dtype:\"bool\"}),DF={kernelName:Tn,backendName:\"webgl\",kernelFunc:iee};var uee=\"return float(isinf(x));\",pee=xe({opSnippet:uee,dtype:\"bool\"}),AF={kernelName:_n,backendName:\"webgl\",kernelFunc:pee};var cee=\"return float(isnan(x));\",lee=xe({opSnippet:cee,dtype:\"bool\"}),FF={kernelName:En,backendName:\"webgl\",kernelFunc:lee};var mee=\"return float(a < b);\",dee=`\n return vec4(lessThan(a, b));\n`,fee=nt({opSnippet:mee,packedOpSnippet:dee,cpuKernelImpl:ZR,dtype:\"bool\"}),PF={kernelName:Rn,backendName:\"webgl\",kernelFunc:fee};var hee=\"return float(a <= b);\",gee=`\n return vec4(lessThanEqual(a, b));\n`,xee=nt({opSnippet:hee,packedOpSnippet:gee,cpuKernelImpl:JR,dtype:\"bool\"}),OF={kernelName:Dn,backendName:\"webgl\",kernelFunc:xee};function yee(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=eD(o,n,s);return e.makeTensorInfo([a.length],\"float32\",a)}var MF={kernelName:An,backendName:\"webgl\",kernelFunc:yee};var bee=Fo+`\n return x < 0.0 ? 0./0. : log(x);\n`,Cee=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,wee=xe({opSnippet:bee,packedOpSnippet:Cee,cpuKernelImpl:tD}),LF={kernelName:Fn,backendName:\"webgl\",kernelFunc:wee};var See=Fo+`\n return log(1.0 + x);\n`,Iee=xe({opSnippet:See}),BF={kernelName:Pn,backendName:\"webgl\",kernelFunc:Iee};var vee=\"return float(a >= 1.0 && b >= 1.0);\",kee=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,Nee=nt({opSnippet:vee,packedOpSnippet:kee,dtype:\"bool\"}),zF={kernelName:On,backendName:\"webgl\",kernelFunc:Nee};var Tee=\"return float(!(x >= 1.0));\",_ee=xe({opSnippet:Tee}),VF={kernelName:Mn,backendName:\"webgl\",kernelFunc:_ee};var Eee=\"return float(a >= 1.0 || b >= 1.0);\",$ee=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,Ree=nt({opSnippet:Eee,packedOpSnippet:$ee,dtype:\"bool\"}),WF={kernelName:Ln,backendName:\"webgl\",kernelFunc:Ree};var ug=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${a}; j <= ${a}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${i}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${p};\n setOutput(val);\n }\n `}};var pg=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${a};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${a}; j <= ${a}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${p};\n setOutput(result);\n }\n `}};var Dee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=A().getBool(\"WEBGL_PACK_NORMALIZATION\")?new pg(n.shape,s,a,i,p):new ug(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},UF={kernelName:Bn,backendName:\"webgl\",kernelFunc:Dee};var cg=class{constructor(e,t,o,n,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${t})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${t} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${n}) * norm + float(${o});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${n})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var Aee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new cg(n.shape,i,p,u,c);return t.runWebGLProgram(l,[n,s,a],n.dtype)},GF={kernelName:Ya,backendName:\"webgl\",kernelFunc:Aee};function HF(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Yr(i,r.dtype,\"max\",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function _v(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=t.texData.get(d.dataId).values,S=new Array(i);for(let $=0;$`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Us(c,\"max\",!1);return t.runWebGLProgram(l,[n],n.dtype)}var jF={kernelName:Wn,backendName:\"webgl\",kernelFunc:Mee};function Lee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new bu(l,\"max\",!1);return t.runWebGLProgram(m,[n],n.dtype)}var XF={kernelName:ia,backendName:\"webgl\",kernelFunc:Lee};var lg=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,p=a-1-e.padInfo.left,u=s*a-1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${n}) {\n float dyR = float(dyRCorner + wR) / ${t}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${a}; wC++) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${a} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},mg=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=c-1-e.padInfo.left,f=p*u*c-1;this.userCode=`\n const ivec3 pads = ivec3(${l}, ${m}, ${d});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${t}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${u};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${i}) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${f} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${u} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function Bee(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new bu(m,\"max\",!0),f=t.runWebGLProgram(d,[a],a.dtype),h=new mg(m),g=t.runWebGLProgram(h,[n,f],a.dtype);return t.disposeIntermediateTensorInfo(f),g}var YF={kernelName:Gi,backendName:\"webgl\",kernelFunc:Bee};function zee(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Vs([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new Us(m,\"max\",d),h=t.runWebGLProgram(f,[i],i.dtype),g=new lg(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var QF={kernelName:Ui,backendName:\"webgl\",kernelFunc:zee};function ZF(r,e,t,o){let n=new Us(t,\"max\",!1),s=o.runWebGLProgram(n,[r],\"float32\");n=new Us(t,\"max\",!0,!0,e);let a=o.runWebGLProgram(n,[r],\"float32\");return[s,a]}var JF={kernelName:ua,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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yg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Re(n),a=t.map(h=>h[0]).join(\",\"),i=t.map((h,g)=>h[0]+e[g]).join(\",\"),p=Rt(\"rc\",n),u=Rt(\"source\",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?\"source\":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1;\n if(${c}) {\n `,n===1?\"\":`}\n rc = outputLoc;\n ${p[n-2]} += 1;\n if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?\"\":` ${p[n-1]} += 1;\n if(${c}) {`],d=n===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",f=\"\";for(let h=0,g=n===1?2:4;h{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Ci({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new yg(n.shape,s,a):new xg(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},C3={kernelName:es,backendName:\"webgl\",kernelFunc:Dv};var pte=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,cte=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = 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f=w.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var S3={kernelName:os,backendName:\"webgl\",kernelFunc:mte};function dte(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=pD(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],\"int32\",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var I3={kernelName:Hp,backendName:\"webgl\",kernelFunc:dte};function fte(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=cD(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],\"int32\",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var v3={kernelName:Kp,backendName:\"webgl\",kernelFunc:fte};function hte(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=lD(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var k3={kernelName:qp,backendName:\"webgl\",kernelFunc:hte};var Av=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=mD(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},N3={kernelName:ma,backendName:\"webgl\",kernelFunc:Av};var gte=\"return 1.0 / x;\",xte=xe({opSnippet:gte}),T3={kernelName:ns,backendName:\"webgl\",kernelFunc:xte};var yte=Wt+`\n return (x < 0.0) ? 0.0 : x;\n`,bte=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Cte=xe({opSnippet:yte,packedOpSnippet:bte}),_3={kernelName:ss,backendName:\"webgl\",kernelFunc:Cte};var wte=Wt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Ste=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Ite=xe({opSnippet:wte,packedOpSnippet:Ste}),E3={kernelName:us,backendName:\"webgl\",kernelFunc:Ite};var bg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var Cg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function vte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new Cg(n.shape,p,u,s,a):new bg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],\"float32\")}var $3={kernelName:is,backendName:\"webgl\",kernelFunc:vte};var wg=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function kte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new wg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var R3={kernelName:Ja,backendName:\"webgl\",kernelFunc:kte};var Sg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var Ig=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function Nte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new Ig(n.shape,p,u,s,a):new Sg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var D3={kernelName:as,backendName:\"webgl\",kernelFunc:Nte};var vg=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float sourceFracRow =\n float(${p[0]}) *\n (float(dyR) / float(${u[0]}));\n\n float sourceFracCol =\n float(${p[1]}) *\n (float(dyC) / float(${u[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${n}) - 1),\n ${o} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${o} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function Tte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new vg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var A3={kernelName:Za,backendName:\"webgl\",kernelFunc:Tte};var kg=class{constructor(e,t){this.variableNames=[\"x\"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${e[0]} - coord - 1));\n }\n `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,p)=>n(p)).join(\",\"),a=Re(o);this.userCode=`\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var Ng=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Rt(\"rc\",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Re(o);o===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${e[0]} - rc - 1),\n ${e[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),\n ${e[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${i} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${p(n.slice())};\n if(${s}){\n result.g = ${u(n.slice())};\n }\n if(${a}) {\n result.b = ${c(n.slice())};\n if(${s}) {\n result.a = ${l(n.slice())};\n }\n }\n setOutput(result);\n }\n `;function p(f){return m(f)}function u(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",m(f)}function c(f){return f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function l(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function m(f){let h=e.map((b,C)=>d(C,f)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function _te(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return Dt({inputs:{x:n},backend:t});let p=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new Ng(n.shape,i):new kg(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var F3={kernelName:ps,backendName:\"webgl\",kernelFunc:_te};var Tg=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let o=e[1],n=e[2];this.outputShape=e;let s=\"\";typeof t==\"number\"?s=`float outputValue = ${t.toFixed(2)};`:s=`\n vec3 fill = vec3(${t.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var P3={kernelName:Ds,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new Tg(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var Ete=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n`,$te=xe({opSnippet:Ete}),O3={kernelName:cs,backendName:\"webgl\",kernelFunc:$te};var Rte=\"return inversesqrt(x);\",Dte=xe({opSnippet:Rte,cpuKernelImpl:dD}),M3={kernelName:ls,backendName:\"webgl\",kernelFunc:Dte};var Cu=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=t>1?\"strides[j]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${e}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${t}; j++) {\n int index = round(${m});\n flattenedIndex += index * ${x};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${f};\n found = true;\n }\n }\n setOutput(mix(${g}, sum, float(found)));\n }\n `}};var _g=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=t>1?\"strides[j]\":\"strides\",b=t>1?\"strides[j + 1]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n vec4 sum = vec4(0.);\n vec4 found = vec4(0.);\n for (int i = 0; i < ${e}; i+=2) {\n ivec2 flattenedIndex = ivec2(0);\n for (int j = 0; j < ${t}; j+=2) {\n ivec4 index = round(${m});\n flattenedIndex += index.xz * ${x};\n if (j + 1 < ${t}) {\n flattenedIndex += index.yw * ${b};\n }\n }\n if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n vec4 updVals = ${f};\n if (flattenedIndex[0] == coords[0]) {\n sum.xy += updVals.xy;\n found.xy = vec2(1.);\n } else if (flattenedIndex[0] == coords[0] + 1) {\n sum.zw += updVals.xy;\n found.zw = vec2(1.);\n }\n if (flattenedIndex[1] == coords[0]) {\n sum.xy += updVals.zw;\n found.xy = vec2(1.);\n } else if (flattenedIndex[1] == coords[0] + 1) {\n sum.zw += updVals.zw;\n found.zw = vec2(1.);\n }\n }\n }\n setOutput(mix(${g}, sum, found));\n }\n `}};function Ate(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],\"float32\",new Float32Array([0])),g;A().getBool(\"WEBGL_PACK\")?g=new _g(p,i,d.shape.length,f.shape.length,c,m):g=new Cu(p,i,d.shape.length,f.shape.length,c,m);let x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var L3={kernelName:ms,backendName:\"webgl\",kernelFunc:Ate};var Eg=class{constructor(e,t,o,n){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[e,o];let s=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=A().getNumber(\"WEBGL_VERSION\")===2?s:a,p=n===\"left\"?\"<\":\"<=\";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${i}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${p} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}};function Fte(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new Eg(n.shape[0],n.shape[1],s.shape[1],a),p=[[n.shape[1]]];return t.runWebGLProgram(i,[n,s],\"int32\",p)}var B3={kernelName:fs,backendName:\"webgl\",kernelFunc:Fte};var $g=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s=\"resRC\",n=\"resRC\";else{let i=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],p=[],u=[];for(let c=0;c= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}};function Pte(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new $g(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var z3={kernelName:fa,backendName:\"webgl\",kernelFunc:Pte};var Ote=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${w.SELU_SCALEALPHA};\n float scale = ${w.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,Mte=xe({opSnippet:Ote}),V3={kernelName:hs,backendName:\"webgl\",kernelFunc:Mte};var Lte=Fo+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,Bte=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,zte=xe({opSnippet:Lte,packedOpSnippet:Bte,cpuKernelImpl:hD}),W3={kernelName:bs,backendName:\"webgl\",kernelFunc:zte};var Vte=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,Wte=xe({opSnippet:Vte}),U3={kernelName:ys,backendName:\"webgl\",kernelFunc:Wte};var Ute=Fo+`\n return sin(x);\n`,Gte=`\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${Xr}\n return result;\n`,Hte=xe({opSnippet:Ute,packedOpSnippet:Gte}),G3={kernelName:gs,backendName:\"webgl\",kernelFunc:Hte};var Kte=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,qte=xe({opSnippet:Kte}),H3={kernelName:xs,backendName:\"webgl\",kernelFunc:qte};var jte=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`,Xte=xe({opSnippet:jte}),K3={kernelName:Cs,backendName:\"webgl\",kernelFunc:Xte};var Yte=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeIntermediateTensorInfo(x)),g},q3={kernelName:ga,backendName:\"webgl\",kernelFunc:Yte};function Qte(r){let{inputs:e,backend:t}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=e;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(o.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n ${o.shape}`);if(n.shape.length!==1)throw new Error(`Values must be a vector, saw:\n ${n.shape}`);if(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n ${a.shape}`);let i=t.readSync(o.dataId),p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[l,m,d,f,h]=xD(i,o.shape,o.dtype,p,n.dtype,u,c);return[t.makeTensorInfo(m,o.dtype,l),t.makeTensorInfo([m[0]],n.dtype,d),t.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var j3={kernelName:Ki,backendName:\"webgl\",kernelFunc:Qte};function Zte(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,c,l]=yD(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var X3={kernelName:ei,backendName:\"webgl\",kernelFunc:Zte};function Jte(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=lh(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var Y3={kernelName:ya,backendName:\"webgl\",kernelFunc:Jte};function ere(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=lh(a,o.shape,o.dtype,i,p);return t.makeTensorInfo(c,o.dtype,u)}var Q3={kernelName:ba,backendName:\"webgl\",kernelFunc:ere};function tre(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let x=t.bufferSync(n),b=t.bufferSync(s),C=y.decodeString(t.readSync(a.dataId)[0]),S=fD(x,b,i,m,c,u,p,l,C,d);return t.makeTensorInfo(i,S.dtype,S.values)}let f=new Cu(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=t.runWebGLProgram(f,[s,n,a],s.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(h),g}var Z3={kernelName:vs,backendName:\"webgl\",kernelFunc:tre};function rre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Gs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var J3={kernelName:xa,backendName:\"webgl\",kernelFunc:rre};var eP=\"return sqrt(x);\",ore=xe({opSnippet:eP,packedOpSnippet:eP,cpuKernelImpl:bD}),tP={kernelName:ws,backendName:\"webgl\",kernelFunc:ore};var nre=\"return x * x;\",sre=xe({opSnippet:nre}),rP={kernelName:qi,backendName:\"webgl\",kernelFunc:sre};var oP=\"return (a - b) * (a - b);\",are=nt({opSnippet:oP,packedOpSnippet:oP}),nP={kernelName:ks,backendName:\"webgl\",kernelFunc:are};function ire(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;if(n.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");let s=t.readSync(n.dataId),a=w.fromUint8ToStringArray(s),i=CD(a,\"string\",o);return t.makeTensorInfo(n.shape,\"string\",i)}var sP={kernelName:Ru,backendName:\"webgl\",kernelFunc:ire};function ure({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=Wt+`\n return x > 0.0 ? 1.0 : float(${e.alpha});\n `,s=new tr(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var aP={kernelName:wo,backendName:\"webgl\",kernelFunc:ure};var Rg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=o;let n=o.length,s=Re(o.length),a=Re(o.length),i=\"\";if(n===1)i=\"coords * strides + begin\";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${e});\n ${s} strides = ${s}(${t});\n\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${i}));\n }\n `}};function pre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=pt.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=te({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let $=pt.computeOutShape(b,C,S),R=Gs({inputs:{x:n},backend:t,attrs:{begin:b,size:$}});k=te({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeIntermediateTensorInfo(R)}else if(t.shouldExecuteOnCPU([n])){let R=t.readSync(n.dataId),D=me(n.shape,n.dtype,R),P=wD(d,D,S,b);k=t.makeTensorInfo(f,n.dtype,P.values)}else{let R=new Rg(b,S,d);k=t.runWebGLProgram(R,[n],n.dtype)}let _=te({inputs:{x:k},backend:t,attrs:{shape:f}});return t.disposeIntermediateTensorInfo(k),_}var iP={kernelName:Ns,backendName:\"webgl\",kernelFunc:pre};function cre(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=SD(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var uP={kernelName:Ca,backendName:\"webgl\",kernelFunc:cre};function lre(r){let{inputs:e,backend:t,attrs:o}=r,{skipEmpty:n}=o,{input:s,delimiter:a}=e;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=t.readSync(s.dataId),p=t.readSync(a.dataId)[0],[u,c,l]=ID(i,p,n),m=c.length;return[t.makeTensorInfo([m,2],\"int32\",u),t.makeTensorInfo([m],\"string\",c),t.makeTensorInfo([2],\"int32\",new Int32Array(l))]}var pP={kernelName:ji,backendName:\"webgl\",kernelFunc:lre};function mre(r){let{inputs:e,backend:t,attrs:o}=r,{numBuckets:n}=o,{input:s}=e;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(n<=0)throw new Error(\"Number of buckets must be at least 1\");let a=t.readSync(s.dataId),i=vD(a,n);return t.makeTensorInfo(s.shape,\"int32\",i)}var cP={kernelName:Xi,backendName:\"webgl\",kernelFunc:mre};var dre=\"return tan(x);\",fre=xe({opSnippet:dre}),lP={kernelName:_s,backendName:\"webgl\",kernelFunc:fre};var hre=`\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,gre=xe({opSnippet:hre}),mP={kernelName:Es,backendName:\"webgl\",kernelFunc:gre};function xre(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return t.makeTensorInfo(n.shape,s.dtype);let d=te({inputs:{x:s},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:a},backend:t,attrs:{shape:[p,u]}}),h=te({inputs:{x:n},backend:t,attrs:{shape:m}}),g=new Cu(p,i,d.shape.length,f.shape.length,c,m,!1,!0),x=t.runWebGLProgram(g,[f,d,h],h.dtype),b=te({inputs:{x},backend:t,attrs:{shape:n.shape}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(x),b}var dP={kernelName:ds,backendName:\"webgl\",kernelFunc:xre};var Dg=class{constructor(e,t){this.variableNames=[\"A\"];let o=new Array(e.length);for(let a=0;a5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],o=[];for(let n=0;n5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=ND(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new Dg(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var fP={kernelName:po,backendName:\"webgl\",kernelFunc:Fv};var Ag=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n `}},Fg=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function kp(r,e){e!==null&&r.disposeIntermediateTensorInfo(e)}function hP(r){let e=1;for(;ep){let P=t.readSync(n.dataId),[O,M]=TD(P,u,n.dtype,s,a);return[t.makeTensorInfo(O.shape,O.dtype,O.values),t.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[t.makeTensorInfo(u,n.dtype,[]),t.makeTensorInfo(u,\"int32\",[])];if(c===1)return[n,Ci({attrs:{shape:u,dtype:\"int32\",value:0},backend:t})];let l=t.texData.get(n.dataId),m=l!==null&&l.isPacked,d=m?t.unpackTensor(n):n,h=y.sizeFromShape(u)/c,g=te({inputs:{x:d},attrs:{shape:[h,c]},backend:t});m&&kp(t,d);let x=hP(s),b=hP(c),C=null,S=()=>C===null?[g,g]:[g,C],k=(P,O,M)=>{let L=S(),B=new Ag(M),U=[[c],[C===null?1:0],[Number.NEGATIVE_INFINITY],[P],[O]],j=C;C=t.runWebGLProgram(B,L,\"int32\",U),kp(t,j)};for(let P=1;P=1;M/=2)k(O,M,[h,b])}for(let P=b;P>x;P/=2){let O=S(),M=new Fg([h,P/2]),B=[[c],[C===null?1:0],[x]],z=C;C=t.runWebGLProgram(M,O,\"int32\",B),kp(t,z);let U=x/2,j=U*2;for(let q=U;q>=1;q/=2)k(j,q,C.shape)}let _=C;C=Gs({inputs:{x:C},backend:t,attrs:{begin:0,size:[h,s]}}),kp(t,_);let $=Tv({inputs:{x:g,indices:C},backend:t,attrs:{axis:1,batchDims:1}});kp(t,g);let R=u.slice(0,-1);R.push(s),_=C,C=te({inputs:{x:C},attrs:{shape:R},backend:t}),kp(t,_);let D=$;return $=te({inputs:{x:$},attrs:{shape:R},backend:t}),kp(t,D),[$,C]}var gP={kernelName:$s,backendName:\"webgl\",kernelFunc:bre};var Pg=class{constructor(e,t,o,n,s,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=o===\"nearest\"?1:2,p;switch(n){case\"constant\":p=1;break;case\"reflect\":p=2;break;case\"wrap\":p=3;break;case\"nearest\":p=4;break;default:p=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${p} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = 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valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function Cre(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Pg(l,m,a,i,p,g);return t.runWebGLProgram(x,[n,s],\"float32\")}var xP={kernelName:Rs,backendName:\"webgl\",kernelFunc:Cre};function wre(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;Vs(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let a=o.readSync(s.dataId),{outputValues:i,outputShape:p,indices:u}=_D(a,n,s.shape,s.dtype);return[o.makeTensorInfo(p,s.dtype,i),o.makeTensorInfo([u.length],\"int32\",u)]}var yP={kernelName:Yi,backendName:\"webgl\",kernelFunc:wre};function Sre(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeIntermediateTensorInfo(h)),f}var bP={kernelName:wa,backendName:\"webgl\",kernelFunc:Sre};var Og=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p=\"0.0\",u=\"sumValue\",c=Math.floor(o/4)*4,l=o%4,m=`\n sumValue += dot(values, segFilter);\n `,d=\"\";s%o>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${p};\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${f}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${a})) * float(${o}));\n int currentSeg = int(mod(float(outIdx), float(${a})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function Ire(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=te({inputs:{x:l},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=oi(n.dtype),g=(S,k,_,$,R)=>{let D=S.shape[0],P=S.shape[1],O=w.segment_util.segOpComputeOptimalWindowSize(P,R),M={windowSize:O,inSize:P,batchSize:D,numSegments:R},L=new Og(M,k),B=t.compileAndRun(L,[S,_],$);if(p.push(B),B.shape[1]===R)return B;let z=Av({backend:t,attrs:{start:0,stop:R,step:1,dtype:\"float32\"}}),U=Fv({inputs:{x:z},backend:t,attrs:{reps:[P/O]}});return p.push(z),p.push(U),g(B,k,U,$,R)},x=g(f,\"unsortedSegmentSum\",s,h,a),b=te({inputs:{x},backend:t,attrs:{shape:m}}),C=b;if(c!=null){p.push(b);let S=w.getUndoAxesPermutation(c);C=bt({inputs:{x:C},backend:t,attrs:{perm:S}})}return p.forEach(S=>t.disposeIntermediateTensorInfo(S)),C}var CP={kernelName:Qi,backendName:\"webgl\",kernelFunc:Ire};var vre=[rA,nA,sA,aA,uA,pA,cA,lA,fA,hA,gA,xA,yA,bA,CA,wA,SA,IA,vA,kA,NA,_A,EA,$A,RA,PA,MA,LA,KD,zA,WA,UA,GA,HA,KA,qA,jA,XA,YA,QA,eF,tF,rF,oF,nF,sF,aF,iF,uF,pF,cF,lF,mF,dF,fF,hF,xF,yF,bF,CF,SF,IF,vF,kF,NF,TF,_F,EF,$F,HD,RF,VA,DF,AF,FF,qD,PF,OF,MF,LF,BF,zF,VF,WF,UF,GF,KF,qF,jF,XF,YF,QF,JF,t3,r3,o3,n3,s3,c3,YD,l3,m3,d3,f3,DA,h3,y3,b3,C3,w3,jD,S3,I3,v3,k3,N3,AA,a3,T3,_3,E3,ZD,$3,R3,D3,A3,F3,P3,O3,M3,L3,B3,z3,V3,W3,U3,G3,H3,TA,p3,K3,q3,j3,X3,Y3,Q3,Z3,J3,tP,rP,nP,sP,aP,iP,uP,pP,cP,u3,eA,lP,mP,dP,fP,gP,xP,tA,yP,bP,CP,g3];for(let r of vre)ti(r);var we;(function(r){r[r.float32=0]=\"float32\",r[r.int32=1]=\"int32\",r[r.bool=2]=\"bool\",r[r.string=3]=\"string\",r[r.complex64=4]=\"complex64\"})(we||(we={}));var wu;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(wu||(wu={}));var wP;function kre(r){wP=r.wasm.cwrap(So,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Nre(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e;if(n.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o,m=t.dataIdMap.get(n.dataId).id,d=t.dataIdMap.get(s.dataId).id,f=0;if(a!=null){let R=t.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);f=R.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=wu[c];if(g==null)throw new Error(`${c} 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voe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:c,dimRoundingMode:l}=t,m=u==null?[1,1]:u,d=w.computeConv2DInfo(n.shape,s.shape,p,m,c,l,!0),f=d.filterHeight,h=d.filterWidth,g=d.padInfo.top,x=d.padInfo.right,b=d.padInfo.bottom,C=d.padInfo.left,S=d.dilationHeight,k=d.dilationWidth,_=d.strideHeight,$=d.strideWidth,R=d.inChannels,D=d.outChannels,P=d.padInfo.type===\"SAME\"?1:0;if(d.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. 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Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=t.makeOutput(s.shape,s.dtype);return BO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var zO={kernelName:Li,backendName:\"wasm\",setupFunc:Eoe,kernelFunc:$oe};var VO;function Roe(r){VO=r.wasm.cwrap(Mi,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Doe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o;if(n.dtype!==s.dtype||n.dtype!==a.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=t.makeOutput(n.shape,n.dtype);return VO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var WO={kernelName:Mi,backendName:\"wasm\",setupFunc:Roe,kernelFunc:Doe};var UO=he(hn);var GO;function Aoe(r){GO=r.wasm.cwrap(Xa,null,[\"number\",\"number\",\"number\"])}function Foe(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=t.makeOutput(n.shape,\"float32\"),a=i=>t.dataIdMap.get(i.dataId).id;return GO(a(n),a(o),a(s)),s}var HO={kernelName:Xa,backendName:\"wasm\",setupFunc:Aoe,kernelFunc:Foe};var Poe=!1,KO=Ge(xn,Poe,\"bool\");var qO=he(gn);var jO=he(yn,\"float32\");function Lg(r){let{inputs:e,attrs:t,backend:o}=r,{input:n}=e,{dim:s}=t,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),zt({inputs:{x:n},backend:o,attrs:{shape:i}})}var XO={kernelName:na,backendName:\"wasm\",kernelFunc:Lg};var YO=he(bn,\"float32\");function Mv(r){let{attrs:{shape:e,value:t},backend:o}=r,{attrs:{dtype:n}}=r;n=n||y.inferDtype(t);let s=o.makeOutput(e,n);return o.typedArrayFromHeap(s).fill(t),s}var QO={kernelName:sa,backendName:\"wasm\",kernelFunc:Mv};var ZO;function Ooe(r){ZO=r.wasm.cwrap(Cn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Moe(r){let{inputs:e,backend:t}=r,{image:o}=e,n=t.makeOutput(o.shape,o.dtype),s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return ZO(s,i,p,u,c,a),n}var JO={kernelName:Cn,backendName:\"wasm\",kernelFunc:Moe,setupFunc:Ooe};var eM=he(wn);var Loe=!1,tM=Ge(Sn,Loe);var rM;function Boe(r){rM=r.wasm.cwrap(In,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function zoe(r){let{backend:e,inputs:t,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=t,c=e.dataIdMap.get(s.dataId).id,l=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,d=p!=null?e.dataIdMap.get(p.dataId).id:0,f=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return rM(c,l,m,d,f,n,g),h}var oM={kernelName:In,backendName:\"wasm\",setupFunc:Boe,kernelFunc:zoe};var nM;function Voe(r){nM=r.wasm.cwrap(Io,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Woe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=wu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,$=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return nM(x,j,q,Y,b,k,_,S,$,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var sM={kernelName:Io,backendName:\"wasm\",setupFunc:Voe,kernelFunc:Woe};var aM;function Uoe(r){aM=r.wasm.cwrap(vo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Goe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m,!0),g=wu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,$=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return aM(x,j,q,Y,b,k,_,S,$,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var iM={kernelName:vo,backendName:\"wasm\",setupFunc:Uoe,kernelFunc:Goe};var uM;function Hoe(r){uM=r.wasm.cwrap(vn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Koe(r){let{backend:e,inputs:t}=r,{params:o,indices:n}=t,[s,a,i,p]=af.prepareAndValidate(o,n),u=e.makeOutput(s,o.dtype);if(a===0)return u;let c=n.shape,l=c[c.length-1],d=e.dataIdMap.get(o.dataId).id,h=e.dataIdMap.get(n.dataId).id,g=new Uint8Array(new Int32Array(p).buffer),x=e.dataIdMap.get(u.dataId).id;return uM(d,we[o.dtype],h,a,l,i,g,x),u}var pM={kernelName:vn,backendName:\"wasm\",setupFunc:Hoe,kernelFunc:Koe};var cM;function qoe(r){cM=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function joe(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,indices:s}=t,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=e.readSync(s.dataId),c=n.shape[p];for(let D=0;D=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let l=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=zt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:e}),d=y.sizeFromShape(s.shape),f=zt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:e}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=e.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,C=e.dataIdMap.get(m.dataId).id,k=e.dataIdMap.get(f.dataId).id,_=e.dataIdMap.get(g.dataId).id,$=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return cM(C,we[n.dtype],$,x,k,l.batchSize,R,_),e.disposeData(m.dataId),e.disposeData(f.dataId),g.shape=l.outputShape,g}var lM={kernelName:aa,backendName:\"wasm\",setupFunc:qoe,kernelFunc:joe};var Xoe=!1,mM=Ge(kn,Xoe,\"bool\");var Yoe=!1,dM=Ge(Nn,Yoe,\"bool\");var fM=he(Tn,\"bool\");var hM=he(_n,\"bool\");var gM=he(En,\"bool\");var xM;function Qoe(r){xM=r.wasm.cwrap($n,null,[\"number\",\"number\",\"number\",\"number\"])}function Zoe(r){let{inputs:{x:e},attrs:{alpha:t},backend:o}=r,n=o.dataIdMap.get(e.dataId).id,s=o.makeOutput(e.shape,\"float32\");if(y.sizeFromShape(e.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;xM(n,we[e.dtype],t,a)}return s}var yM={kernelName:$n,backendName:\"wasm\",setupFunc:Qoe,kernelFunc:Zoe};var Joe=!1,bM=Ge(Rn,Joe,\"bool\");var ene=!1,CM=Ge(Dn,ene,\"bool\");var wM;function tne(r){wM=r.wasm.cwrap(An,null,[\"number\",\"number\",\"number\",\"number\"])}function rne(r){let{attrs:e,backend:t}=r,{start:o,stop:n,num:s}=e,a=Math.floor(s),i=t.makeOutput([a],\"float32\");return wM(t.dataIdMap.get(i.dataId).id,o,n,a),i}var SM={kernelName:An,backendName:\"wasm\",setupFunc:tne,kernelFunc:rne};var IM=he(Fn);var vM=he(Pn);var one=!1,kM=Ge(On,one,\"bool\");var NM=he(Mn);var nne=!1,TM=Ge(Ln,nne,\"bool\");var sne=!1,_M=Ge(R0,sne,\"bool\");var EM;function ane(r){EM=r.wasm.cwrap(Bn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ine(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o;if(n.dtype!==\"float32\")throw new Error(\"LRN error: x must have dtype float32\");let u=t.makeOutput(n.shape,n.dtype);return EM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var $M={kernelName:Bn,backendName:\"wasm\",setupFunc:ane,kernelFunc:ine};var RM;function une(r){RM=r.wasm.cwrap(Ya,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function pne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o;if(n.dtype!==\"float32\"||s.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"LRNGrad error: x, y, and dy must have dtype float32\");let l=t.makeOutput(n.shape,n.dtype);return RM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,a.shape[3],i,p,u,c),l}var DM={kernelName:Ya,backendName:\"wasm\",setupFunc:une,kernelFunc:pne};var AM;function cne(r){AM=r.wasm.cwrap(zn,null,[\"number\",\"number\",\"number\",\"number\"])}function lne(r){let{backend:e,inputs:t,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=t,p=e.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e);if(d){let C=e.dataIdMap.get(c.dataId).id;u=c,p=C}let f=u.shape.length;w.assertAxesAreInnerMostDims(\"max\",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;AM(p,we[a.dtype],x,C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var FM={kernelName:zn,backendName:\"wasm\",setupFunc:cne,kernelFunc:lne};var mne=!1,PM=Ge(Vn,mne);var OM;function dne(r){OM=r.wasm.cwrap(Wn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function fne(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=t,c=w.computePool2DInfo(n.shape,a,i,1,p,u),l=c.filterHeight,m=c.filterWidth,d=c.padInfo.top,f=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,C=c.strideHeight,S=c.strideWidth,k=c.inChannels,_=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let $=o.makeOutput(c.outShape,\"float32\"),R=o.dataIdMap.get($.dataId).id;return OM(s,n.shape[0],n.shape[1],n.shape[2],l,m,d,f,h,g,x,b,C,S,k,_,R),$}var MM={kernelName:Wn,backendName:\"wasm\",setupFunc:dne,kernelFunc:fne};var LM;function hne(r){LM=r.wasm.cwrap(\"MaxPool3D\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function gne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=w.computePool3DInfo(n.shape,s,a,1,i,p,u),l=t.makeOutput(c.outShape,n.dtype);return LM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var BM={kernelName:ia,backendName:\"wasm\",setupFunc:hne,kernelFunc:gne};var zM;function xne(r){zM=r.wasm.cwrap(\"MaxPool3DGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function yne(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=t.makeOutput(s.shape,s.dtype);return zM(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var VM={kernelName:Gi,backendName:\"wasm\",setupFunc:xne,kernelFunc:yne};var WM;function bne(r){WM=r.wasm.cwrap(\"MaxPoolGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Cne(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool2DInfo(s.shape,a,i,1,p,u),l=t.makeOutput(s.shape,s.dtype);return WM(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),l}var UM={kernelName:Ui,backendName:\"wasm\",setupFunc:bne,kernelFunc:Cne};var GM;function wne(r){GM=r.wasm.cwrap(\"MaxPoolWithArgmax\",null,[\"number\",\"number\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Sne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,includeBatchInIndex:p}=o;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,[1,1],i),l=t.makeOutput(c.outShape,n.dtype),m=t.makeOutput(c.outShape,\"int32\");return GM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,t.dataIdMap.get(m.dataId).id,we[n.dtype],p,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[l,m]}var HM={kernelName:ua,backendName:\"wasm\",setupFunc:wne,kernelFunc:Sne};var KM;function Ine(r){KM=r.wasm.cwrap(Un,null,[\"number, number, number\"])}function vne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e),f=l;if(d){let S=e.dataIdMap.get(c.dataId).id;S!==i&&(u=c,p=S,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims(\"mean\",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!==\"float32\"&&(b=Mr({backend:e,inputs:{x:u},attrs:{dtype:\"float32\"}}),p=e.dataIdMap.get(b.dataId).id);let C=e.makeOutput(h,\"float32\");if(y.sizeFromShape(u.shape)!==0){let S=e.dataIdMap.get(C.dataId).id;KM(p,x,S)}if(d&&e.disposeData(c.dataId),s){let S=w.expandShapeToKeepDim(C.shape,m);C.shape=S}return u.dtype!==\"float32\"&&e.disposeData(b.dataId),C}var qM={kernelName:Un,backendName:\"wasm\",setupFunc:Ine,kernelFunc:vne};var jM;function kne(r){jM=r.wasm.cwrap(Gn,null,[\"number\",\"number\",\"number\",\"number\"])}function Nne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e);if(d){let 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CL={kernelName:rs,backendName:\"wasm\",setupFunc:jne,kernelFunc:Xne};var wL;function Yne(r){wL=r.wasm.cwrap(os,null,[\"number\",\"number\",\"number\",\"number\"])}function Qne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e),f=l;if(d){let C=e.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims(\"prod\",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;wL(p,x,we[b.dtype],C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var SL={kernelName:os,backendName:\"wasm\",setupFunc:Yne,kernelFunc:Qne};var Zne=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=up(o,n,s,a),p=e.makeOutput([i.length],a);return 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PL={kernelName:Za,backendName:\"wasm\",setupFunc:ase,kernelFunc:ise};var OL;function use(r){OL=r.wasm.cwrap(ps,null,[\"number\",\"array\",\"number\",\"array\",\"number\",\"number\"])}function pse(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=y.parseAxisParam(s,n.shape);if(n.shape.length===0)return Np({inputs:{x:n},backend:t});let i=t.makeOutput(n.shape,n.dtype),p=t.dataIdMap.get(n.dataId).id,u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),l=new Uint8Array(new Int32Array(n.shape).buffer);OL(p,c,a.length,l,n.shape.length,u);let m=zt({inputs:{x:i},attrs:{shape:n.shape},backend:t});return t.disposeData(i.dataId),m}var ML={kernelName:ps,backendName:\"wasm\",kernelFunc:pse,setupFunc:use};var LL;function cse(r){LL=r.wasm.cwrap(Ds,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\"])}function 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find the buffer in buffer manager\");a[i]=a[a.length-1],a.pop(),this.numUsedBuffers--,this.numBytesUsed-=o,t?(this.freeBuffers.get(s).push(e),this.numFreeBuffers++):(e.destroy(),this.numBytesAllocated-=o)}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function YB(r,e){return`${r}_${e}`}var qg=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,o,n){let s=ZB(o),a=e*t*s,i=QB(e,t,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[e,t],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(e){if(this.freeTextures.size===0)return;let t=e.width,o=e.height,n=e.format,s=e.usage,a=QB(t,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(e);if(p<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");i.splice(p,1);let u=ZB(n),c=t*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function QB(r,e,t,o){return`${r}_${e}_${t}_${o}`}function ZB(r){if(r===\"rgba8unorm\")return 16;throw new Error(`${r} is not supported!`)}function JB(r,e){if(Math.max(...r)>5)throw new Error(\"Cannot symbolically compute strides for rank > 6 tensor.\");let t=r.length,o=\"xyzwuv\",n=r.map(a=>`${e}.${o[a]}`),s=new Array(t-1);s[t-2]=n[t-1];for(let a=t-3;a>=0;--a)s[a]=`(${s[a+1]} * ${n[a+1]})`;return s}var Qr=(r,e,t)=>t===\"int32\"?`atomicAdd(${r}, bitcast(${e}));`:`\n {\n var oldValue = 0;\n loop {\n let newValueF32 = bitcast(oldValue) + (${e});\n let newValue = bitcast(newValueF32);\n let res = atomicCompareExchangeWeak(${r}, oldValue, newValue);\n if res.exchanged {\n break;\n }\n oldValue = res.old_value;\n }\n }`;var wi;(function(r){r[r.FROM_PIXELS=0]=\"FROM_PIXELS\",r[r.DRAW=1]=\"DRAW\"})(wi||(wi={}));var oz=(r,e,t,o,n)=>{let s={dtype:o.dtype,shape:o.shape},a=cae(t,s,e),i=r.createShaderModule({code:a,label:e.constructor.name}),p=A().get(\"WEBGPU_PRINT_SHADER\");if(p!==\"\"){p=p.toLowerCase();let u=p.split(\",\");(p===\"all\"||u.some(c=>e.shaderKey.toLowerCase().includes(c)))&&(console.group(e.shaderKey),console.debug(a),console.groupEnd())}return n?r.createComputePipelineAsync({compute:{module:i,entryPoint:\"_start\"},label:e.constructor.name,layout:\"auto\"}):r.createComputePipeline({compute:{module:i,entryPoint:\"_start\"},label:e.constructor.name,layout:\"auto\"})},Ae=(r,e=\"f32\")=>{switch(r){case 1:return`${e}`;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${r}-component ${e} is not supported.`)}};function ft(r){if(r<=1)return\"i32\";if(r===2)return\"vec2\";if(r===3)return\"vec3\";if(r===4)return\"vec4\";if(r===5)return\"vec5\";if(r===6)return\"vec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Oo(r){if(r===0)return\"x\";if(r===1)return\"y\";if(r===2)return\"z\";if(r===3)return\"w\";if(r===4)return\"u\";if(r===5)return\"v\";throw Error(`Index ${r} is not yet supported`)}function G(...r){let e;switch(r.length){case 0:e=`\n fn main()\n `;break;case 1:e=`\n fn main(${r[0]} : i32)\n `;break;default:throw Error(\"Unreachable\")}return e}function ez(r,e){let t;return t=`\n ${pae(e)}\n fn _start(@builtin(local_invocation_id) LocalId : vec3,\n @builtin(global_invocation_id) GlobalId : vec3,\n @builtin(local_invocation_index) LocalIndex: u32,\n @builtin(workgroup_id) WorkgroupId : vec3,\n @builtin(num_workgroups) NumWorkgroups : vec3) {\n localId = LocalId;\n localIndex = LocalIndex;\n globalId = GlobalId;\n numWorkgroups = NumWorkgroups;\n workgroupId = WorkgroupId;\n ${r?\"main(getGlobalIndex());\":\"main();\"};\n }\n `,t}function pae(r){return`\n @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]})\n`}function cae(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(t.outputComponent=t.outputComponent?t.outputComponent:1,o.push(`\n\n var localId: vec3;\n var localIndex: u32;\n var globalId: vec3;\n var numWorkgroups: vec3;\n var workgroupId: vec3;\n\n // Only used when the y/z dimension of workgroup size is 1.\n fn getGlobalIndex() -> i32 {\n ${sz(t)?\" return i32(globalId.x);\":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +\n workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u +\n localIndex);\n `}\n }\n `),t.pixelsOpType!=null){let f=t.pixelsOpType===wi.FROM_PIXELS?`@group(0) @binding(0) var result: array<${Su(e.dtype,t.outputComponent)}>;`:`@group(0) @binding(1) var inBuf : array<${Su(r[0].dtype,t.outputComponent)}>;`,h=e.shape.length===3?\"vec2\":\"i32\";o.push(`\n struct Uniform {\n outShapeStrides : ${h},\n size : i32,\n numChannels : i32,\n alpha : f32,\n };\n\n ${f}\n @group(0) @binding(2) var uniforms: Uniform;\n `);let g=rz(t);return[tz,o.join(`\n`),cm(e.shape),t.getUserCode(),ez(g,t)].join(`\n`)}let s,a,i=\"struct Uniforms { NAN : f32, INFINITY : f32, \";t.variableNames.forEach((f,h)=>{let g=ft(r[h].shape.length);i+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `,s=r[h].shape.length-1,a=ft(s),i+=`${f.charAt(0).toLowerCase()+f.slice(1)}ShapeStrides: ${a}, `});let p=ft(e.shape.length);i+=`outShape : ${p}, `,s=e.shape.length-1,a=ft(s),i+=`\n outShapeStrides: ${a}, `,t.size&&(i+=\"size : i32, \"),t.uniforms&&(i+=t.uniforms),i+=\"};\",i=yae(i),o.push(i),t.atomic?o.push(`\n @group(0) @binding(0) var result: array>;\n `):o.push(`\n @group(0) @binding(0) var result: array<${Su(e.dtype,t.outputComponent)}>;\n `),t.variableNames.forEach((f,h)=>{o.push(`\n @group(0) @binding(${1+h}) var ${f}: array<${t.variableComponents?Su(r[h].dtype,t.variableComponents[h]):Su(r[h].dtype,t.outputComponent)}>;\n `)}),i!==\"\"&&o.push(`\n @group(0) @binding(${1+t.variableNames.length}) var uniforms: Uniforms;\n `);let u=hae(e.shape,t.dispatchLayout),c=[tz,o.join(`\n`)+lae,cm(e.shape),u,gae(e.shape.length)];t.atomic||c.push(xae(e.shape,e.dtype,t.outputComponent)),t.variableNames.forEach((f,h)=>{c.push(`${cm(r[h].shape,f)}`)});let l=r.map((f,h)=>fae(f,e.shape,t.variableComponents?t.variableComponents[h]:t.outputComponent,t.dispatchLayout.x.length===e.shape.length)).join(`\n`);c.push(l),c.push(t.getUserCode());let m=rz(t);return c.push(ez(m,t)),c.join(`\n`)}function nz(r,e,t){let o=r.shaderKey;if(r.pixelsOpType!=null)return o;let n=[],s=[];e.forEach(c=>{n.push(c.shape),s.push(c.dtype)}),n.push(t.shape),s.push(t.dtype);let a=e.map(c=>w.getBroadcastDims(c.shape,t.shape)),i=e.map(c=>y.arraysEqual(c.shape,t.shape)).join(\"_\"),p=a.map(c=>c.join(\"_\")).join(\";\"),u=sz(r)?\"flatDispatch\":\"\";return o+=\"_\"+(r.workgroupSize?r.workgroupSize.join(\",\"):\"\")+n.map(c=>c.length).join(\",\")+s.join(\",\")+r.variableNames.join(\",\")+p+i+u,o}var tz=`\n struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\n // Checks whether coordinates lie within the bounds of the shape.\n fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool {\n return all(coord >= vec2(0)) && all(coord < shape);\n }\n fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool {\n return all(coord >= vec3(0)) && all(coord < shape);\n }\n fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool {\n return all(coord >= vec4(0)) && all(coord < shape);\n }\n\n fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n return coord;\n }\n fn getIndexFromCoords2D(coords : vec2, shape : vec2) -> i32 {\n return dot(coords, vec2(shape.y, 1));\n }\n fn getIndexFromCoords3D(coords : vec3, shape : vec3) -> i32 {\n return dot(coords, vec3(shape.y * shape.z, shape.z, 1));\n }\n fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n }\n fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n }\n fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n }\n\n // NaN defination in IEEE 754-1985 is :\n // - sign = either 0 or 1.\n // - biased exponent = all 1 bits.\n // - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n fn isnan(val: f32) -> bool {\n let floatToUint: u32 = bitcast(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n fn isnanVec4(val : vec4) -> vec4 {\n let floatToUint: vec4 = bitcast>(val);\n return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u);\n }\n`,lae=`\n fn isinf(val: f32) -> bool {\n return abs(val) == uniforms.INFINITY;\n }\n`;function cm(r,e=\"\"){let t=r.length,o=e!==\"\"?`get${e.charAt(0).toUpperCase()+e.slice(1)}CoordsFromIndex`:\"getCoordsFromIndex\",n=e!==\"\"?`${e.charAt(0).toLowerCase()+e.slice(1)}ShapeStrides`:\"outShapeStrides\";if(t<=1)return`fn ${o}(index : i32) -> i32 { return index; }`;let s=y.computeStrides(r),a=ft(t),i=[];for(let u=0;u vec2 {\n let d0 = index / uniforms.${n}; let d1 = index - d0 * uniforms.${n};\n return vec2(d0, d1);\n }`;let p;return p=\"var index2 = index;\"+s.map((u,c)=>{let l=`let ${i[c]} = index2 / uniforms.${n}.${Oo(c)}`,m=c===s.length-1?`let ${i[c+1]} = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`:`index2 = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`;return`${l}; ${m};`}).join(\"\"),`\n fn ${o}(index : i32) -> ${a} {\n ${p}\n return ${a}(${i.join(\",\")});\n }\n `}function mae(r,e){let t=r.name,o=r.shape.length,n=ft(o),s=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=a.map(c=>`${c} : i32`).join(\", \");if(o<1)return`\n fn ${s}() -> ${Ae(e)} {\n return ${Ae(e)}(${t}[0]);\n }\n `;let p=`uniforms.${t.charAt(0).toLowerCase()+t.slice(1)}Shape`,u=`${o}D`;return o===0&&(u=\"1D\"),`\n fn ${s}(${i}) -> ${Ae(e)} {\n return ${Ae(e)}(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p})${e===1?\"\":` / ${e}`}]);\n }\n `}function dae(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a=\"get\"+s+\"ByOutput\",i=r.shape.length,p=e.length,u=ft(p);if(y.arraysEqual(r.shape,e)&&o)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)} {\n return ${Ae(t)}(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(t)} {\n return ${Ae(t)}(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}${t===1?\"\":` / ${t}`}]);\n }\n `;let c=w.getBroadcastDims(r.shape,e),l=p-i,m=\"\";if(i===0)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)}{\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(t)}{\n return get${s}();\n }\n `;p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(g=>`coords.${Oo(g+l)} = 0;`).join(`\n`);let d=\"\";if(p<2&&i>0)d=\"coords\";else if(p>1){let g=ft(i),x=r.shape.map((b,C)=>`coords.${Oo(C+l)}`).join(\", \");d=`${g}(${x})`}else d=\"coords\";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)} {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return ${Ae(t)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t===1?\"\":` / ${t}`}]);\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> ${Ae(t)} {\n var coords = coordsIn;\n ${m}\n return ${Ae(t)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t===1?\"\":` / ${t}`}]);\n }\n`}function fae(r,e,t,o){let n=mae(r,t);return r.shape.length<=e.length&&(n+=dae(r,e,t,o)),n}function hae(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return\"\";if(t.length===s)return`fn getOutputCoords() -> ${ft(s)}{\n let globalIndex = getGlobalIndex();\n return getCoordsFromIndex(globalIndex);\n }\n `;let i=\"\",p=[t,o,n];for(let m=0;m ${c} {\n ${i}\n`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(\",\")}); }`,l}function gae(r){let e=\"\";switch(r){case 0:case 1:e+=`\n fn getOutputIndexFromCoords(coords : i32) -> i32 {\n return coords;\n }\n `;break;case 2:e+=`\n fn getOutputIndexFromCoords(coords : vec2) -> i32 {\n return dot(coords, vec2(uniforms.outShapeStrides, 1));\n }\n `;break;case 3:e+=`\n fn getOutputIndexFromCoords(coords : vec3) -> i32 {\n return dot(coords, vec3(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n }\n `;break;case 4:e+=`\n fn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n }\n `;break;case 5:e+=`\n fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u;\n }\n `;break;case 6:e+=`\n fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u * uniforms.outShapeStrides.u +\n coords.v;\n }\n `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function sz(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function Su(r,e=1){if(r===\"float32\")return Ae(e,\"f32\");if(r===\"int32\"||r===\"bool\")return Ae(e,\"i32\");throw new Error(`type ${r} is not supported.`)}function xae(r,e,t){let o=r.length,n=Su(e,t),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Ae(t)}) {\n result[flatIndex] = ${n}(value);\n }\n\n fn setOutputAtIndexI32(flatIndex : i32, value : ${Ae(t,\"i32\")}) {\n result[flatIndex] = ${n}(value);\n }\n `;if(o>=2){let a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=ft(o);s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(t)}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex${t===1?\"\":` / ${t}`}, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(t,\"i32\")}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex${t===1?\"\":` / ${t}`}, value);\n }\n `}return s}function yae(r){let e=/(\\w+)\\s*:\\s*vec(5|6)/g;r=r.replace(e,o=>\"@align(16) \"+o);let t=/vec(5|6)\\s*,\\s*(\\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function rz(r){return!(r.dispatchLayout.hasOwnProperty(\"y\")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty(\"z\")&&r.dispatchLayout.z.length!==0)}var Zv={};qe(Zv,{GPUBytesPerElement:()=>jg,MatMulProgramType:()=>Mo,assertNotComplex:()=>fm,computeDispatch:()=>H,computeWorkPerThreadForConv2d:()=>mm,computeWorkgroupInfoForMatMul:()=>Qv,computeWorkgroupSizeForConv2d:()=>lm,flatDispatchLayout:()=>X,isWebGPUSupported:()=>dm,tilesFitEvenlyIntoShape:()=>Cae});var Tp=r=>{let e=1;for(let t=0;tt%r[o]===0)}function H(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(Tp(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(Tp(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(Tp(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function Qv(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function lm(r,e,t=!1){if(t)return[8,8,1];let o=Tp(r.x.map(s=>e[s])),n=Tp(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function mm(r,e,t=!1){if(t)return[4,4,1];let o=Tp(r.x.map(s=>e[s])),n=Tp(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function X(r){return{x:r.map((e,t)=>t)}}function jg(r){if(r===\"float32\"||r===\"int32\"||r===\"bool\"||r===\"string\")return 4;if(r===\"complex64\")return 8;throw new Error(`Unknown dtype ${r}`)}function dm(){return!!(typeof globalThis!=\"undefined\"&&globalThis.navigator&&globalThis.navigator.gpu)}function fm(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!==\"complex64\",()=>`${e} does not support complex64 tensors in the WebGPU backend.`)})}var Mo;(function(r){r[r.MatMulReduceProgram=0]=\"MatMulReduceProgram\",r[r.MatMulSplitKProgram=1]=\"MatMulSplitKProgram\",r[r.MatMulSmallOutputSizeProgram=2]=\"MatMulSmallOutputSizeProgram\",r[r.MatMulPackedProgram=3]=\"MatMulPackedProgram\",r[r.MatMulMax=4]=\"MatMulMax\"})(Mo||(Mo={}));var wae=A().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),Sae=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[s,s,s]):[s,s,1]},jc=class r extends ao{nextDataId(){return r.nextDataId++}constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchCountInPass=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.queryResolveBuffer=null,this.querySet=null,this.querySetCount=2,this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,this.hasReadSyncWarned=!1,this.hasTimestampQueryWarned=!1,!dm())throw new Error(\"WebGPU is not supported on this device\");this.pipelineCache={},this.device=e,this.queue=e.queue,this.commandEncoder=null,this.computePassEncoder=null,this.adapterInfo=new Hg(t),this.supportTimestampQuery=this.device.features.has(\"timestamp-query\"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Kg(this.device),this.textureManager=new qg(this.device),this.tensorMap=new Bo(this,ur()),A().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}disposeData(e,t=!1){if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);return t?o.refCount=0:o.refCount--,o.refCount>0?!1:(o.complexTensorInfos!=null&&(this.disposeData(o.complexTensorInfos.real.dataId),this.disposeData(o.complexTensorInfos.imag.dataId)),this.commandQueueOwnedIds.has(e)?(this.tensorDataPendingDisposal.push(e),!0):(this.releaseResource(e),this.tensorMap.delete(e),!0))}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resource)){if(t.external){t.resource=null;return}t.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(t.resource):t.resource instanceof GPUTexture&&this.textureManager.releaseTexture(t.resource),t.resource=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let e;try{e=await Promise.all(Object.values(this.pipelineCache))}catch(t){throw new Error(t.message)}Object.keys(this.pipelineCache).map((t,o)=>{this.pipelineCache[t]=e[o]})}async getBufferData(e){if(A().getBool(\"WEBGPU_ENGINE_COMPILE_ONLY\"))return console.warn(\"The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false\"),null;let t=e.size,o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o),A().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(y.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o,complexTensorInfos:n}=t;if(o!=null||t.dtype===\"string\")return o;if(t.dtype===\"complex64\"){let h=this.readSync(n.real.dataId),g=this.readSync(n.imag.dataId),x=y.convertBackendValuesAndArrayBuffer(w.mergeRealAndImagArrays(h,g).buffer,\"float32\");return this.convertAndCacheOnCPU(e,x),x}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn(\"The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead.\"));let s=[\"opaque\",\"premultiplied\"],a=t.resource,i=a.size;y.assert(i%4===0,()=>\"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.\");let p=i/4,u=new ArrayBuffer(i),c=256,l=256,m=s.map(h=>new OffscreenCanvas(c,l)),d=new OffscreenCanvas(c,l);this.endComputePassEncoder(),m.map((h,g)=>{let x=h.getContext(\"webgpu\");return x.configure({device:this.device,format:\"bgra8unorm\",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),x.getCurrentTexture()}).map((h,g)=>{let x=c*4,b=(R,D,P)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:a,bytesPerRow:x,offset:P},{texture:h},{width:R,height:D}),this.submitQueue();let O=d.getContext(\"2d\",{willReadFrequently:!0});O.clearRect(0,0,R,D),O.drawImage(m[g],0,0);let M=O.getImageData(0,0,R,D).data,L=s[g],B=new Uint8ClampedArray(u,P,R*D*4);for(let z=0;z0&&(b(S,k,_),_+=k*(c*4)),S=$%c,S>0&&b(S,1,_)});let f=y.convertBackendValuesAndArrayBuffer(u,t.dtype);return this.convertAndCacheOnCPU(e,f),f}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return o;let n;if(t.dtype===\"complex64\"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=w.mergeRealAndImagArrays(a,i)}else{let s=await this.getBufferData(t.resource);n=y.convertBackendValuesAndArrayBuffer(s,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e){let t=e.size,o=e.usage,n=this.bufferManager.acquireBuffer(t,o);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,o){let n=e.buffer;if(o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. \");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:t,values:null,refCount:1,external:e.zeroCopy});let a=this.tensorMap.get(s),i=jg(a.dtype)*y.sizeFromShape(a.shape);if(e.buffer.sizey.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(e.shape,e.dtype,t)}async time(e){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn(\"This device doesn't support timestamp-query extension. Start Chrome browser with flag --enable-dawn-features=allow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled.\"),this.hasTimestampQueryWarned=!0);let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let o=this.tensorMap.get(e.dataId).resource;return o instanceof GPUBuffer?{buffer:o}:o instanceof GPUTexture?o.createView():o}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resource!=null)return;let o=jg(t.dtype)*y.sizeFromShape(t.shape),n,s=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(t.values){if(n=this.bufferManager.acquireBuffer(o,s,!0),n.mapState===\"unmapped\"){let a=this.bufferManager.acquireBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),i=a.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(i).set(t.values):new Float32Array(i).set(t.values),a.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(a,0,n,0,o),this.stagingPendingDisposal.push(a)}else{let a=n.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),n.unmap()}t.values=null}else n=this.bufferManager.acquireBuffer(o,s);t.resource=n}makeUniforms(e){let t=0,o=0,n=[],s=1;e.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),u>s&&(s=u),t=Math.ceil(t/u)*u,o=p.data.length,n.push(t),t+=p.data.length*4}),t=Math.ceil(t/s)*s;let a=new ArrayBuffer(t);e.forEach((p,u)=>{let c=n[u];p.type===\"int32\"?new Int32Array(a,c,p.data.length).set(p.data):p.type===\"uint32\"?new Uint32Array(a,c,p.data.length).set(p.data):new Float32Array(a,c,p.data.length).set(p.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(i,0,a,0,t),this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=Sae(this.device,e);let a=t.map((p,u)=>{if(p.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(p.dataId),{dtype:this.tensorMap.get(p.dataId).dtype,shape:p.shape,name:e.variableNames[u]}});e.shaderKey=nz(e,a,s);let i=A().getBool(\"WEBGPU_ENGINE_COMPILE_ONLY\");return e.shaderKey in this.pipelineCache||(this.pipelineCache[e.shaderKey]=oz(this.device,e,a,s,i)),e.pipeline=this.pipelineCache[e.shaderKey],i||this.recordAndSubmit(e,s,t,n),s}recordAndSubmit(e,t,o,n){if(e.pipeline instanceof Promise)throw new Error(\"Please call checkCompileCompletionAsync to ensure parallel compilation is done!\");let s=[],a=[],i=\"int32\";if(e.pixelsOpType==null){s.push({type:\"float32\",data:[NaN]},{type:\"float32\",data:[1/0]}),a=o.concat(t).map(d=>d.shape);let m=\"int32\";a.map(d=>{s.push({type:m,data:d});let f=y.computeStrides(d);s.push({type:m,data:f})})}else{let m=y.computeStrides(t.shape);s.push({type:i,data:m})}if(e.size){let m=y.sizeFromShape(e.outputShape);s.push({type:i,data:[e.outputComponent?m/e.outputComponent:m]})}n&&(s=[...s,...n]);let p=[this.tensorToBinding(t),...o.map(m=>this.tensorToBinding(m)),this.makeUniforms(s)];o.forEach(m=>{this.commandQueueOwnedIds.add(m.dataId)}),this.commandQueueOwnedIds.add(t.dataId);let u=this.device.createBindGroup({layout:e.pipeline.getBindGroupLayout(0),entries:p.map((m,d)=>({binding:d,resource:m}))}),c=this.activeTimers!=null;this.ensureCommandEncoderReady();let l={};c&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:this.querySetCount})),l.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:0,endOfPassWriteIndex:1},this.computePassEncoder=this.commandEncoder.beginComputePass(l)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(l)),this.computePassEncoder.setPipeline(e.pipeline),this.computePassEncoder.setBindGroup(0,u),this.computePassEncoder.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),this.dispatchCountInPass++,(c||A().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchCountInPass||e.pixelsOpType===wi.DRAW)&&(this.endComputePassEncoder(),c?this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let e=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.querySetCount*8),this.submitQueue(),await e.mapAsync(GPUMapMode.READ);let t=new BigUint64Array(e.getMappedRange()),o=Number(t[1]-t[0])/1e6;return e.unmap(),this.bufferManager.releaseBuffer(e),o}shouldExecuteOnCPU(e,t=wae){return A().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(o=>this.tensorMap.get(o.dataId).resource==null&&y.sizeFromShape(o.shape){let r={powerPreference:A().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},e=await navigator.gpu.requestAdapter(r),t={},o=[];e.features.has(\"timestamp-query\")&&o.push(\"timestamp-query\"),e.features.has(\"bgra8unorm-storage\")&&o.push([\"bgra8unorm-storage\"]),t.requiredFeatures=o;let n=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize,maxBufferSize:n.maxBufferSize,maxComputeWorkgroupSizeX:n.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:n.maxComputeInvocationsPerWorkgroup};let s=await e.requestDevice(t),a=\"info\"in e?e.info:\"requestAdapterInfo\"in e?await e.requestAdapterInfo():void 0;return new jc(s,a)},3);var fe;(function(r){r[r.ADD=0]=\"ADD\",r[r.ATAN2=1]=\"ATAN2\",r[r.COMPLEX_MULTIPLY_IMAG=2]=\"COMPLEX_MULTIPLY_IMAG\",r[r.COMPLEX_MULTIPLY_REAL=3]=\"COMPLEX_MULTIPLY_REAL\",r[r.DIV=4]=\"DIV\",r[r.ELU_DER=5]=\"ELU_DER\",r[r.EQUAL=6]=\"EQUAL\",r[r.FLOOR_DIV=7]=\"FLOOR_DIV\",r[r.GREATER=8]=\"GREATER\",r[r.GREATER_EQUAL=9]=\"GREATER_EQUAL\",r[r.LESS=10]=\"LESS\",r[r.LESS_EQUAL=11]=\"LESS_EQUAL\",r[r.LOGICAL_AND=12]=\"LOGICAL_AND\",r[r.LOGICAL_OR=13]=\"LOGICAL_OR\",r[r.MAX=14]=\"MAX\",r[r.MIN=15]=\"MIN\",r[r.MOD=16]=\"MOD\",r[r.MUL=17]=\"MUL\",r[r.NOT_EQUAL=18]=\"NOT_EQUAL\",r[r.POW=19]=\"POW\",r[r.PRELU=20]=\"PRELU\",r[r.SQUARED_DIFFERENCE=21]=\"SQUARED_DIFFERENCE\",r[r.SUB=22]=\"SUB\"})(fe||(fe={}));var Iae=\"let resultTemp = a + b;\",vae=\"let resultTemp = atan2(a, b);\",kae=\"let resultTemp = areal * breal - aimag * bimag;\",Nae=\"let resultTemp = areal * bimag + aimag * breal;\",Tae=\"let resultTemp = a / b;\",_ae=\"let resultTemp = select(a * (b + 1.0), a, b >= b - b);\",Eae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a == b);\n`,$ae=`\n let remainder =\n select(a % b, round(a % b), (round(a) == a) & (round(b) == b));\n let quotient = (a - remainder) / b;\n let resultTemp =\n round(select(quotient, quotient - 1, sign(remainder) == -sign(b)));\n`,Rae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a > b);\n`,Dae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a >= b);\n`,Aae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a < b);\n`,Fae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a <= b);\n`,Pae=\"return f32(a >= 1.0 && b >= 1.0);\",Oae=`return (vec4(a >= vec4(1.0)) *\n vec4(b >= vec4(1.0)));`,Mae=\"return f32(a >= 1.0 || b >= 1.0);\",Lae=`return min(vec4(a >= vec4(1.0)) +\n vec4(b >= vec4(1.0)), vec4(1.0));`,Bae=\"let resultTemp = max(a, b);\",zae=\"let resultTemp = min(a, b);\",Vae=`\n let isNaN = b == 0.;\n var resultTemp = a % b;\n resultTemp = select((resultTemp + b) % b, resultTemp,\n (a < 0. && b < 0.) || (a >= 0. && b > 0.));\n`,Wae=`\n let isNaN = !vec4(b);\n var resultTemp = vec4(a % b);\n if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {\n resultTemp[0] = (resultTemp[0] + b[0]) % b[0];\n }\n if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {\n resultTemp[1] = (resultTemp[1] + b[1]) % b[1];\n }\n if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {\n resultTemp[2] = (resultTemp[2] + b[2]) % b[2];\n }\n if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {\n resultTemp[3] = (resultTemp[3] + b[3]) % b[3];\n }\n`,Uae=\"let resultTemp = a * b;\",Gae=`\n var resultTemp = f32(a != b);\n let valueForNaN = 1.0;\n`,Hae=`\n var resultTemp = vec4(a != b);\n let valueForNaN = 1.0;\n`,Kae=`\n let isNaN = a < 0.0 && floor(b) < b;\n if (b == 0.0) {\n return 1.0;\n }\n var resultTemp = select(sign(a) * pow(abs(a), b), pow(abs(a), b),\n round(abs(b) % 2.0) != 1.0);\n`,qae=`\n let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1);\n let isModRound1 = vec4(isModRound1Bool);\n let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n var resultTemp = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n let isExpZero = b == vec4(0.0);\n if (isExpZero.r) {\n resultTemp.r = 1.0;\n }\n if (isExpZero.g) {\n resultTemp.g = 1.0;\n }\n if (isExpZero.b) {\n resultTemp.b = 1.0;\n }\n if (isExpZero.a) {\n resultTemp.a = 1.0;\n }\n let isNaN = (a < vec4(0.0)) & (floor(b) < b);\n`,jae=\"if (a < 0.0) { return b * a; } return a;\",Xae=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,Yae=\"let resultTemp = (a - b) * (a - b);\",Qae=\"let resultTemp = a - b;\";function Xc(r,e){let t;do{switch(r){case fe.ATAN2:t=vae;break;case fe.MAX:t=Bae;break;case fe.MIN:t=zae;break;case fe.MOD:t=e?Wae:Vae;break;case fe.NOT_EQUAL:t=e?Hae:Gae;break;case fe.POW:t=e?qae:Kae;break;default:continue}let o,n,s;return e?(o=\"isnanVec4\",n=\"vec4\",s=\"vec4\"):(o=\"isnan\",n=\"f32\",s=\"bool\"),`\n let aIsNaN = ${o}(a);\n let aPostLegalization = select(a, ${n}(42), aIsNaN);\n let bIsNaN = ${o}(b);\n let bPostLegalization = select(b, ${n}(42), bIsNaN);\n let isNaN = false;\n let valueForNaN = uniforms.NAN;\n {\n let a = aPostLegalization;\n let b = bPostLegalization;\n ${t}\n return select(\n resultTemp, ${n}(valueForNaN),\n ${s}(isNaN) | aIsNaN | bIsNaN);\n }\n `}while(!1);switch(r){case fe.ADD:t=Iae;break;case fe.COMPLEX_MULTIPLY_IMAG:t=Nae;break;case fe.COMPLEX_MULTIPLY_REAL:t=kae;break;case fe.DIV:t=Tae;break;case fe.ELU_DER:t=_ae;break;case fe.EQUAL:t=Eae;break;case fe.FLOOR_DIV:t=$ae;break;case fe.GREATER:t=Rae;break;case fe.GREATER_EQUAL:t=Dae;break;case fe.LESS:t=Aae;break;case fe.LESS_EQUAL:t=Fae;break;case fe.LOGICAL_AND:return e?Oae:Pae;case fe.LOGICAL_OR:return e?Lae:Mae;case fe.MUL:t=Uae;break;case fe.PRELU:return e?Xae:jae;case fe.SQUARED_DIFFERENCE:t=Yae;break;case fe.SUB:t=Qae;break;default:}return`\n ${t}\n return resultTemp;\n `}var Z;(function(r){r[r.ABS=0]=\"ABS\",r[r.ACOS=1]=\"ACOS\",r[r.ACOSH=2]=\"ACOSH\",r[r.ASIN=3]=\"ASIN\",r[r.ASINH=4]=\"ASINH\",r[r.ATAN=5]=\"ATAN\",r[r.ATANH=6]=\"ATANH\",r[r.CEIL=7]=\"CEIL\",r[r.COS=8]=\"COS\",r[r.COSH=9]=\"COSH\",r[r.ELU=10]=\"ELU\",r[r.ERF=11]=\"ERF\",r[r.EXP=12]=\"EXP\",r[r.EXPM1=13]=\"EXPM1\",r[r.FLOOR=14]=\"FLOOR\",r[r.IS_FINITE=15]=\"IS_FINITE\",r[r.IS_INF=16]=\"IS_INF\",r[r.IS_NAN=17]=\"IS_NAN\",r[r.LINEAR=18]=\"LINEAR\",r[r.LOG=19]=\"LOG\",r[r.LOG1P=20]=\"LOG1P\",r[r.LOGICAL_NOT=21]=\"LOGICAL_NOT\",r[r.NEG=22]=\"NEG\",r[r.RELU=23]=\"RELU\",r[r.RELU6=24]=\"RELU6\",r[r.LEAKYRELU=25]=\"LEAKYRELU\",r[r.RECIPROCAL=26]=\"RECIPROCAL\",r[r.ROUND=27]=\"ROUND\",r[r.RSQRT=28]=\"RSQRT\",r[r.SELU=29]=\"SELU\",r[r.SIGMOID=30]=\"SIGMOID\",r[r.SIGN=31]=\"SIGN\",r[r.SIN=32]=\"SIN\",r[r.SINH=33]=\"SINH\",r[r.SOFTPLUS=34]=\"SOFTPLUS\",r[r.SQRT=35]=\"SQRT\",r[r.SQUARE=36]=\"SQUARE\",r[r.STEP=37]=\"STEP\",r[r.TAN=38]=\"TAN\",r[r.TANH=39]=\"TANH\",r[r.TO_INT=40]=\"TO_INT\"})(Z||(Z={}));var Zae=\"return abs(a);\",Jae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return acos(a);\n`,eie=`\n if (a < 1.) {\n return uniforms.NAN;\n }\n return acosh(a);\n`,tie=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return asin(a);\n`,rie=\"return asinh(a);\",oie=`\n if (isnan(a)) {\n return uniforms.NAN;\n }\n return atan(a);\n`,nie=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n if (a == 1.) {\n return uniforms.INFINITY;\n }\n if (a == -1.) {\n return -uniforms.INFINITY;\n }\n return atanh(a);\n`,sie=\"return ceil(a);\",aie=\"return cos(a);\",iie=`\n let e2x = exp(-a);\n return (e2x + 1.0 / e2x) / 2.0;\n`,uie=\"return exp(a) - 1.0;\",pie=\"if (a >= 0.0) { return a; } return (exp(a) - 1.0);\",cie=`\n var resFloat = exp(a) - vec4(1.0);\n if (a.r >= 0.0) {\n resFloat.r = a.r;\n }\n if (a.g >= 0.0) {\n resFloat.g = a.g;\n }\n if (a.b >= 0.0) {\n resFloat.b = a.b;\n }\n if (a.a >= 0.0) {\n resFloat.a = a.a;\n }\n return resFloat;\n`,lie=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n let p = ${w.ERF_P};\n let a1 = ${w.ERF_A1};\n let a2 = ${w.ERF_A2};\n let a3 = ${w.ERF_A3};\n let a4 = ${w.ERF_A4};\n let a5 = ${w.ERF_A5};\n\n let sign = sign(a);\n let absA = abs(a);\n let t = 1.0 / (1.0 + p * absA);\n return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));\n`,mie=\"return exp(a);\",die=\"return floor(a);\",fie=\"return f32(!isnan(a) && !isinf(a));\",hie=\"return f32(isinf(a));\",gie=\"return f32(isnan(a));\",xie=\"return a;\",yie=`if (a < 0.0) { return uniforms.NAN; }\n return log(a);`,bie=`\n if (isnan(a)) { return a; }\n return log(1.0 + a);\n`,Cie=\"return f32(!(a >= 1.0));\",wie=\"return -a;\",Sie=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",Iie=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,vie=\"return 1.0 / a;\",kie=\"return select(a, 0.0, a < 0.0);\",Nie=\"return clamp(a, 0.0, 6.0);\",Tie=\"return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));\",_ie=`\n return select(a, vec4(0.0), a < vec4(0.0));\n`,Eie=\"return round(a);\",$ie=\"return inverseSqrt(a);\",Rie=`\n if (a >= 0.0) {\n return ${w.SELU_SCALE} * a;\n } else {\n return ${w.SELU_SCALEALPHA} * (exp(a) - 1.0);\n }\n`,Die=\"return 1.0 / (1.0 + exp(-1.0 * a));\",Aie=\"return sign(a);\",Fie=\"return sin(a);\",Pie=`\n let e2x = exp(a);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Oie=`\n let epsilon = 1.1920928955078125e-7;\n let threshold = log(epsilon) + 2.0;\n\n let too_large = a > -threshold;\n let too_small = a < threshold;\n let exp_a = exp(a);\n\n if (too_large) {\n return a;\n } else if (too_small) {\n return exp_a;\n } else {\n return log(exp_a + 1.0);\n }\n`,Mie=\"return sqrt(a);\",Lie=\"return a * a;\",Bie=`\n if (isnan(a)) {\n return a;\n }\n\n return select(uniforms.stepAlpha, 1.0, a > 0.0);\n`,zie=\"return tan(a);\",Vie=`\n let e2x = exp(-2.0 * abs(a));\n return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,Wie=\"return f32(i32((a)));\";function Si(r,e){switch(r){case Z.ABS:return Zae;case Z.ACOS:return Jae;case Z.ACOSH:return eie;case Z.ASIN:return tie;case Z.ASINH:return rie;case Z.ATAN:return oie;case Z.ATANH:return nie;case Z.COS:return aie;case Z.COSH:return iie;case Z.CEIL:return sie;case Z.ELU:return e?cie:pie;case Z.ERF:return lie;case Z.EXP:return mie;case Z.EXPM1:return uie;case Z.FLOOR:return die;case Z.IS_FINITE:return fie;case Z.IS_INF:return hie;case Z.IS_NAN:return gie;case Z.LINEAR:return xie;case Z.LOG:return yie;case Z.LOG1P:return bie;case Z.LOGICAL_NOT:return Cie;case Z.NEG:return wie;case Z.LEAKYRELU:return e?Iie:Sie;case Z.RECIPROCAL:return vie;case Z.RELU:return e?_ie:kie;case Z.RELU6:return e?Tie:Nie;case Z.ROUND:return Eie;case Z.RSQRT:return $ie;case Z.SELU:return Rie;case Z.SIGMOID:return Die;case Z.SIGN:return Aie;case Z.SIN:return Fie;case Z.SINH:return Pie;case Z.SOFTPLUS:return Oie;case Z.SQRT:return Mie;case Z.SQUARE:return Lie;case Z.STEP:return Bie;case Z.TAN:return zie;case Z.TANH:return Vie;case Z.TO_INT:return Wie;default:throw new Error(`BinaryType ${r} is not implemented!`)}}function dr(r,e=!1,t=!1,o=3){if(r===null)return\"\";let n=\"\";if(r===\"linear\")n=Si(Z.LINEAR);else if(r===\"relu\")n=Si(Z.RELU,t);else if(r===\"elu\")n=Si(Z.ELU,t);else if(r===\"relu6\")n=Si(Z.RELU6,t);else if(r===\"prelu\")n=Xc(fe.PRELU,t);else if(r===\"sigmoid\")n=Si(Z.SIGMOID,t);else if(r===\"leakyrelu\")n=Si(Z.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=Ae(t?4:1),i=\"\";return e?i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n let b = getPreluActivationWeightsByOutputCoords(coords);\n ${n}\n }`:i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n ${n}\n }`,i}function Zr(r,e){return`\n ${r?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\n ${e?\"value = activation(value, coords);\":\"\"}\n `}function Jv(r,e,t=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=`\n ${r?\"value = getA(batch, col, row);\":\"value = getA(batch, row, col);\"}\n\n `,i=e?\"value = getB(batch, col, row);\":\"value = getB(batch, row, col);\";return`\n fn mm_readA(batch: i32, row: i32, col: i32) -> ${Ae(s)} {\n var value = ${Ae(s)}(0.0);\n ${t&&n?a:`\n ${r?\"if(row < uniforms.dimAOuter && col < uniforms.dimInner)\":\"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])\"}\n {\n ${a}\n }\n `}\n return value;\n }\n\n fn mm_readB(batch: i32, row: i32, col: i32) -> ${Ae(s)} {\n var value = ${Ae(s)}(0.0);\n ${i}\n return value;\n }\n `}function hm(r,e,t,o,n=!1,s=!1,a=!1,i=1){return`\n ${Jv(t,o,n,s,a,i)}\n fn mm_write(batch: i32, row: i32, col: i32, valueIn: ${Ae(i)}) {\n ${n&&s?\"\":\"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n {\n var value = valueIn;\n let coords = vec3(batch, row, col);\n ${Zr(r,e)}\n setOutputAtCoords(coords[0], coords[1], coords[2], value);\n }\n }\n `}var Uie=(r,e)=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart + inputCol * ${e});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRow + innerRow,\n kStart + inputCol * ${e});\n `,Gie=(r,e,t,o)=>{if(r)return`\n for (var k = 0; k < ${o}; k++) {\n let BCached0 = mm_Bsub[k][tileCol];\n let ACached0 = mm_Asub[k][localRow];\n for (var i = 0; i < ${t}; i++) {\n acc[i] = fma(BCached0, vec4(ACached0[i]), acc[i]);\n }\n }`;{let n=\"\",s=\"\";for(let a=0;a(ACached[${a}]), acc[i]);`;return`\n for (var k = 0; k < ${o/e}; k++) {\n ${n}\n for (var i = 0; i < ${t}; i++) {\n let ACached = mm_Asub[tileRow + i][k];\n ${s}\n }\n }`}};function _p(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=e[1]*r[1],p=e[0]*r[0],u=t?i:o,c=t?o:i,l=u/e[0],m=o/e[1],d=r[1],f=r[0];return y.assert((t&&l===4&&r[1]===4||!t&&(l===3||l===4))&&u%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4.\n Otherwise, innerElementSize ${l} must be 3 or 4.\n tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),`\n var mm_Asub : array, ${u/l}>, ${c}>;\n var mm_Bsub : array, ${p/r[0]}>, ${o}>;\n\n ${G()} {\n let localRow = i32(localId.y);\n let tileRow = localRow * ${d};\n let tileCol = i32(localId.x);\n\n let globalRow = i32(globalId.y) * ${d};\n let globalCol = i32(globalId.x) * ${f};\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!a?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!a?\"batch\":\"batch % uniforms.bShape[0]\"};\n let globalRowStart = i32(workgroupId.y) * ${i};\n\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc: array, ${d}>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${m};\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${Uie(t,l)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol);\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n ${Gie(t,l,d,o)}\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n }`}var az=r=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRowStart + inputRow,\n kStart + inputCol);\n `,Hie=r=>r?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function Ep(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*e[1],u=r[0]*e[0],c=t?p:o,l=t?o:p;y.assert(l%e[1]===0&&c%e[0]===0&&o%e[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let m=l/e[1],d=c/e[0],f=o/e[1],h=r[1],g=r[0],x=a?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${p};\n let globalColStart = i32(workgroupId.x) * ${u};\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) {\n ${az(t)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalColStart + inputCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}];\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let ACached = ${t?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let gRow = globalRowStart + localRow + innerRow * ${e[1]};\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let gCol = globalColStart + localCol + innerCol * ${e[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\n let tileRow = i32(localId.y) * ${h};\n let tileCol = i32(localId.x) * ${g};\n\n let globalRow = i32(globalId.y) * ${h};\n let globalCol = i32(globalId.x) * ${g};\n let globalRowStart = i32(workgroupId.y) * ${p};\n\n let tileRowA = i32(localId.y) * ${m};\n let tileColA = i32(localId.x) * ${d};\n let tileRowB = i32(localId.y) * ${f};\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n for (var innerCol = 0; innerCol < ${d}; innerCol++) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${az(t)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalCol + innerCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n ${Hie(t)}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n }\n `;return`\n var mm_Asub : array, ${l}>;\n var mm_Bsub : array, ${o}>;\n\n ${G()} {\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!i?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!i?\"batch\":\"batch % uniforms.bShape[0]\"};\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc : array, ${h}>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${x}\n }\n `}var Kie=r=>r?`\n mm_readA(batchA, colA, globalRow),\n mm_readA(batchA, colA + 1, globalRow),\n mm_readA(batchA, colA + 2, globalRow),\n mm_readA(batchA, colA + 3, globalRow)\n `:`\n mm_readA(batchA, globalRow, colA),\n mm_readA(batchA, globalRow, colA + 1),\n mm_readA(batchA, globalRow, colA + 2),\n mm_readA(batchA, globalRow, colA + 3)\n `;function qie(r,e=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let t=r[0]*4;return`\n var mm_Asub : array, ${r[0]}>;\n\n ${G()} {\n let tileCol = i32(localId.x);\n let globalCol = i32(globalId.x);\n let globalRow = i32(globalId.y);\n\n let numTiles = (uniforms.dimInner - 1) / ${t} + 1;\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n // Without this initialization strange values show up in acc.\n var acc = 0.0;\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n let colA = t * ${t} + tileCol * 4;\n mm_Asub[tileCol] = vec4(${Kie(e)});\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < ${t/4}; k++) {\n let rowB = t * ${t} + k * 4;\n let BCached = vec4(mm_readB(batchB, rowB, globalCol),\n mm_readB(batchB, rowB + 1, globalCol),\n mm_readB(batchB, rowB + 2, globalCol),\n mm_readB(batchB, rowB + 3, globalCol));\n\n let ACached = mm_Asub[k];\n acc = acc + dot(ACached, BCached);\n }\n\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var Xg=class{constructor(e,t,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?e[1]:e[2];if(this.isVec4=(u%4===0&&!o||t[1]%4===0&&o)&&t[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=t[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=Qv(t[1],u,t[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push(\"bias\"),l&&this.variableNames.push(\"preluActivationWeights\"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights,this.isVec4)}\n ${hm(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}\n ${this.isVec4?_p(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?qie(this.workgroupSize,this.transposeA):Ep(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)}\n `}};function jie(r){return`\n var sumValues : array;\n ${G()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n let row = coords[1];\n let col = coords[2];\n var sum = 0.0;\n let Length = uniforms.dimInner;\n for (var k = i32(localId.x); k < Length; k = k + ${r}) {\n let dataA = mm_readA(batchA, row, k);\n let dataB = mm_readB(batchB, k, col);\n sum = sum + dataA * dataB;\n }\n sumValues[localId.x] = sum;\n workgroupBarrier();\n\n for(var currentSize = ${r/2}u; currentSize > 1u;\n currentSize = currentSize / 2u) {\n if (localId.x < currentSize)\n {\n sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n }\n workgroupBarrier();\n }\n\n if (localId.x == 0u) {\n sum = sumValues[0] + sumValues[1];\n mm_write(batch, row, col, sum);\n }\n }\n `}var Yg=class{constructor(e,t=!1,o=!1,n=null,s=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=t,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${t}_${o}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${hm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${jie(this.workgroupSize[0])}\n `}};function Xie(r){let e=r[1],t=r[0],o=e>t?e:t;return`\n var mm_Asub : array, ${e}>;\n var mm_Bsub : array, ${o}>;\n\n // If the output size is small for matrix multiplication, avoid to use vec4\n // and handle some elements per thread to optimally utilize the ALU.\n // Read data from global memory to registers firstly, then store them into\n // shared memory, so it is instruction-Level parallelism for arithmetic\n // operations and others handle IO operations between barrier api, makes ALU\n // and load/store units work simultaneously, could improves the performance.\n ${G()} {\n let tileRow = i32(localId.y);\n let tileCol = i32(localId.x);\n let globalRow = i32(globalId.y);\n let globalCol = i32(globalId.x);\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n\n // uniforms.dimInner should be greater than 0.\n let numTiles = (uniforms.dimInner - 1) / ${o} + 1;\n var acc = 0.0;\n\n var globalColA = tileCol;\n var globalRowB = 0;\n var regA = mm_readA(batchA, globalRow, globalColA);\n var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var t = 0; t < numTiles; t = t + 1) {\n mm_Asub[tileRow][tileCol] = regA;\n mm_Bsub[2 * tileRow][tileCol] = regB0;\n mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n workgroupBarrier();\n\n regA = mm_readA(batchA, globalRow, globalColA);\n regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var k = 0; k < ${o}; k = k + 1) {\n acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n }\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var Qg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push(\"bias\");let c=p!=null;c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${hm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${Xie(this.workgroupSize)}\n `}};var Zg=class{constructor(e,t,o=!1,n=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,y.assert(e[0]===1,()=>\"MatMulSplitKProgram only supports batch = 1.\"),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let s=(o&&this.outputShape[1]%4===0||!o&&t%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=s?4:1,s||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=H(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let e=this.outputComponent;return`\n ${Jv(!1,this.transposeB,!1,!1,!1,e)}\n fn mm_write(batch: i32, row : i32, col : i32, value : ${Ae(e)}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n let coords = vec3(batch, row, col);\n let flatIndex = getOutputIndexFromCoords(coords);\n // The problem is that we should initialize output to zero before using.\n // Otherwise, the original value will be added to the result.\n for (var i = 0; i < ${e}; i = i + 1) {\n ${Qr(\"&result[flatIndex + i]\",`${e>1?\"value[i]\":\"value\"}`,\"float32\")}\n }\n }\n }\n ${e===4?_p(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Ep(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}\n `}},Jg=class{constructor(e,t=null,o=null,n=null){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var value = getXByOutputIndex(index);\n ${Zr(this.addBias,this.activation)}\n setOutputAtIndex(index, value);\n }\n }\n `}};var ex=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.value);\n }\n }\n `}};function vt(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new ex(o),i=[{type:\"float32\",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var iz={kernelName:sa,backendName:\"webgpu\",kernelFunc:vt};function pe(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var uz={kernelName:da,backendName:\"webgpu\",kernelFunc:pe};function $p({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),S=Sr.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=pe({inputs:{x:r},backend:n,attrs:{shape:k}}),R=pe({inputs:{x:e},backend:n,attrs:{shape:_}}),D=[$,R],P=Math.max(x,b),O=[$,R],M=[{type:\"int32\",data:[d]},{type:\"int32\",data:[f]},{type:\"int32\",data:[l]}],L,B,z=[P,d,f],U=A().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");if(U<0){let q=A().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),Y=q>0?q:n.thresholdToIncreaseWorkgroups,J=P*Math.ceil(d/32)*Math.ceil(f/32);J<=Y||d<=8&&J<=Y*2?P*d*f<=128?U=Mo.MatMulReduceProgram:P===1&&m>=2e3?U=Mo.MatMulSplitKProgram:U=Mo.MatMulSmallOutputSizeProgram:U=Mo.MatMulPackedProgram}switch(U){case Mo.MatMulReduceProgram:L=new Yg(z,t,o,s,p,a);break;case Mo.MatMulSplitKProgram:{if(B=vt({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Zg(z,m,t,o),s||p){B=n.runWebGPUProgram(L,O,r.dtype,M,B);let Y=new Jg(B.shape,s,p,a),J=null,re=[B];s&&re.push(s),a&&re.push(a),p===\"leakyrelu\"&&(J=[{type:\"float32\",data:[i]}],Y.uniforms+=\" alpha : f32,\");let ne=n.runWebGPUProgram(Y,re,B.dtype,J);D.push(B);let ee=pe({inputs:{x:ne},backend:n,attrs:{shape:S}});D.push(ne);for(let oe of D)n.disposeData(oe.dataId);return ee}break}case Mo.MatMulSmallOutputSizeProgram:L=new Qg(k,_,z,t,o,s,p,a);break;case Mo.MatMulPackedProgram:let q=n.adapterInfo.isIntel();L=new Xg(k,z,t,o,s,p,a,q);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&O.push(s),a&&O.push(a),p===\"leakyrelu\"&&(M.push({type:\"float32\",data:[i]}),L.uniforms+=\" alpha : f32,\"),B=n.runWebGPUProgram(L,O,r.dtype,M,B);let j=pe({inputs:{x:B},backend:n,attrs:{shape:S}});D.push(B);for(let q of D)n.disposeData(q.dataId);return j}function Yie(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return $p({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var pz={kernelName:So,backendName:\"webgpu\",kernelFunc:Yie};var gm=class{constructor(e,t,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=w.assertAndGetBroadcastShape(t,o),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n fn binaryOpComplex(\n areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n ${Xc(this.op,!1)}\n }\n\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let areal = getARealByOutputIndex(index);\n let aimag = getAImagByOutputIndex(index);\n let breal = getBRealByOutputIndex(index);\n let bimag = getBImagByOutputIndex(index);\n setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n }\n }\n `}};var Ii=class{constructor(e,t,o){if(this.size=!0,this.variableNames=[\"A\",\"B\"],this.outputShape=w.assertAndGetBroadcastShape(t,o),this.dispatchLayout=X(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${e}_${this.lastDimensionSize}`,this.type=\"shared\",this.workgroupSize=[256,1,1];else{let n=t.length>0&&t[t.length-1]%4===0,s=o.length>0&&o[o.length-1]%4===0;n&&s?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(y.isScalarShape(o)||o[o.length-1]===1)||s&&(y.isScalarShape(t)||t[t.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type=\"nonshared\",this.shaderKey=`binary_${e}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let e,t=this.outputComponent===4?\"vec4\":\"f32\",o=`\n fn binaryOperation(a : ${t}, b : ${t}) -> ${t} {\n ${Xc(this.op,this.outputComponent===4)}\n };\n `;if(this.type===\"shared\"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index);\n let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}];\n let b = getBByOutputIndex(index);`;e=`\n ${o}\n var sharedBuf : array;\n ${G(\"index\")} {\n // Fill in the shared memory buffer.\n let localIndex = i32(localId.x);\n if(localIndex < ${this.lastDimensionSize}) {\n sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n }\n workgroupBarrier();\n\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n ${s}\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `}else e=`\n ${o}\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index * ${this.outputComponent});\n let a = ${t}(getAByOutputCoords(coords));\n let b = ${t}(getBByOutputCoords(coords));\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `;return e}};function At(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var cz={kernelName:Co,backendName:\"webgpu\",kernelFunc:At};function xo(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.tensorMap.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var lz={kernelName:Di,backendName:\"webgpu\",kernelFunc:xo};var Jr=class{constructor(e,t,o=\"\"){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,o!==\"\"&&(this.uniforms=o),this.shaderKey=`unary_${t}`}getUserCode(){return`\n fn unaryOperation(a : f32) -> f32 {\n ${Si(this.op,!1)}\n }\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n setOutputAtIndex(index, unaryOperation(a));\n }\n }\n `}};function ye({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Jr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function et({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype===\"complex64\"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==fe.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},S={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Ii(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,S],dt(x.dtype,b.dtype))});else{let g=new gm(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new gm(fe.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,\"float32\"),f=p.runWebGPUProgram(x,b,\"float32\")}let h=xo({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype===\"string\"||i.dtype===\"string\"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype===\"string\"?w.fromUint8ToStringArray(l):l,f=a.dtype===\"string\"?w.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Ii(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:mz,castImpl:dz,ceilImpl:fz,concatImpl:hz,equalImpl:gz,expImpl:xz,expm1Impl:yz,floorImpl:bz,floorDivImpl:Cz,gatherNdImpl:wz,gatherV2Impl:Sz,greaterEqualImpl:Iz,greaterImpl:vz,lessEqualImpl:kz,lessImpl:Nz,logImpl:Tz,maxImpl:_z,maximumImpl:Ez,minimumImpl:$z,multiplyImpl:Rz,negImpl:Dz,notEqualImpl:Az,prodImpl:Fz,rangeImpl:Pz,rsqrtImpl:Oz,scatterImpl:Mz,simpleAbsImpl:Lz,sliceImpl:Bz,stridedSliceImpl:zz,stringNGramsImpl:Vz,subImpl:Wz,tileImpl:Uz,topKImpl:Gz,transposeImpl:Hz,uniqueImpl:rOt}=Ic;var Qie=ye({opType:Z.ABS,cpuKernelImpl:Lz}),Kz={kernelName:Xs,backendName:\"webgpu\",kernelFunc:Qie};var Zie=ye({opType:Z.ACOS}),qz={kernelName:Vo,backendName:\"webgpu\",kernelFunc:Zie};var Jie=ye({opType:Z.ACOSH}),jz={kernelName:Wo,backendName:\"webgpu\",kernelFunc:Jie};var eue=et({opType:fe.ADD,cpuKernelImpl:mz,supportsComplex:!0}),Xz={kernelName:uo,backendName:\"webgpu\",kernelFunc:eue};var tx=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(\" + \");return`\n ${G(\"index\")} {\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let flatIndex = index * ${this.workPerThread} + i;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n ${e.join(`\n `)}\n setOutputAtIndex(flatIndex, ${t});\n }\n }\n }\n `}};function tue(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new tx(s);return t.runWebGPUProgram(a,o,n)}var Yz={kernelName:Uo,backendName:\"webgpu\",kernelFunc:tue};var rx=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return`\n var tile : array, ${this.workgroupSize[0]}>;\n ${G()} {\n var x = i32(workgroupId.x) * ${e} + i32(localId.x);\n var y = i32(workgroupId.y) * ${e} + i32(localId.y);\n let width = uniforms.outShape[0];\n let height = uniforms.outShape[1];\n if (x < width && y < height) {\n tile[localId.y][localId.x] = f32(A[y * width + x]);\n }\n workgroupBarrier();\n\n x = i32(workgroupId.y) * ${e} + i32(localId.x);\n y = i32(workgroupId.x) * ${e} + i32(localId.y);\n if (x < height && y < width) {\n setOutputAtIndex((y * height + x), tile[localId.x]\n [localId.y]);\n }\n }\n `}};var ox=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o=32768&&o>=512?this.workgroupSize=[512,1,1]:e.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\",o=this.workgroupSize[0];this.reduceType===\"min\"||this.reduceType===\"max\"?(e=`\n if (isnan(candidate)) {\n bestValue = uniforms.NAN;\n } else if (!isnan(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n { bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"?(e=\" bestValue = bestValue * candidate; \",t=\"1.0\"):this.reduceType===\"all\"?(e=\" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); \",t=\"1.0\"):this.reduceType===\"any\"&&(e=\" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); \",t=\"0.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n fn DIV_CEIL(a : u32, b : u32) -> u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestValues : array;\n `}\n fn getOffset(outputIndex : i32) -> i32 {\n let outputCoords = getCoordsFromIndex(outputIndex);\n let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n return offset;\n }\n ${G(\"index\")} {\n let outputIndex = index / ${o};\n let offset = getOffset(outputIndex);\n var bestValue = ${t};\n let Length = uniforms.reduceSize;\n let WorkPerThread = DIV_CEIL(u32(Length), ${o}u);\n for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n k = k + ${o}) {\n let candidate = f32(x[offset + k]);\n ${e}\n }\n xBestValues[localId.x] = bestValue;\n workgroupBarrier();\n\n var reduceSize = min(u32(Length), ${o}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n ${e}\n xBestValues[localId.x] = bestValue;\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n ${n}\n }\n }\n `}};var rue={mean:\"float32\",all:\"bool\",any:\"bool\"};function eo(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=w.getAxesPermutation(p,s),c=r;u!=null&&(c=xr({inputs:{x:r},attrs:{perm:u},backend:n}),p=w.getInnerMostAxes(p.length,s),a.push(c)),w.assertAxesAreInnerMostDims(o,p,s);let[l,m]=w.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=w.expandShapeToKeepDim(l,i));let f;if((o===\"max\"||o===\"prod\")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case\"max\":let g=_z(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case\"prod\":let{outVals:x,outShape:b,outDtype:C}=Fz(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=rue[o]||oi(r.dtype),S=[{type:\"int32\",data:[h]}],k=new nx(b,o,n.device.limits.maxComputeWorkgroupSizeX),_=n.runWebGPUProgram(k,[c],C,S);a.push(_),f=pe({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function oue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,\"all\",t)}var Zz={kernelName:Go,backendName:\"webgpu\",kernelFunc:oue};function nue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,\"any\",t)}var Jz={kernelName:Ho,backendName:\"webgpu\",kernelFunc:nue};var Yc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let n=[t];this.op=o===\"min\"?\"<\":\">\";let[s,a]=w.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=X(this.outputShape),y.sizeFromShape(a)<32?(this.type=\"plain\",this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type=\"shared\",this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${Oo(this.inputShape.length-1)}`,o=()=>{let n=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+=\"outputCoords,\");else for(let s=0;s u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestIndices : array;\n var xBestValues : array;\n `}\n\n ${G(\"index\")} {\n let outputIndex = index / ${e};\n let reduceLength = ${t()};\n\n var bestIndex = i32(localId.x);\n var bestValue = uniforms.infinityValue;\n let outputCoords = getCoordsFromIndex(outputIndex);\n for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n k = k + ${e}) {\n let candidate = getX(${o()} k);\n if (!isnan(candidate) && candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = k;\n }\n }\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = bestIndex;\n workgroupBarrier();\n\n var reduceSize = min(u32(reduceLength), ${e}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n }\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n }\n }\n `:`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outputCoords = getCoordsFromIndex(index);\n var bestIndex = 0;\n var bestValue = getX(${o()} 0);\n let reduceLength = ${t()};\n for (var i = 1; i < reduceLength; i++) {\n let candidate = getX(${o()} i);\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = i;\n }\n }\n setOutputAtIndexI32(index, bestIndex);\n }\n }\n `}};function sue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=new Yc(p.shape,a[0],\"max\"),l=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var eV={kernelName:Ys,backendName:\"webgpu\",kernelFunc:sue};function aue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=new Yc(p.shape,a[0],\"min\"),l=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var tV={kernelName:Qs,backendName:\"webgpu\",kernelFunc:aue};var iue=ye({opType:Z.ASIN}),rV={kernelName:Ko,backendName:\"webgpu\",kernelFunc:iue};var uue=ye({opType:Z.ASINH}),oV={kernelName:qo,backendName:\"webgpu\",kernelFunc:uue};var pue=ye({opType:Z.ATAN}),nV={kernelName:jo,backendName:\"webgpu\",kernelFunc:pue};var cue=et({opType:fe.ATAN2}),sV={kernelName:Yo,backendName:\"webgpu\",kernelFunc:cue};var lue=ye({opType:Z.ATANH}),aV={kernelName:Xo,backendName:\"webgpu\",kernelFunc:lue};var sx=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"strides : vec2,\",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let xRCCorner = coords.yz * uniforms.strides;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n let value = getX(batch, xRCorner, xCCorner, d);\n setOutputAtIndex(index, value);\n }\n }\n `}};var Ba=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,\",this.workgroupSize=[128,1,1],this.size=!0,t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool2D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType===\"avg\"?e=\"resultValue = resultValue + value; count = count + 1.0;\":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"((batch * uniforms.xShape[1] + xR) * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"(xR * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"wR * uniforms.filterDims.y + wC\"};\n }`:e=\"resultValue = max(value, resultValue);\";let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / max(count, 1.0)\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n let xRCCorner = vec2(coords.yz) * uniforms.strides - uniforms.pads;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilations.x) {\n let xR = xRCorner + wR;\n\n if (xR < 0 || xR >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilations.y) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.y) {\n continue;\n }\n\n let value = getX(batch, xR, xC, d);\n ${e}\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${t});`}\n }\n }\n `}},Iu=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,\",this.workgroupSize=[128,1,1],this.size=!0,t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool3D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType===\"avg\"?e=\"resultValue += value; count += 1.0;\":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"(((batch * uniforms.xShape.y + xD) * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"((xD * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"wD * uniforms.filterDims.y * uniforms.filterDims.y + wR * uniforms.filterDims.z + wC\"};\n }`:e=\"resultValue = max(value, resultValue);\";let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / max(count, 1.0)\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let xCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xDCorner = xCorner.x;\n let xRCorner = xCorner.y;\n let xCCorner = xCorner.z;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wD = 0; wD < uniforms.filterDims.x; wD++) {\n let xD = xDCorner + wD;\n if (xD < 0 || xD >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims.y; wR++) {\n let xR = xRCorner + wR;\n if (xR < 0 || xR >= uniforms.convDims.y) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.z; wC++) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.z) {\n continue;\n }\n\n let value = getX(batch, xD, xR, xC, ch);\n ${e}\n }\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${t});`}\n }\n }\n `}};function t0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return eo(n,s,a,\"max\",t)}var iV={kernelName:zn,backendName:\"webgpu\",kernelFunc:t0};function r0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,\"mean\",t)}var uV={kernelName:Un,backendName:\"webgpu\",kernelFunc:r0};function ax(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return At({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type===\"VALID\"){let a=r.shape.length,i=pe({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t===\"avg\"?p=r0({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),p=t0({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=pe({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:\"int32\",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new sx(e):(t===\"avg\"?n=new Ba(e,\"avg\"):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),n=new Ba(e,\"max\")),s.push({type:\"int32\",data:[e.padInfo.top,e.padInfo.left]},{type:\"int32\",data:[e.dilationHeight,e.dilationWidth]},{type:\"int32\",data:[e.inHeight,e.inWidth]},{type:\"int32\",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function mue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,c=w.computePool2DInfo(n.shape,s,a,1,i,p);return ax(n,c,\"avg\",t)}var pV={kernelName:Qo,backendName:\"webgpu\",kernelFunc:mue};function due(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Iu(l,\"avg\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var cV={kernelName:Zs,backendName:\"webgpu\",kernelFunc:due};var ix=class{constructor(e){this.variableNames=[\"dy\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool2DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n\n dotProd = dotProd + dyValue * uniforms.avgMultiplier;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},ux=class{constructor(e){this.variableNames=[\"dy\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool3DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n dotProd += dyValue * uniforms.avgMultiplier;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function fue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=w.computePool3DInfo(a.shape,i,p,1,u,c),m=new ux(l),d=1/(l.filterDepth*l.filterHeight*l.filterWidth),f=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.effectiveFilterDepth-1-l.padInfo.front,l.effectiveFilterHeight-1-l.padInfo.top,l.effectiveFilterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]},{type:\"int32\",data:[l.outDepth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"float32\",data:[d]}];return t.runWebGPUProgram(m,[n],a.dtype,f)}var lV={kernelName:Ri,backendName:\"webgpu\",kernelFunc:fue};function hue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;fm([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new ix(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:\"int32\",data:[c.outHeight]},{type:\"int32\",data:[c.outWidth]},{type:\"float32\",data:[m]}];return t.runWebGPUProgram(l,[n],a.dtype,d)}var mV={kernelName:$i,backendName:\"webgpu\",kernelFunc:hue};function gue(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return $p({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var dV={kernelName:Zo,backendName:\"webgpu\",kernelFunc:gue};var px=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${ft(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=ft(this.rank),t=xue(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>\"sourceLoc = uniforms.start + coords;\"):o=this.outputShape.map((s,a)=>`sourceLoc.${o0[a]} = uniforms.start.${Oo(a)} + coords.${o0[a]};`),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var sourceLoc : ${e};\n let coords = getCoordsFromIndex(index);\n ${o.join(`\n`)}\n setOutputAtIndex(index, getSource(${t}));\n }\n }\n `}},o0=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function xue(r){if(r===1)return\"sourceLoc\";if(r<=6)return o0.slice(0,r).map(e=>`sourceLoc.${e}`).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}function Hs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=pt.parseSliceParams(n,s,a);if(pt.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.tensorMap.get(n.dataId),m=Bz(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new px(i,p),c=[{type:\"int32\",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var fV={kernelName:ha,backendName:\"webgpu\",kernelFunc:Hs};var yue=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),h=xr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Hs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},hV={kernelName:Js,backendName:\"webgpu\",kernelFunc:yue};var bue=`\n fn bincount_write(index: i32, value: f32) {\n ${Qr(\"&result[index]\",\"value\",\"float32\")}\n }\n`,Cue=`\n fn bincount_write(index: i32, value: f32) {\n atomicStore(&result[index], bitcast(value));\n }\n`,Qc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"binCountSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push(\"w\"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`\n ${this.binaryOutput?Cue:bue}\n ${G(\"index\")} {\n ${this.rank===1?`if (index < uniforms.xShape) {\n let indexVal = i32(getX(index));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(index)\":\"1.\"};\n bincount_write(indexVal, value);\n }\n }`:`let coord = getCoordsFromIndex(index);\n if (coordsInBounds2D(coord, uniforms.xShape)) {\n let indexVal = i32(getX(coord[0], coord[1]));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(coord[0], coord[1])\":\"1.\"};\n bincount_write(coord.x * uniforms.binCountSize + indexVal, value);\n }\n }`}\n }\n `}};function wue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=vt({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new Qc([i],u),f=[{type:\"int32\",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var gV={kernelName:Jo,backendName:\"webgpu\",kernelFunc:wue};var cx=class{constructor(e){this.outputShape=[],this.variableNames=[\"s0\",\"s1\"],this.uniforms=\"s0Size : i32, s1Size : i32, \",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"broadcastArgs\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var s0 = 1.0;\n var s1 = 1.0;\n let indexS0 = index - uniforms.size + uniforms.s0Size;\n let indexS1 = index - uniforms.size + uniforms.s1Size;\n if (indexS0 >= 0) {\n s0 = getS0(indexS0);\n }\n if (indexS1 >= 0) {\n s1 = getS1(indexS1);\n }\n\n if (s0 == 1.0) {\n setOutputAtIndex(index, s1);\n } else if (s1 == 1.0) {\n setOutputAtIndex(index, s0);\n } else if (s0 != s1) {\n setOutputAtIndex(index, uniforms.NAN);\n } else {\n setOutputAtIndex(index, s0);\n }\n }\n }\n `}};function Sue(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e;if(t.shouldExecuteOnCPU([o,n])){let c=t.tensorMap.get(o.dataId),l=t.tensorMap.get(n.dataId),m=c.values,d=l.values,f=w.assertAndGetBroadcastShape(Array.from(m),Array.from(d));return t.makeTensorInfo([f.length],\"int32\",Int32Array.from(f))}let s=y.sizeFromShape(o.shape),a=y.sizeFromShape(n.shape),i=Math.max(s,a),p=new cx(i),u=[{type:\"int32\",data:[s]},{type:\"int32\",data:[a]}];return t.runWebGPUProgram(p,[o,n],\"int32\",u)}var xV={kernelName:ea,backendName:\"webgpu\",kernelFunc:Sue};var n0=et({opType:fe.NOT_EQUAL,dtype:\"bool\",cpuKernelImpl:Az}),yV={kernelName:Yn,backendName:\"webgpu\",kernelFunc:n0};function vi(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.real},backend:t})}var bV={kernelName:Hi,backendName:\"webgpu\",kernelFunc:vi};function CV(r,e){let t=new Jr(r.shape,Z.TO_INT),o=e.runWebGPUProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function s0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return At({inputs:{x:n},backend:t});let a=Gr(n.shape),i=s0({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=xo({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype===\"complex64\"){let a=vi({inputs:{input:n},backend:t}),i=s0({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=dz(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return CV(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=n0({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var wV={kernelName:yo,backendName:\"webgpu\",kernelFunc:s0};var Iue=ye({opType:Z.CEIL,cpuKernelImpl:fz}),SV={kernelName:en,backendName:\"webgpu\",kernelFunc:Iue};var lx=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n var clampedValue = clamp(\n value, vec4(uniforms.minVal), vec4(uniforms.maxVal));\n clampedValue = select(clampedValue, value, isnanVec4(value));\n setOutputAtIndex(index, clampedValue);\n }\n }\n `}};var mx=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n if (isnan(value)) {\n setOutputAtIndex(index, value);\n return;\n }\n setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n }\n }\n `}};function vue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:\"float32\",data:[s]},{type:\"float32\",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new lx(n.shape):i=new mx(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var IV={kernelName:bo,backendName:\"webgpu\",kernelFunc:vue};var dx=class{constructor(e){this.outputShape=[],this.variableNames=[\"real\",\"imag\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"complexAbs\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let re = abs(getRealByOutputIndex(index));\n let im = abs(getImagByOutputIndex(index));\n let mx = max(re, im);\n\n // The length function in wgsl may be not underflow-safe on some GPUs.\n // So the safe solution is to ensure underflow-safety in all cases.\n setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0));\n }\n }\n `}};function vV(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function kue(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.tensorMap.get(o.dataId),s=new dx(o.shape),a=[vV(o,n.complexTensorInfos.real),vV(o,n.complexTensorInfos.imag)];return t.runWebGPUProgram(s,a,a[0].dtype)}var kV={kernelName:Ai,backendName:\"webgpu\",kernelFunc:kue};var fx=class{constructor(e){this.uniforms=\"\",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=w.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let s=1;svi({inputs:{input:C},backend:t})),h=r.map(C=>Rp({inputs:{input:C},backend:t})),g=Zc(f,e,t),x=Zc(h,e,t),b=xo({inputs:{real:g,imag:x},backend:t});return f.forEach(C=>t.disposeData(C.dataId)),h.forEach(C=>t.disposeData(C.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let f=r.map(k=>{let $=[-1,y.sizeFromShape(k.shape.slice(e))];return pe({inputs:{x:k},backend:t,attrs:{shape:$}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=w.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=hz(h,g,o,x),C=w.computeOutShape(r.map(k=>k.shape),e),S=t.makeTensorInfo(C,o,b);return f.forEach(k=>t.disposeData(k.dataId)),S}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new fx(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:\"int32\",data:[l[0]]});for(let f=1;ft.disposeData(f.dataId));let d=pe({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function Nue(r,e,t){let o=w.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>pe({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function a0(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:t}):Zc(p,s,t)}var TV={kernelName:ta,backendName:\"webgpu\",kernelFunc:a0};function Tue(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=D=>{switch(D){case 1:return\"resData = f32(x[xIndex]);\";case 3:return\"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";case 4:return\"resData = vec4(x[xIndex / 4]);\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},l=D=>{switch(D){case 1:return\"return f32(W[row * uniforms.wShape[3] + col]);\";case 4:return\"return vec4(W[(row * uniforms.wShape[3] + col) / 4]);\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},m=r?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,d=r?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,f=r?\"uniforms.xShape[1]\":\"uniforms.xShape[2]\",h=r?\"uniforms.xShape[2]\":\"uniforms.xShape[3]\",g=r?\"row\":\"col\",x=r?\"col\":\"row\",b=`\n let inChannels = uniforms.wShape[2];\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n let outRow = ${g} / outWidth;\n let outCol = ${g} % outWidth;\n\n let WRow = ${x} / (uniforms.filterDims[1] * inChannels);\n let WCol = ${x} / inChannels % uniforms.filterDims[1];\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * WRow - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * WCol - uniforms.pads[1];\n let xCh = ${x} % inChannels;\n var resData = ${Ae(i)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the 'same' padding type.\n if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {\n ${m}\n let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n ${c(i)}\n }\n return resData;`,C=r?e&&o?`\n ${b}`:`\n if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${b}\n }\n return ${Ae(i)}(0.0);`:o&&t?`\n ${b}`:`\n if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n ${b}\n }\n return ${Ae(i)}(0.0);`,S=`${l(p)}`,k=Ae(u),_=r?Ae(i):Ae(p),$=r?Ae(p):Ae(i);return`\n ${dr(s,a,u===4,4)}\n fn mm_readA(batch: i32, row : i32, col : i32) -> ${_} {\n ${r?C:S}\n }\n\n fn mm_readB(batch: i32, row : i32, col : i32) -> ${$} {\n ${r?S:C}\n }\n\n fn mm_write(batch: i32, row : i32, col : i32, valueIn : ${k}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n {\n var value = valueIn;\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n ${d}\n ${Zr(n,s)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`}var hx=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=lm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=mm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),s&&(this.variableNames.push(\"bias\"),this.variableComponents.push(4)),i&&(this.variableNames.push(\"preluActivationWeights\"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?_p(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Ep(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`\n ${Tue(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}\n ${e}\n `}};var gx=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,\",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights,!1,4)}\n fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{\n let coords = vec4(batch, row, col, chan);\n if (coordsInBounds4D(coords, uniforms.xShape)) {\n return getX(batch, row, col, chan);\n } else {\n return 0.0;\n }\n }\n fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n let coords = vec4(row, col, xChannel, outChannel);\n if(coordsInBounds4D(coords, uniforms.wShape)) {\n return getW(row, col, xChannel, outChannel);\n } else {\n return 0.0;\n }\n }\n fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {\n let coords = ${this.isChannelsLast?\"vec4(batch, row, col, chan);\":\"vec4(batch, chan, row, col);\"}\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = valueIn;\n ${Zr(this.addBias,this.activation)}\n setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);\n }\n }\n ${G(\"index\")} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let outChannel = ${this.isChannelsLast?\"coords[3];\":\"coords[1];\"}\n let outRow = ${this.isChannelsLast?\"coords[1];\":\"coords[2];\"}\n let outCol = ${this.isChannelsLast?\"coords[2];\":\"coords[3];\"}\n var acc : f32 = 0.0;\n for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * row - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * col - uniforms.pads[1];\n for (var xChannel = 0; xChannel < ${this.isChannelsLast?\"uniforms.xShape[3];\":\"uniforms.xShape[1];\"} xChannel = xChannel + 1) {\n ${this.isChannelsLast?\"let v = readInp(batch, xRow, xCol, xChannel);\":\"let v = readInp(batch, xChannel, xRow, xCol);\"}\n let f = readFilt(row, col, xChannel, outChannel);\n acc = acc + v * f;\n }\n }\n }\n writeResult(batch, outRow, outCol, outChannel, acc);\n }\n `}};var xx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32,\n inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?\"coords[1]\":\"coords[2]\",n=this.isChannelsLast?\"coords[2]\":\"coords[1]\",s=this.isChannelsLast?\"getX(batch, xRow, xCol, ch)\":\"getX(batch, ch, xRow, xCol)\";return`\n ${G(\"index\")} {\n let coords = getCoordsFromIndex(index);\n if(index < uniforms.size) {\n let batch = coords[0];\n let row = ${o};\n let col = ${n};\n let offsetY = (row / uniforms.outWidth) * uniforms.strides[0] - uniforms.pads[0];\n let xRow = offsetY + uniforms.dilations[0] * (col / uniforms.itemsPerBlockRow);\n var value = 0.0;\n if(xRow < uniforms.xShape[${e}] && xRow >= 0) {\n let offsetX = (row % uniforms.outWidth) * uniforms.strides[1] -\n uniforms.pads[1];\n let xCol = offsetX + uniforms.dilations[1] * ((col %\n uniforms.itemsPerBlockRow) / uniforms.inChannels);\n let ch = col % uniforms.inChannels;\n if(xCol < uniforms.xShape[${t}] && xCol >= 0) {\n value = ${s};\n }\n }\n setOutputAtIndex(index, value);\n }\n }\n `}};function yx(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function _ue({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat===\"channelsLast\",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=pe({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=pe({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=yx(s.shape,p);x!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=yx(n.shape,p);x!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=$p({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=pe({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function Eue({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,C=b===\"channelsLast\",S=p*u*c,k=h*f,_=C?[t.batchSize,k,S]:[t.batchSize,S,k],$=new xx(_,C),R=[{type:\"int32\",data:[d.top,d.left]},{type:\"int32\",data:[m,l]},{type:\"int32\",data:[x,g]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*p]},{type:\"int32\",data:[c]}],D=o.runWebGPUProgram($,[r],r.dtype,R),P=[];P.push(D);let O=pe({inputs:{x:e},backend:o,attrs:{shape:[1,S,-1]}});if(P.push(O),s!=null){let U=yx(s.shape,C);U!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:U}}),P.push(s))}if(n!=null){let U=yx(n.shape,C);U!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:U}}),P.push(n))}let B=$p({a:C?D:O,b:C?O:D,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=pe({inputs:{x:B},backend:o,attrs:{shape:t.outShape}});P.push(B);for(let U of P)o.disposeData(U.dataId);return z}function bx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat===\"channelsLast\",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=A().getBool(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type===\"SAME\"||t.padInfo.type===\"VALID\")))return _ue({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=A().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),f=d>-1?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(A().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")||h<=f)return Eue({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:\"int32\",data:[t.filterHeight,t.filterWidth]},{type:\"int32\",data:[...x]},{type:\"int32\",data:[t.strideHeight,t.strideWidth]},{type:\"int32\",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new gx(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,$=c?t.outChannels:t.outHeight*t.outWidth,R=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:\"int32\",data:[_]},{type:\"int32\",data:[$]},{type:\"int32\",data:[R]});let D=o.adapterInfo.isIntel();g=new hx(t,_,$,R,p,i,u,D)}let C=[],S=[r,e];p&&(!c&&n.shape.length===1&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),S.push(n)),u&&(!c&&s.shape.length===1&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),S.push(s)),i===\"leakyrelu\"&&(b.push({type:\"float32\",data:[a]}),g.uniforms+=\" alpha : f32,\");let k=o.runWebGPUProgram(g,S,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function $ue(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return bx({x:n,filter:s,convInfo:m,backend:o})}var _V={kernelName:tn,backendName:\"webgpu\",kernelFunc:$ue};var Cx=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,\",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=e.inShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=this.isChannelsLast&&e.outChannels%4===0&&e.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1,n=`\n ${G()} {\n let batch = i32(globalId.z) / uniforms.outShape[1];\n let r = i32(globalId.z) % uniforms.outShape[1];\n let c = i32(globalId.y) * ${this.workPerThread};\n let d1 = i32(globalId.x) * 4;\n\n let dyCorner = vec2(r, c) - uniforms.pads;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd: array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = f32(dyCorner.x + wR) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) ||\n fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = f32(dyCorner.y + wC) / f32(uniforms.strides.y);\n let dyC2 = f32(dyCorner.y + 1 + wC) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n var bDyCVal = true;\n var bDyCVal2 = true;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0) {\n bDyCVal = false;\n }\n if (dyC2 < 0.0 || dyC2 >= f32(uniforms.outBackprop[2]) ||\n fract(dyC2) > 0.0) {\n bDyCVal2 = false;\n }\n\n let idyC = i32(dyC);\n let idyC2 = i32(dyC2);\n if (bDyCVal && bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n xValue = getDy(batch, idyR, idyC2, d2);\n dotProd[1] = dotProd[1] + vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n }\n } else if (bDyCVal) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n }\n } else if (bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC2, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[1] = dotProd[1] + tmpval;\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n }\n }\n }\n `;return this.isVec4?`\n ${n}\n `:`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[${o}];\n\n let dyCorner = vec2(coords[${e}], coords[${t}]) - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC = i32(dyC);\n\n for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n let xValue = ${this.isChannelsLast?\"getDy(batch, idyR, idyC, d2)\":\"getDy(batch, d2, idyR, idyC)\"};\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},wx=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=\"pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let d2 = coords[3];\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b = b + 1) {\n for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n if (${this.isChannelsLast}) {\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd = dotProd + xValue * dyValue;\n } else {\n let dyValue = getDy(b, d2, yR, yC);\n let xValue = getX(b, d1, xR, xC);\n dotProd = dotProd + xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Sx=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32,\n outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerFilter\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wF = coords.x;\n let wR = coords.y;\n let wC = coords.z;\n let d1 = coords.w;\n let d2 = coords.u;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yF = 0; yF < uniforms.outDepth; yF++) {\n let xF = wF + yF * uniforms.strides[0] - uniforms.pads[0];\n if (xF < 0 || xF >= uniforms.inDepth) {\n continue;\n }\n\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[1] - uniforms.pads[1];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[2] - uniforms.pads[2];\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yF, yR, yC, d2);\n let xValue = getX(b, xF, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Ix=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerInput\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let d1 = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyFCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let dyF = f32(dyFCorner + wF) / f32(uniforms.strides[0]);\n if (dyF < 0.0 || dyF >= f32(uniforms.outDepth) || fract(dyF) > 0.0) {\n continue;\n }\n let idyF = i32(dyF);\n\n let wFPerm = uniforms.filterDims[0] - 1 - wF;\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n let wRPerm = uniforms.filterDims[1] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let wCPerm = uniforms.filterDims[2] - 1 - wC;\n\n for (var d2 = 0; d2 < uniforms.outChannels; d2++) {\n let xValue = getDy(batch, idyF, idyR, idyC, d2);\n let wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Rue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new wx(m),f=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]},{type:\"int32\",data:[m.inHeight]},{type:\"int32\",data:[m.inWidth]}];return t.runWebGPUProgram(d,[n,s],n.dtype,f)}var EV={kernelName:Fi,backendName:\"webgpu\",kernelFunc:Rue};function Due(r=4){let e=s=>{switch(s){case 1:return\"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n return vec4(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${`\n let outRow = row / uniforms.outShape[2];\n let outCol = row % uniforms.outShape[2];\n\n let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.strides[0]);\n let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.strides[1]);\n if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n let coord = vec4(\n batch,\n i32(xR),\n i32(xC),\n col % uniforms.outBackprop[3]);\n return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}\n }\n return ${Ae(r)}(0.0);`;return`\n fn mm_readA(batch: i32, row : i32, col : i32) -> ${Ae(r)} {\n ${o}\n }\n\n fn mm_readB(batch: i32, row : i32, col : i32) -> ${Ae(r)} {\n let coordX = uniforms.filterDims.x - 1 -\n row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let coordY = uniforms.filterDims.y - 1 -\n (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n coordX >= 0 && coordY >= 0) {\n let rowInner = row % uniforms.outBackprop[3];\n let coord = vec4(coordX, coordY, col, rowInner);\n ${e(r)}\n }\n return ${Ae(r)}(0.0);\n }\n\n fn mm_write(batch: i32, row : i32, col : i32, valueInput : ${Ae(r)}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n var value = valueInput;\n let outCoord = vec4(\n batch,\n row / uniforms.outShape[2],\n row % uniforms.outShape[2],\n col);\n result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;\n }\n }`}var vx=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=lm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=mm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?_p(this.elementsPerThread,this.workgroupSize):Ep(this.elementsPerThread,this.workgroupSize);return`\n ${Due(this.isVec4?4:1)}\n ${e}\n `}};function Aue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:\"int32\",data:[m.filterHeight,m.filterWidth]},{type:\"int32\",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(A().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")||m.dataFormat!==\"channelsLast\")f=new Cx(m);else{f=new vx(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:\"uint32\",data:[h]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[x]})}return t.runWebGPUProgram(f,[n,s],\"float32\",d)}var $V={kernelName:rn,backendName:\"webgpu\",kernelFunc:Aue};var kx=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3dnaive\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords.x;\n let d2 = coords.u;\n\n let xFRCCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xFCorner = xFRCCorner.x;\n let xRCorner = xFRCCorner.y;\n let xCCorner = xFRCCorner.z;\n\n let inputDepthNearestVec4 = (uniforms.xShape.u / 4) * 4;\n let inputDepthVec4Remainder = uniforms.xShape.u % 4;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let xF = xFCorner + wF * uniforms.dilations[0];\n if (xF < 0 || xF >= uniforms.xShape.y) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let xR = xRCorner + wR * uniforms.dilations[1];\n if (xR < 0 || xR >= uniforms.xShape.z) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let xC = xCCorner + wC * uniforms.dilations[2];\n if (xC < 0 || xC >= uniforms.xShape.w) {\n continue;\n }\n\n for (var d1 = 0; d1 < inputDepthNearestVec4; d1 += 4) {\n let xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n let wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (inputDepthVec4Remainder == 1) {\n dotProd += getX(batch, xF, xR, xC, inputDepthNearestVec4) *\n getW(wF, wR, wC, inputDepthNearestVec4, d2);\n } else if (inputDepthVec4Remainder == 2) {\n let xValues = vec2(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1)\n );\n let wValues = vec2(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (inputDepthVec4Remainder == 3) {\n let xValues = vec3(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2)\n );\n let wValues = vec3(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }`}};function Fue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=[u.padInfo.front,u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new kx(u),d=dt(n.dtype,s.dtype);return t.runWebGPUProgram(m,[n,s],d,l)}var RV={kernelName:on,backendName:\"webgpu\",kernelFunc:Fue};function Pue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Sx(u),l=[{type:\"int32\",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.batchSize]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.inDepth]},{type:\"int32\",data:[u.inHeight]},{type:\"int32\",data:[u.inWidth]}];return t.runWebGPUProgram(c,[n,s],s.dtype,l)}var DV={kernelName:ja,backendName:\"webgpu\",kernelFunc:Pue};function Oue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,pad:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,a,1,i),c=new Ix(u),l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.outChannels]}];return t.runWebGPUProgram(c,[n,s],n.dtype,l)}var AV={kernelName:nn,backendName:\"webgpu\",kernelFunc:Oue};var Mue=ye({opType:Z.COS}),FV={kernelName:sn,backendName:\"webgpu\",kernelFunc:Mue};var Lue=ye({opType:Z.COSH}),PV={kernelName:an,backendName:\"webgpu\",kernelFunc:Lue};var Nx=class{constructor(e,t,o,n){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let height_ratio = f32(${o});\n let width_ratio = f32(${a});\n let b = coords[0];\n let y = coords[1];\n let x = coords[2];\n let d = coords[3];\n // get box vals\n let y1 = getBoxes(b, 0);\n let x1 = getBoxes(b, 1);\n let y2 = getBoxes(b, 2);\n let x2 = getBoxes(b, 3);\n // get image in batch index\n let bInd = i32(round(getBoxInd(b)));\n if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n return;\n }\n let height_scale = ${n};\n let width_scale = ${i};\n let in_y = ${s};\n if( in_y < 0.0 || in_y > ${e} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let in_x = ${p};\n if( in_x < 0.0 || in_x > ${t} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let sourceFracIndexCR = vec2(in_x,in_y);\n if(${this.methodId} == 1) {\n // Compute the four integer indices.\n let sourceFloorCR = vec2(sourceFracIndexCR);\n let sourceCeilCR = vec2(ceil(sourceFracIndexCR));\n let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n let fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n let top = topLeft + (topRight - topLeft) * fracCR.x;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n let newValue = top + (bottom - top) * fracCR.y;\n setOutputAtIndex(index, newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n let sourceNearestCR = vec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n let newValue = getImage(\n bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutputAtIndex(index, newValue);\n }\n }\n }\n `}};var Bue=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Nx(n.shape[3],s.shape,i,p),l=[{type:\"float32\",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],\"float32\",l)},OV={kernelName:cn,backendName:\"webgpu\",kernelFunc:Bue};var Dp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Dp||(Dp={}));var xm=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Dp.Prod?\"1.0\":\"0.0\",o=this.exclusive?t:`getX(${MV(e,\"coords\",this.op)})`,n=this.outputShape[this.outputShape.length-1],s=\"\",a=\"\";return this.exclusive?(s=this.reverse?`end != ${n-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(s=this.reverse?`end + pow2 < ${n}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var coords = getCoordsFromIndex(index);\n\n let end = ${LV(e,\"coords\",this.op)};\n var val = ${o};\n let pow2 = i32(pow(2.0, uniforms.index));\n if (${s}) {\n let idx = ${a};\n ${LV(e,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${MV(e,\"coords\",this.op)});\n }\n setOutputAtIndex(index, val);\n }\n }\n `}};function MV(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function LV(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Tx(r,e,t,o,n,s){let a=e.shape.length,i=w.getAxesPermutation([o],a),p=e;i!=null&&(p=xr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new xm(r,p.shape,!1,s),f=l,h=[{type:\"float32\",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new xm(r,p.shape,n,s),d=l,f=[{type:\"float32\",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=xr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function zue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Tx(Dp.Prod,n,t,s,a,i)}var BV={kernelName:un,backendName:\"webgpu\",kernelFunc:zue};function Vue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Tx(Dp.Sum,n,t,s,a,i)}var zV={kernelName:pn,backendName:\"webgpu\",kernelFunc:Vue};function Wue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=vt({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new Qc(m,c,i),g=[{type:\"int32\",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var VV={kernelName:ra,backendName:\"webgpu\",kernelFunc:Wue};var _x=class{constructor(e,t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let h = ${this.getHeightCoordString()};\n let w = ${this.getWidthCoordString()};\n let d = ${this.getDepthCoordString()};\n\n let in_h = h / uniforms.blockSize;\n let offset_h = h % uniforms.blockSize;\n let in_w = w / uniforms.blockSize;\n let offset_w = w % uniforms.blockSize;\n let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n ${this.getOutputDepthSize()};\n let in_d = d + offset_d;\n\n let rlt = ${this.getInputSamplingString()};\n setOutputAtIndex(index, rlt);\n }\n }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function Uue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=[{type:\"int32\",data:[s]}],g=new _x(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var WV={kernelName:ln,backendName:\"webgpu\",kernelFunc:Uue};var Ex=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pads : vec2, inDims : vec2,\",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push(\"bias\"),a&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return`\n ${dr(this.activation,this.hasPreluActivation,!1,4)}\n\n var mm_Asub : array, ${o}>;\n var mm_Bsub : array, ${this.filterHeight}>;\n fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n var value = 0.0;\n if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n {\n value = getX(batch, channel, row, col);\n }\n return value;\n }\n\n ${G()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.zw) - uniforms.pads;\n let channelMul = uniforms.wShape[3];\n let d1 = coords[1] / channelMul;\n let q = coords[1] % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n\n // Load one tile of X into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {\n for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {\n let rowOffset = inputRow - localRow;\n let colOffset = inputCol - localCol;\n mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n }\n }\n\n // Load one tile of W into local memory.\n var wIndex = i32(localIndex);\n ${e, inDims : vec2, virtualWidth : i32,\",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=e.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let s=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=X(s),this.dispatch=H(this.dispatchLayout,s,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return`\n ${dr(this.activation,this.hasPreluActivation,!0,4)}\n fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 {\n var value = vec4(0.0);\n if (col >=0 && col < uniforms.inDims[1]) {\n value = getX(batch, row, col, channel);\n }\n return value;\n }\n\n ${G(\"index\")} {\n let width0 = uniforms.outShape[3] / ${this.outputComponent};\n let d1 = (index % width0) * ${this.outputComponent};\n var index1 = index / width0;\n let width1 = uniforms.virtualWidth / ${this.workPerThread};\n let c = (index1 % width1) * ${this.workPerThread};\n index1 = index1 / width1;\n let r = index1 % uniforms.outShape[1];\n let batch = index1 / uniforms.outShape[1];\n\n let xRCCorner = vec2(r, c) * vec2(${t}, ${o}) - uniforms.pads;\n\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n var xVals : array, ${e}>;\n var dotProd : array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n\n // Use constant instead of uniform can give better performance.\n for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n let xR = xRCorner + wR;\n if (xR >=0 && xR < uniforms.inDims[0]) {\n for (var i = 0; i < ${e}; i++) {\n xVals[i] = readX(batch, xR, xCCorner + i, d1);\n }\n for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n let wValue = getW(wR, wC, d1, 0);\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]);\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = dotProd[i];\n ${Zr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n }\n `}};var el=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32,\n filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?\"getX(batch, xR, xC, d1);\":\"getX(batch, d1, xR, xC);\";return`\n ${dr(this.activation,this.hasPreluActivation,!1,4)}\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.${this.isChannelsLast?\"yz\":\"zw\"}) * uniforms.strides - uniforms.pads;\n let d2 = coords[${this.isChannelsLast?3:1}];\n let channelMul = uniforms.wShape[3];\n let d1 = d2 / channelMul;\n let q = d2 % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n let inputRowEnd = inputRowStart + uniforms.filterHeight *\n uniforms.dilations[0];\n let inputColEnd = inputColStart + uniforms.filterWidth *\n uniforms.dilations[1];\n\n // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get\n // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n var value = 0.0;\n\n // Extract if checking out of for loop for performance.\n if (inputRowStart >= 0 && inputColStart >= 0 &&\n inputRowEnd < uniforms.inDims[0] &&\n inputColEnd < uniforms.inDims[1]) {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n } else {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n if (xR < 0 || xR >= uniforms.inDims[0]) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n if (xC < 0 || xC >= uniforms.inDims[1]) {\n continue;\n }\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n }\n ${Zr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n `}};function Gue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:\"int32\",data:[d.padInfo.top,d.padInfo.left]},{type:\"int32\",data:[d.inHeight,d.inWidth]}],h=d.dataFormat===\"channelsLast\",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new Ex(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?(g=new Jc(d),f.push({type:\"int32\",data:[g.virtualWidth]})):(g=new el(d),f.push({type:\"int32\",data:[d.filterHeight]},{type:\"int32\",data:[d.filterWidth]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var UV={kernelName:mn,backendName:\"webgpu\",kernelFunc:Gue};var $x=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32,\n outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_filter\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let dm = coords[3];\n let d2 = d1 * uniforms.channelMul + dm;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Rx=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_input\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[3];\n let dyCorner = coords.yz - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n\n let idyR = i32(dyR);\n let wRPerm = uniforms.filterDims[0] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n\n let idyC = i32(dyC);\n let wCPerm = uniforms.filterDims[1] - 1 - wC;\n\n for (var dm = 0; dm < uniforms.channelMul; dm++) {\n let d2 = d1 * uniforms.channelMul + dm;\n let xValue = getDy(batch, idyR, idyC, d2);\n let wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Hue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new $x(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.inHeight]},{type:\"int32\",data:[l.inWidth]},{type:\"int32\",data:[l.batchSize]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],\"float32\",d)}var GV={kernelName:Pi,backendName:\"webgpu\",kernelFunc:Hue};function Kue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Rx(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.filterHeight-1-l.padInfo.top,l.filterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],n.dtype,d)}var HV={kernelName:Oi,backendName:\"webgpu\",kernelFunc:Kue};var Dx=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"diag\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);\n setOutputAtIndex(index, value);\n }\n }\n `}};function que(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Dx(s),p=t.runWebGPUProgram(i,[a],a.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var KV={kernelName:oa,backendName:\"webgpu\",kernelFunc:que};var Ax=class{constructor(e){this.variableNames=[\"x\",\"w\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"dilation2d\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let neg_infinity = -3.4e38;\n let coords = getOutputCoords();\n let batch = coords.x;\n let d1 = coords.w;\n let outTopLeftCorner = coords.yz * uniforms.strides - uniforms.pads;\n let hBeg = outTopLeftCorner.x;\n let wBeg = outTopLeftCorner.y;\n\n var curVal = neg_infinity;\n for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) {\n let hIn = hBeg + h * uniforms.dilations[0];\n\n if (hIn >= 0 && hIn < uniforms.xShape[1]) {\n for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) {\n let wIn = wBeg + w * uniforms.dilations[1];\n\n if (wIn >= 0 && wIn < uniforms.xShape[2]) {\n let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1);\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n setOutputAtIndex(index, curVal);\n }\n }\n `}};function jue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationHeight,u.dilationWidth]}],m=new Ax(u);return t.runWebGPUProgram(m,[n,s],n.dtype,l)}var qV={kernelName:dn,backendName:\"webgpu\",kernelFunc:jue};var Fx=class{constructor(e,t){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.inShape,this.dispatchLayout=X(e.outShape),this.dispatch=H(this.dispatchLayout,e.outShape,this.workgroupSize),t!==\"float32\"&&t!==\"int32\")throw new Error(`Dilation2DBackpropInput only supports float32 and int32\n types, does not support ${t} type.`);this.type=t,this.shaderKey=\"dilation2DBackpropInput\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var xRMax = 0;\n var xCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n xRMax = xR;\n xCMax = xC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.xShape[3] *\n (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b));\n let value = getDy(b, r, c, d);\n ${Qr(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}},Px=class{constructor(e,t,o){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(e.outShape),this.dispatch=H(this.dispatchLayout,e.outShape,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"dilation2DBackpropFilter\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var wRMax = 0;\n var wCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n wRMax = wR;\n wCMax = wC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]);\n let value = getDy(b, r, c, d);\n ${Qr(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}};function Xue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=s.dtype,m=new Px(c,s.shape,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=vt({backend:t,attrs:{shape:s.shape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var jV={kernelName:Li,backendName:\"webgpu\",kernelFunc:Xue};function Yue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=n.dtype,m=new Fx(c,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=vt({backend:t,attrs:{shape:c.inShape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var XV={kernelName:Mi,backendName:\"webgpu\",kernelFunc:Yue};var Ox=class{constructor(e,t,o){this.variableNames=[\"Image\"],this.uniforms=\"alpha: f32,\",this.workgroupSize=[64,1,1],this.pixelsOpType=wi.DRAW,this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.type=t,this.textureFormat=o,this.shaderKey=`draw_${t}_${o}`}getUserCode(){let e,t=this.type===\"float32\"?\"value\":\"value / 255.0\";return e=`\n if (uniforms.numChannels == 1) {\n rgba[0] = ${t};\n rgba[1] = ${t};\n rgba[2] = ${t};\n } else {\n rgba[d] = ${t};\n }`,`\n @group(0) @binding(0) var outImage : texture_storage_2d<${this.textureFormat}, write>;\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var rgba = vec4(0.0, 0.0, 0.0, uniforms.alpha);\n for (var d = 0; d < uniforms.numChannels; d = d + 1) {\n let value = f32(inBuf[index * uniforms.numChannels + d]);\n ${e}\n }\n rgba.x = rgba.x * rgba.w;\n rgba.y = rgba.y * rgba.w;\n rgba.z = rgba.z * rgba.w;\n let coords = getCoordsFromIndex(index);\n textureStore(outImage, vec2(coords.yx), rgba);\n }\n }\n `}};function Que(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{canvas:s,options:a}=o,[i,p]=n.shape.slice(0,2),{imageOptions:u}=a||{},c=(u==null?void 0:u.alpha)||1,l=t.device.features.has(\"bgra8unorm-storage\")?\"bgra8unorm\":\"rgba8unorm\",m=[i,p],d=new Ox(m,n.dtype,l);s.width=p,s.height=i;let f=\"webgpu\",h=s.getContext(f),g;h||(g=new OffscreenCanvas(p,i),h=g.getContext(f));let x=n.shape.length===3?n.shape[2]:1;h.configure({device:t.device,format:l,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:\"premultiplied\"});let b=\"int32\",C=t.makeTensorInfo(m,b),S=t.tensorMap.get(C.dataId);S.resource=h.getCurrentTexture(),S.external=!0;let k=[{type:\"uint32\",data:[x]},{type:\"float32\",data:[c]}];if(t.runWebGPUProgram(d,[n],b,k,C),g){let _=s.getContext(\"2d\");if(!_)throw new Error(\"Please make sure this canvas has only been used for 2d or webgpu context!\");_.drawImage(g,0,0)}return t.disposeData(C.dataId),n}var YV={kernelName:$u,backendName:\"webgpu\",kernelFunc:Que};var i0=et({opType:fe.MUL,cpuKernelImpl:Rz,supportsComplex:!0}),QV={kernelName:Xn,backendName:\"webgpu\",kernelFunc:i0};function u0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,\"sum\",t)}var ZV={kernelName:Ss,backendName:\"webgpu\",kernelFunc:u0};function Zue(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=u0({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var JV={kernelName:Bi,backendName:\"webgpu\",kernelFunc:Zue};var Jue=ye({opType:Z.ELU}),eW={kernelName:hn,backendName:\"webgpu\",kernelFunc:Jue};var epe=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=new Ii(fe.ELU_DER,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],o.dtype)},tW={kernelName:Xa,backendName:\"webgpu\",kernelFunc:epe};var tpe=et({opType:fe.EQUAL,dtype:\"bool\",cpuKernelImpl:gz}),rW={kernelName:xn,backendName:\"webgpu\",kernelFunc:tpe};var rpe=ye({opType:Z.ERF}),oW={kernelName:gn,backendName:\"webgpu\",kernelFunc:rpe};var ope=ye({opType:Z.EXP,cpuKernelImpl:xz,dtype:\"float32\"}),nW={kernelName:yn,backendName:\"webgpu\",kernelFunc:ope};function Mx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var sW={kernelName:na,backendName:\"webgpu\",kernelFunc:Mx};var npe=ye({opType:Z.EXPM1,cpuKernelImpl:yz}),aW={kernelName:bn,backendName:\"webgpu\",kernelFunc:npe};var ym=class{constructor(e,t){this.variableNames=[\"real\",\"imag\"],this.outputShape=[],this.uniforms=\"exponentMultiplier : f32, denominator: f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return`\n fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {\n ${this.component===\"real\"?\"return real * expR - imag * expI;\":\"return real * expI + imag * expR;\"}\n }\n\n fn mulMatDFT(batch: i32, index: i32) -> f32 {\n let indexRatio = f32(index) / f32(uniforms.realShape[1]);\n let exponentMultiplierTimesIndexRatio =\n uniforms.exponentMultiplier * indexRatio;\n\n var result = 0.0;\n\n for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {\n // x = (-2|2 * PI / N) * index * i;\n let x = exponentMultiplierTimesIndexRatio * f32(i);\n let expR = cos(x);\n let expI = sin(x);\n let real = getReal(batch, i);\n let imag = getImag(batch, i);\n\n result = result +\n unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;\n }\n\n return result;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));\n }\n }\n `}};function Lx(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=pe({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new ym(\"real\",u),l=new ym(\"imag\",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:\"float32\",data:[d]},{type:\"float32\",data:[f]}],g=t.runWebGPUProgram(c,m,\"float32\",h);i.push(g);let x=t.runWebGPUProgram(l,m,\"float32\",h);i.push(x);let b=xo({inputs:{real:g,imag:x},backend:t});i.push(b);let C=pe({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(S=>t.disposeData(S.dataId)),C}function spe(r){let{inputs:e,backend:t}=r,{input:o}=e;return Lx(o,!1,t)}var iW={kernelName:zi,backendName:\"webgpu\",kernelFunc:spe};var Bx=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordX = uniforms.xShape[2] - coords[2] - 1;\n let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var uW={kernelName:Cn,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Bx(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var ape=ye({opType:Z.FLOOR,cpuKernelImpl:bz}),pW={kernelName:wn,backendName:\"webgpu\",kernelFunc:ape};var ipe=et({opType:fe.FLOOR_DIV,cpuKernelImpl:Cz,dtype:\"int32\"}),cW={kernelName:Sn,backendName:\"webgpu\",kernelFunc:ipe};var zx=class{constructor(e,t,o=!1){this.pixelsOpType=wi.FROM_PIXELS,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?\"textureLoad(src, vec2(coords.yx));\":\"textureLoad(src, vec2(coords.yx), 0)\";return`\n @binding(1) @group(0) var src: ${this.importVideo?\"texture_external\":\"texture_2d\"};\n ${G(\"index\")} {\n let flatIndex = index * uniforms.numChannels;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n let values = ${e};\n for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n result[flatIndex + i] = i32(floor(255.0 * values[i]));\n }\n }\n }\n `}};var lW={kernelName:Du,backendName:\"webgpu\",kernelFunc:upe},tl,p0=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function upe(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o;if(n==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!=\"undefined\"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!=\"undefined\"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=A().getBool(\"WEBGPU_IMPORT_EXTERNAL_TEXTURE\")&&a,f=a||i;if(u||p||f){let b;if(d)b=t.device.importExternalTexture({source:n});else{if(f){let L=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(tl==null||L!==p0)&&(p0=L,tl=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:p0})),tl.canvas.width=c,tl.canvas.height=l,tl.drawImage(n,0,0,c,l),n=tl.canvas}let P=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,M=t.textureManager.acquireTexture(m[1],m[0],\"rgba8unorm\",P);t.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b=M}let C=y.sizeFromShape(m),S=y.computeStrides(m),k=new zx(m,s,d),_=[{type:\"uint32\",data:[C]},{type:\"uint32\",data:[s]},{type:\"uint32\",data:[...S]}],$=t.makeTensorInfo([l,c],\"int32\"),R=t.tensorMap.get($.dataId);R.resource=b;let D=t.runWebGPUProgram(k,[$],\"int32\",_);return t.disposeData($.dataId),D}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,C=0;for(let S=0;S(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0)));\n }\n }\n `}};var mW={kernelName:In,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=e,u=t,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new Vx(o.shape,a.shape,i.shape,l,m),f=[{type:\"float32\",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function ppe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return bx({x:n,filter:s,convInfo:g,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var dW={kernelName:Io,backendName:\"webgpu\",kernelFunc:ppe};function cpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=w.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:\"int32\",data:[h.padInfo.top,h.padInfo.left]},{type:\"int32\",data:[h.inHeight,h.inWidth]}],S;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(S=new Jc(h,x,m,b),C.push({type:\"int32\",data:[S.virtualWidth]})):(S=new el(h,x,m,b),C.push({type:\"int32\",data:[h.filterHeight]},{type:\"int32\",data:[h.filterWidth]},{type:\"int32\",data:[h.strideHeight,h.strideWidth]},{type:\"int32\",data:[h.dilationHeight,h.dilationWidth]})),m===\"leakyrelu\"&&(C.push({type:\"float32\",data:[d]}),S.uniforms+=\" alpha : f32,\"),t.runWebGPUProgram(S,g,\"float32\",C)}var fW={kernelName:vo,backendName:\"webgpu\",kernelFunc:cpe};var Wx=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${ft(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var flattenIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexTemp = i32(round(getIndices(coords[0], j)));\n let strideNum = ${e};\n flattenIndex = flattenIndex + indexTemp * strideNum;\n }\n\n setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n }\n }\n `}};function lpe(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=pe({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=pe({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let b=t.readSync(n.dataId),C=t.bufferSync(o),S=wz(b,C,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,S.values)}let f=new Wx(a,[u,c]),h=[{type:\"int32\",data:[a]},{type:\"int32\",data:l}],g=t.runWebGPUProgram(f,[d,m],d.dtype,h),x=pe({inputs:{x:g},backend:t,attrs:{shape:p}});return t.disposeData(m.dataId),t.disposeData(d.dataId),t.disposeData(g.dataId),x}var hW={kernelName:vn,backendName:\"webgpu\",kernelFunc:lpe};var Ux=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=mpe(this.aShape);return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let resRC = getCoordsFromIndex(index);\n let indexZ = i32(getIndices(resRC.x, resRC.z));\n let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n setOutputAtIndex(index, inBounds * getA(${e}));\n }\n }\n `}};function mpe(r){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],t=[];for(let o=0;ot.disposeData(D.dataId)),t.makeTensorInfo(u.outputShape,R.dtype,R.values)}let h=new Ux(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=pe({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var gW={kernelName:aa,backendName:\"webgpu\",kernelFunc:c0};var dpe=et({opType:fe.GREATER,cpuKernelImpl:vz,dtype:\"bool\"}),xW={kernelName:kn,backendName:\"webgpu\",kernelFunc:dpe};var fpe=et({opType:fe.GREATER_EQUAL,dtype:\"bool\",cpuKernelImpl:Iz}),yW={kernelName:Nn,backendName:\"webgpu\",kernelFunc:fpe};function hpe(r){let{inputs:e,backend:t}=r,{input:o}=e;return Lx(o,!0,t)}var bW={kernelName:Vi,backendName:\"webgpu\",kernelFunc:hpe};var gpe=ye({opType:Z.IS_FINITE,dtype:\"bool\"}),CW={kernelName:Tn,backendName:\"webgpu\",kernelFunc:gpe};var xpe=ye({opType:Z.IS_INF,dtype:\"bool\"}),wW={kernelName:_n,backendName:\"webgpu\",kernelFunc:xpe};var ype=ye({opType:Z.IS_NAN,dtype:\"bool\"}),SW={kernelName:En,backendName:\"webgpu\",kernelFunc:ype};function bpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:\"float32\",data:[s]}],i=new Jr(n.shape,Z.LEAKYRELU,\"alpha : f32,\");return t.runWebGPUProgram(i,[n],\"float32\",a)}var IW={kernelName:$n,backendName:\"webgpu\",kernelFunc:bpe};var Cpe=et({opType:fe.LESS,dtype:\"bool\",cpuKernelImpl:Nz}),vW={kernelName:Rn,backendName:\"webgpu\",kernelFunc:Cpe};var wpe=et({opType:fe.LESS_EQUAL,dtype:\"bool\",cpuKernelImpl:kz}),kW={kernelName:Dn,backendName:\"webgpu\",kernelFunc:wpe};var Gx=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"start : f32, step : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"linSpace\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);\n }\n }\n `}};function Spe(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=(n-o)/(s-1),i=new Gx(s),p=[{type:\"float32\",data:[o]},{type:\"float32\",data:[a]}];return e.runWebGPUProgram(i,[],\"float32\",p)}var NW={kernelName:An,backendName:\"webgpu\",kernelFunc:Spe};var Ipe=ye({opType:Z.LOG,cpuKernelImpl:Tz}),TW={kernelName:Fn,backendName:\"webgpu\",kernelFunc:Ipe};var vpe=ye({opType:Z.LOG1P}),_W={kernelName:Pn,backendName:\"webgpu\",kernelFunc:vpe};var kpe=et({opType:fe.LOGICAL_AND,dtype:\"bool\"}),EW={kernelName:On,backendName:\"webgpu\",kernelFunc:kpe};var Npe=ye({opType:Z.LOGICAL_NOT}),$W={kernelName:Mn,backendName:\"webgpu\",kernelFunc:Npe};var Tpe=et({opType:fe.LOGICAL_OR}),RW={kernelName:Ln,backendName:\"webgpu\",kernelFunc:Tpe};var DW=`\n var powValue = 0.0;\n let basis = uniforms.bias + uniforms.alpha * sum;\n if (uniforms.beta == 0.5) {\n powValue = inverseSqrt(basis);\n } else if (uniforms.beta == 1.0) {\n powValue = 1.0 / basis;\n } else {\n powValue = exp(log(basis) * (-uniforms.beta));\n }\n`,Hx=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let x = getX(b, r, c, d);\n var sum = 0.0;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let idx = d + i;\n if (idx >= 0 && idx < uniforms.xShape[3]) {\n let z = getX(b, r, c, idx);\n sum = sum + z * z;\n }\n }\n ${DW}\n\n setOutputAtIndex(index, x * powValue);\n }\n }\n `}},Kx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey=\"lrn_shared\"}getUserCode(){return`\n var lrnSub: array;\n const elementsPerWorkgroup = ${this.elementsPerWorkgroup};\n const maxAllowRadius = ${this.maxAllowRadius};\n\n ${G()} {\n let localDepth = i32(localId.x);\n let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup;\n let xDepth = workgroupDepth + localDepth - maxAllowRadius;\n let b = i32(globalId.z) / uniforms.xShape[1];\n let r = i32(globalId.z) - b * uniforms.xShape[1];\n let c = i32(globalId.y);\n let d = workgroupDepth + localDepth;\n\n var x = 0.0;\n if (xDepth >= 0 && xDepth < uniforms.xShape[3]) {\n x = getX(b, r, c, xDepth);\n }\n lrnSub[localDepth] = x;\n workgroupBarrier();\n\n if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) {\n var sum = 0.0;\n let index = localDepth + maxAllowRadius;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let z = lrnSub[index + i];\n sum = sum + z * z;\n }\n ${DW}\n\n setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);\n }\n } `}};function _pe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new Hx(n.shape):u=new Kx(n.shape,s);let c=[{type:\"int32\",data:[s]},{type:\"float32\",data:[a]},{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var AW={kernelName:Bn,backendName:\"webgpu\",kernelFunc:_pe};var qx=class{constructor(e){this.outputShape=[],this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.uniforms=\"depthRadius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn_grad\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n\n let MIN_DEPTH_BEGIN = 0;\n let MAX_DEPTH_END = uniforms.outShape[3];\n var result = 0.0;\n for (var d = MIN_DEPTH_BEGIN; d < MAX_DEPTH_END; d++) {\n let depthBegin = max(MIN_DEPTH_BEGIN, d - uniforms.depthRadius);\n let depthEnd = min(MAX_DEPTH_END, d + uniforms.depthRadius + 1);\n\n var norm = 0.0;\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n } else {\n break;\n }\n }\n\n norm = uniforms.alpha * norm + uniforms.bias;\n\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n var dyi = -2.0 * uniforms.alpha * uniforms.beta\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * uniforms.beta);\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n } else {\n break;\n }\n }\n }\n\n setOutputAtIndex(index, result);\n }\n }\n `}};function Epe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new qx(n.shape),m=[{type:\"int32\",data:[i]},{type:\"float32\",data:[p]},{type:\"float32\",data:[u]},{type:\"float32\",data:[c]}];return t.runWebGPUProgram(l,[n,s,a],n.dtype,m)}var FW={kernelName:Ya,backendName:\"webgpu\",kernelFunc:Epe};var $pe=et({opType:fe.MAX,cpuKernelImpl:Ez}),PW={kernelName:Vn,backendName:\"webgpu\",kernelFunc:$pe};function Rpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,c=w.computePool2DInfo(n.shape,s,a,1,i,p);return ax(n,c,\"max\",t)}var OW={kernelName:Wn,backendName:\"webgpu\",kernelFunc:Rpe};function Dpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Iu(l,\"max\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var MW={kernelName:ia,backendName:\"webgpu\",kernelFunc:Dpe};var jx=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool2DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] - 1;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR += uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC += uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wR * uniforms.filterDims[1] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Xx=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool3DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] * uniforms.filterDims[2] - 1;\n\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wD * uniforms.filterDims[1] * uniforms.filterDims[2] + wR * uniforms.filterDims[2] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n }\n\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Ape(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Iu(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.inDepth,m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[a],\"int32\",f),g=new Xx(m);f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterDepth-1-m.padInfo.front,m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outDepth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],a.dtype,f);return t.disposeData(h.dataId),x}var LW={kernelName:Gi,backendName:\"webgpu\",kernelFunc:Ape};function Fpe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;fm([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=new Ba(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[i],\"int32\",f),g=new jx(m);f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],i.dtype,f);return t.disposeData(h.dataId),x}var BW={kernelName:Ui,backendName:\"webgpu\",kernelFunc:Fpe};function Ppe(r){let{inputs:e,backend:t,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=e;y.assert(p.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(p.shape,n,s,u,a),l=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]}],m=new Ba(c,\"max\",!1),d=t.runWebGPUProgram(m,[p],p.dtype,l);m=new Ba(c,\"max\",!0,!0,i);let f=t.runWebGPUProgram(m,[p],\"int32\",l);return[d,f]}var zW={kernelName:ua,backendName:\"webgpu\",kernelFunc:Ppe};function Ope(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,\"min\",t)}var VW={kernelName:Gn,backendName:\"webgpu\",kernelFunc:Ope};var Mpe=et({opType:fe.MIN,cpuKernelImpl:$z}),WW={kernelName:Hn,backendName:\"webgpu\",kernelFunc:Mpe};var Yx=class{constructor(e,t,o){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(\",\"),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:\"\"}`).join(\",\"),n=e===1?\"start\":\"start[i]\",s=e===1?\"end\":\"end[i]\",a=e===1?\"outC\":\"outC[i]\",i=ft(e),p=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let start = ${i}(${t});\n let end = ${i}(${o});\n var outC = getCoordsFromIndex(index);\n for (var i = 0; i < ${e}; i = i + 1) {\n if (${a} < ${n}) {\n ${a} = ${n} * 2 - ${a} - ${this.offset};\n } else if(${a} >= ${s}) {\n ${a} = (${s} - 1) * 2 - ${a} + ${this.offset};\n }\n }\n let coords = outC - start;\n setOutputAtIndex(index, getX(${p}));\n }\n }\n `}};var UW={kernelName:Kn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:\"int32\",data:[c[0],c[1]]})),p=new Yx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var Lpe=et({opType:fe.MOD}),GW={kernelName:qn,backendName:\"webgpu\",kernelFunc:Lpe};var Qx=class{constructor(e,t){this.variableNames=[\"probs\"],this.outputShape=[],this.uniforms=\"seed : f32, numOutcomes: i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"multinomial\"}getUserCode(){return`\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n fn random (seed : f32, resultUV : vec2) -> f32 {\n let HASHSCALE1 = 443.8975;\n let p = resultUV * seed;\n var p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 = p3 + dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n\n let resUV = vec2(f32(coords[1]) / f32(uniforms.outShape[1]),\n f32(coords[0]) / f32(uniforms.outShape[0]));\n let r = random(uniforms.seed, resUV);\n var cdf = 0.0;\n for (var i = 0; i < uniforms.numOutcomes - 1; i = i + 1) {\n cdf = cdf + getProbs(batch, i);\n\n if (r < cdf) {\n setOutputAtIndexI32(index, i);\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutputAtIndexI32(index, uniforms.numOutcomes - 1);\n }\n }\n `}};var Zx=class{constructor(e){this.variableNames=[\"logits\"],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey=\"softmax\"}getUserCode(){return`\n var buf : array;\n var rowMaxShared : f32;\n var rowSumShared : f32;\n const blockSize = ${this.workgroupSize[0]};\n ${G(\"index\")} {\n let row = index / blockSize;\n let tid = i32(localId.x);\n let cols = uniforms.outShape[1];\n\n var threadMax = -3.402823e+38f;\n for (var col = tid; col < cols; col += blockSize) {\n let value = getLogits(row, col);\n threadMax = max(threadMax, value);\n }\n if (tid < cols) {\n buf[tid] = threadMax;\n }\n workgroupBarrier();\n\n var reduceSize = min(cols, blockSize);\n for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) {\n reduceSize = currSize + (reduceSize & 1);\n if (tid < currSize) {\n buf[tid] = max(buf[tid], buf[tid + reduceSize]);\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowMaxShared = buf[0];\n }\n workgroupBarrier();\n\n var threadSum = 0.0;\n for (var col = tid; col < cols; col += blockSize) {\n let subExp = exp(getLogits(row, col) - rowMaxShared);\n threadSum += subExp;\n }\n buf[tid] = threadSum;\n workgroupBarrier();\n\n for (var currSize = blockSize >> 1; currSize > 0; currSize = currSize >> 1) {\n if (tid < currSize) {\n buf[tid] = buf[tid] + buf[tid + currSize];\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowSumShared = buf[0];\n }\n workgroupBarrier();\n\n for (var col = tid; col < cols; col += blockSize) {\n let value = exp(getLogits(row, col) - rowMaxShared) / rowSumShared;\n setOutputAtCoords(row, col, value);\n }\n }\n `}};function l0(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=pe({inputs:{x:n},backend:t,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new Zx(a.shape),p=t.runWebGPUProgram(i,[a],n.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n.shape}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var HW={kernelName:Is,backendName:\"webgpu\",kernelFunc:l0};function Bpe(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:l0({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new Qx(u,s),m=[{type:\"float32\",data:[a]},{type:\"int32\",data:[c]}],d=t.runWebGPUProgram(l,[p],\"int32\",m);return i||t.disposeData(p.dataId),d}var KW={kernelName:jn,backendName:\"webgpu\",kernelFunc:Bpe};function zpe(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=Dz(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Jr(o.shape,Z.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var qW={kernelName:pa,backendName:\"webgpu\",kernelFunc:zpe};function Vpe(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Vt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],\"int32\",new Int32Array(l))}var jW={kernelName:Qn,backendName:\"webgpu\",kernelFunc:Vpe};function Wpe(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Vt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),t.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var XW={kernelName:Zn,backendName:\"webgpu\",kernelFunc:Wpe};var Jx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"onValue : f32, offValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"onehot\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,\n f32(i32(round(getX(coords.x))) == coords.y)));\n }\n }\n `}};function Upe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Jx(u,a),l=pe({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=pe({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var YW={kernelName:Jn,backendName:\"webgpu\",kernelFunc:Upe};function bm(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"complex64\"){let n=vi({inputs:{input:o},backend:t}),s=bm({inputs:{x:n},backend:t}),a=Rp({inputs:{input:o},backend:t}),i=bm({inputs:{x:a},backend:t}),p=xo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return vt({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:t})}var QW={kernelName:Sa,backendName:\"webgpu\",kernelFunc:bm};function ZW(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(o.dtype===\"complex64\"){let n=vi({inputs:{input:o},backend:t}),s=ZW({inputs:{x:n},backend:t}),a=Rp({inputs:{input:o},backend:t}),i=bm({inputs:{x:a},backend:t}),p=xo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return vt({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var JW={kernelName:ca,backendName:\"webgpu\",kernelFunc:ZW};function Gpe(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Mx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=Mx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=a0({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var eU={kernelName:la,backendName:\"webgpu\",kernelFunc:Gpe};function m0(r,e=!1){let t=r.length,o=ft(t),n=r.map((l,m)=>`uniforms.pad${m}[0]`).join(\",\"),s=r.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${t>1?`[${m}]`:\"\"}`).join(\",\"),a=t>1?`${o}(${n})`:`${n}`,i=t>1?`${o}(${s})`:`${s}`,p=t>1?\"any(paddedCoords < start)\":\"paddedCoords < start\",u=t>1?\"any(paddedCoords >= end)\":\"paddedCoords >= end\",c=t>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,t):\"coords\";return`\n let start = ${a};\n let end = ${i};\n if (${p} || ${u}) {\n setOutputAtIndex(index, ${e?0:\"uniforms.constantValue\"});\n } else {\n let coords = paddedCoords - start;\n setOutputAtIndex(index, getX(${c}));\n }\n `}var ey=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let paddedCoords = getCoordsFromIndex(index);\n ${m0(this.xShape)}\n }\n }\n `}};var Hpe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return At({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return vt({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:\"float32\",data:[a]}];s.map(u=>i.push({type:\"int32\",data:[u[0],u[1]]}));let p=new ey(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},tU={kernelName:es,backendName:\"webgpu\",kernelFunc:Hpe};var Kpe=et({opType:fe.POW}),rU={kernelName:ts,backendName:\"webgpu\",kernelFunc:Kpe};function qpe(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Ii(fe.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],\"float32\")}var oU={kernelName:rs,backendName:\"webgpu\",kernelFunc:qpe};function jpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,\"prod\",t)}var nU={kernelName:os,backendName:\"webgpu\",kernelFunc:jpe};var Xpe=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=Pz(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},sU={kernelName:ma,backendName:\"webgpu\",kernelFunc:Xpe};var Ype=et({opType:fe.DIV}),aU={kernelName:fn,backendName:\"webgpu\",kernelFunc:Ype};var Qpe=ye({opType:Z.RECIPROCAL}),iU={kernelName:ns,backendName:\"webgpu\",kernelFunc:Qpe};var Zpe=ye({opType:Z.RELU}),uU={kernelName:ss,backendName:\"webgpu\",kernelFunc:Zpe};var Jpe=ye({opType:Z.RELU6}),pU={kernelName:us,backendName:\"webgpu\",kernelFunc:Jpe};var ty=class{constructor(e,t,o){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, halfPixelCenters : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC =\n (vec2(rc) + vec2(uniforms.halfPixelCenters)) *\n effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters);\n\n // Compute the four integer indices.\n let sourceFloorRC = vec2(sourceFracIndexRC);\n let sourceCeilRC = vec2(\n min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC)));\n\n let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n let fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n let top = topLeft + (topRight - topLeft) * fracRC.y;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n let newValue = top + (bottom - top) * fracRC.x;\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function ece(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[i?.5:0]}],f=new ty(n.shape,p,u);return t.runWebGPUProgram(f,[n],\"float32\",d)}var cU={kernelName:is,backendName:\"webgpu\",kernelFunc:ece};var ry=class{constructor(e,t){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32,\n invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeBilinearBackprop_${t}`}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(startRLerp - f32(uniforms.winHeight / 2));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(startCLerp - f32(uniforms.winWidth / 2));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let dxR = f32(dyR) * uniforms.heightScale;\n let topDxRIndex = i32(floor(dxR));\n let bottomDxRIndex = i32(min(ceil(dxR), f32(uniforms.outShape[1] - 1)));\n let dxRLerp = dxR - f32(topDxRIndex);\n let inverseDxRLerp = 1.0 - dxRLerp;\n\n let dxC = f32(dyC) * uniforms.widthScale;\n let leftDxCIndex = i32(floor(dxC));\n let rightDxCIndex = i32(min(ceil(dxC), f32(uniforms.outShape[2] - 1)));\n let dxCLerp = dxC - f32(leftDxCIndex);\n let inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function tce(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ry(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[d]},{type:\"float32\",data:[f]},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return t.runWebGPUProgram(C,[s],s.dtype,S)}var lU={kernelName:Ja,backendName:\"webgpu\",kernelFunc:tce};var oy=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, roundBase : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":e=\"vec2(rc) * effectiveInputOverOutputRatioRC\",`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC = ${e};\n\n // Compute the coordinators of nearest neighbor point.\n let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n let sourceNearestRC = vec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function rce(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[s?.5:0]}],f=new oy(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var mU={kernelName:as,backendName:\"webgpu\",kernelFunc:rce};var ny=class{constructor(e,t){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32,\n winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeNearestNeigborBackprop_${t}`}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(floor(startRLerp - f32(uniforms.winHeight / 2)));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(floor(startCLerp - f32(uniforms.winWidth / 2)));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let sourceFracRow = f32(uniforms.effectiveXSize[0]) *\n (f32(dyR) / f32(uniforms.effectiveYSize[0]));\n\n let sourceFracCol = f32(uniforms.effectiveXSize[1]) *\n (f32(dyC) / f32(uniforms.effectiveYSize[1]));\n\n let sourceNearestRow =\n i32(min(f32(uniforms.outShape[1] - 1),\n ${this.alignCorners?\"floor(sourceFracRow + 0.5)\":\"floor(sourceFracRow)\"}));\n\n let sourceNearestCol =\n i32(min(f32(uniforms.outShape[2] - 1),\n ${this.alignCorners?\"floor(sourceFracCol + 0.5)\":\"floor(sourceFracCol)\"}));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function oce(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ny(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return t.runWebGPUProgram(C,[s],s.dtype,S)}var dU={kernelName:Za,backendName:\"webgpu\",kernelFunc:oce};var sy=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\" axis : vec4,\",this.shaderKey=\"reverse\"}getUserCode(){return`\n \n // Using uniform variables as judging conditions, so the function has\n // coherent execution within all threads.\n fn getReverseCoords(coords : vec4) -> vec4 {\n var reverseCoords = coords;\n if (uniforms.axis[0] == 1) {\n reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;\n }\n if (uniforms.axis[1] == 1) {\n reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;\n }\n if (uniforms.axis[2] == 1) {\n reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;\n }\n if (uniforms.axis[3] == 1) {\n reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;\n }\n\n return reverseCoords;\n }\n \n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let reverseCoords = getReverseCoords(coords);\n setOutputAtIndex(index, getX(reverseCoords[0],\n reverseCoords[1], reverseCoords[2], reverseCoords[3]));\n }\n }\n `}};function nce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return At({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:\"int32\",data:c}],m=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new sy(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=pe({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var fU={kernelName:ps,backendName:\"webgpu\",kernelFunc:nce};var ay=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n uniforms.sinRadians;\n let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n uniforms.cosRadians;\n let coordX = i32(round(coordXFloat + uniforms.centerX));\n let coordY = i32(round(coordYFloat + uniforms.centerY));\n ${this.fillSnippet}\n if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n coordY < uniforms.xShape[1]) {\n outputValue = getX(coords[0], coordY, coordX, coords[3]);\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var hU={kernelName:Ds,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new ay(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:\"float32\",data:[u]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(n)]},{type:\"float32\",data:[Math.cos(n)]}];return typeof s==\"number\"?l.push({type:\"float32\",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:\"float32\",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var sce=ye({opType:Z.ROUND}),gU={kernelName:cs,backendName:\"webgpu\",kernelFunc:sce};var ace=ye({opType:Z.RSQRT,cpuKernelImpl:Oz}),xU={kernelName:ls,backendName:\"webgpu\",kernelFunc:ace};var za=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=[\"updates\",\"indices\"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}_${s.length}`;let u=ft(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",n=\"\",s=\"\";this.dispatchLayout.x.length===1?(n=\"flattenedIndex\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n return index;\n }\n `):this.dispatchLayout.x.length===2&&(n=\"vec2(flattenedIndex, coords[1])\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 {\n // N.B. |updates| could be a scalar tensor, conceptually representing a\n // 2D tensor with all values equal to that. By design, its size must be\n // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n // gives the other.\n let sliceSize = uniforms.outShape[1];\n let d0 = index / sliceSize;\n let d1 = index - d0 * sliceSize;\n return vec2(d0, d1);\n }\n `);let i=`getUpdates(${Array.from({length:this.updatesRank},(u,c)=>`coords[${c}]`).join(\", \")})`;return`\n ${s}\n ${G(\"index\")} {\n if (index < uniforms.updatesSize) {\n let coords = getUpdatesCoordsFromFlatIndex(index);\n var flattenedIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexInside = i32(round(${t}));\n flattenedIndex = flattenedIndex + indexInside * ${o};\n }\n let updateValue =\n ${Su(this.type)}(${i});\n let flatIndex = getOutputIndexFromCoords(${n});\n\n ${this.sumDupeIndices?Qr(\"&result[flatIndex]\",\"updateValue\",this.type):\"atomicStore(&result[flatIndex], bitcast(updateValue));\"}\n }\n }`}};function ice(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=pe({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=vt({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[x]}],C=new za(f.shape,i,d.shape.length,f.shape.length,c,m,h),S=t.runWebGPUProgram(C,[f,d],h,b,g),k=pe({inputs:{x:S},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(S.dataId),k}var yU={kernelName:ms,backendName:\"webgpu\",kernelFunc:ice};var iy=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"sortedSequence\",\"values\"],this.uniforms=\"numInputs : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return`\n fn findBound(batch: i32, value: f32) -> i32 {\n var left = i32(0);\n var right = uniforms.numInputs;\n while (left < right) {\n var mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${this.side===\"left\"?\"<\":\"<=\"} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let value = getValuesByOutputIndex(index);\n setOutputAtIndexI32(index, findBound(coords[0], value));\n }\n }\n `}};function uce(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new iy([s.shape[0],s.shape[1]],a),p=[{type:\"int32\",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],\"int32\",p)}var bU={kernelName:fs,backendName:\"webgpu\",kernelFunc:uce};var uy=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=o,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[],a=[];for(let i=0;i= 1.0) {\n setOutputAtIndex(index, getA(${t}));\n } else {\n setOutputAtIndex(index, getB(${t}));\n }\n }\n }\n `}};function pce(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new uy(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var CU={kernelName:fa,backendName:\"webgpu\",kernelFunc:pce};var cce=ye({opType:Z.SELU}),wU={kernelName:hs,backendName:\"webgpu\",kernelFunc:cce};var lce=ye({opType:Z.SIGMOID}),SU={kernelName:bs,backendName:\"webgpu\",kernelFunc:lce};var mce=ye({opType:Z.SIGN}),IU={kernelName:ys,backendName:\"webgpu\",kernelFunc:mce};var dce=ye({opType:Z.SIN}),vU={kernelName:gs,backendName:\"webgpu\",kernelFunc:dce};var fce=ye({opType:Z.SINH}),kU={kernelName:xs,backendName:\"webgpu\",kernelFunc:fce};var hce=ye({opType:Z.SOFTPLUS}),NU={kernelName:Cs,backendName:\"webgpu\",kernelFunc:hce};var py=class{constructor(e,t,o,n,s,a){this.variableNames=[\"x\"],this.outputShape=[],this.uniforms=\"\",this.workgroupSize=[64,1,1],this.size=!0;let i=new Array(n.length);for(let p=0;p{this.uniforms+=` pad${u} : vec2,`}),this.shaderKey=`spaceToBatchND_${s}`}getUserCode(){let e=ft(this.outputShape.length),t=e0(this.newDim);return`\n ${cm(this.paddedXShape,\"PaddedX\")}\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${e}(${t}), uniforms.reshapedPaddedXShape);\n let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex);\n ${m0(this.xShape,!0)}\n }\n }\n `}};var gce=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=[[0,0]];p.push(...a);for(let b=1+s.length;bb[0]+n.shape[C]+b[1]),c=w.getReshaped(u,s,i,!1),l=w.getPermuted(c.length,s.length,!1),m=w.getReshapedPermuted(u,s,i,!1),d=y.computeStrides(u),f=new py(n.shape,u,p,c,l,d.length),h=[{type:\"int32\",data:c},{type:\"int32\",data:d}];p.map(b=>h.push({type:\"int32\",data:[b[0],b[1]]}));let g=t.runWebGPUProgram(f,[n],n.dtype,h),x=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeData(g.dataId),x},TU={kernelName:ga,backendName:\"webgpu\",kernelFunc:gce};var cy=class{constructor(e,t,o){this.variableNames=[\"input\",\"indices\",\"segmentIds\"],this.outputShape=[],this.uniforms=\"segmentSize : i32, sparseSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e,this.type=o,this.dispatchLayout=X([t]),this.dispatch=H(this.dispatchLayout,[t],this.workgroupSize),this.shaderKey=\"sparseSegmentSum\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.sparseSize) {\n let indexInSegmentIds = index / uniforms.segmentSize;\n let indexInSegment = index % uniforms.segmentSize;\n let indexInInput = indices[indexInSegmentIds];\n let segmentId = segmentIds[indexInSegmentIds];\n\n let value = input[indexInInput * uniforms.segmentSize + indexInSegment];\n let outIndex = segmentId * uniforms.segmentSize + indexInSegment;\n ${Qr(\"&result[outIndex]\",\"value\",this.type)}\n }\n }\n `}},ly=class{constructor(e,t){this.variableNames=[\"segmentIds\"],this.outputShape=[],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=[e],this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.shaderKey=\"sparseSegmentIdCountProgram\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.segmentIdsShape) {\n let segmentId = segmentIds[index];\n ${Qr(\"&result[segmentId]\",\"1\",\"int32\")}\n }\n }\n `}},my=class{constructor(e,t){this.variableNames=[\"segmentSum\",\"sameSegmentIdCount\"],this.outputShape=[],this.uniforms=\"segmentSize : i32\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.type=t,this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),this.shaderKey=\"sparseSegmentMean\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let segmentId = index / uniforms.segmentSize;\n let count = sameSegmentIdCount[segmentId];\n if (count != 0) {\n ${this.type===\"float32\"?\"setOutputAtIndex(index, segmentSum[index] / f32(count));\":\"setOutputAtIndexI32(index, segmentSum[index] / count);\"}\n }\n }\n }\n `}};function dy(r,e,t,o=!1,n){let a=y.sizeFromShape(r.shape)/r.shape[0],i=r.dtype,p=y.sizeFromShape(e.shape),u=n.readSync(t.dataId),l=p>0?u[p-1]+1:0,m,d=r.shape.slice();d[0]=l;let f=p*a,h=vt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new cy(d,f,i);let g=[{type:\"int32\",data:[a]},{type:\"int32\",data:[f]}],x=n.runWebGPUProgram(m,[r,e,t],i,g,h);if(o)return x;let b=vt({backend:n,attrs:{shape:[l],value:0,dtype:\"int32\"}});m=new ly(l,t.shape);let C=n.runWebGPUProgram(m,[t],\"int32\",null,b),S=vt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new my(d,i),g=[{type:\"int32\",data:[a]}];let k=n.runWebGPUProgram(m,[x,C],i,g,S);return n.disposeData(x.dataId),n.disposeData(C.dataId),k}function xce(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;return dy(o,n,s,!1,t)}var _U={kernelName:ya,backendName:\"webgpu\",kernelFunc:xce};function yce(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;return dy(o,n,s,!0,t)}var EU={kernelName:ba,backendName:\"webgpu\",kernelFunc:yce};var fy=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=Uz(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new fy(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var $U={kernelName:po,backendName:\"webgpu\",kernelFunc:Cm};function Cce(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let R=t.bufferSync(n),D=t.bufferSync(s),P=y.decodeString(t.readSync(a.dataId)[0]),O=Mz(R,D,i,m,c,u,p,l,P,d);return t.makeTensorInfo(i,O.dtype,O.values)}let f=[m/c,c],h=pe({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?pe({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):At({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=pe({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),S=Cm({inputs:{x:C},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:\"int32\",data:[p]},{type:\"int32\",data:l},{type:\"int32\",data:[k]}];switch(u){case 0:break;case 1:{let R=new za([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(R,[g,h],x,_,S)}break;default:{let R=new za([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(R,[b,h],x,_,S)}{let R=new za([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(R,[g,h],x,_,S)}}let $=pe({inputs:{x:S},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(C.dataId),t.disposeData(b.dataId),t.disposeData(S.dataId),$}var RU={kernelName:vs,backendName:\"webgpu\",kernelFunc:Cce};function wce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Hs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var DU={kernelName:xa,backendName:\"webgpu\",kernelFunc:wce};var Sce=ye({opType:Z.SQRT}),AU={kernelName:ws,backendName:\"webgpu\",kernelFunc:Sce};var FU={kernelName:qi,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Jr(t.shape,Z.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var Ice=et({opType:fe.SQUARED_DIFFERENCE}),PU={kernelName:ks,backendName:\"webgpu\",kernelFunc:Ice};function vce({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=new Jr(o.shape,Z.STEP,\"stepAlpha : f32,\"),s=[{type:\"float32\",data:[e.alpha]}];return t.runWebGPUProgram(n,[o],o.dtype,s)}var OU={kernelName:wo,backendName:\"webgpu\",kernelFunc:vce};var hy=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=ft(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, getX(${t}));\n }\n }\n `}};function kce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=pt.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=pe({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=pt.computeOutShape(b,C,S),$=Hs({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=pe({inputs:{x:$},backend:t,attrs:{shape:f}}),t.disposeData($.dataId)}else if(t.shouldExecuteOnCPU([n])){let $=t.readSync(n.dataId),R=me(n.shape,n.dtype,$),D=zz(d,R,S,b);k=t.makeTensorInfo(f,n.dtype,D.values)}else{let $=new hy(d),R=[{type:\"int32\",data:b},{type:\"int32\",data:S}],D=t.runWebGPUProgram($,[n],n.dtype,R);k=pe({inputs:{x:D},backend:t,attrs:{shape:f}}),t.disposeData(D.dataId)}return k}var MU={kernelName:Ns,backendName:\"webgpu\",kernelFunc:kce};function Nce(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=Vz(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var LU={kernelName:Ca,backendName:\"webgpu\",kernelFunc:Nce};var Tce=et({opType:fe.SUB,cpuKernelImpl:Wz,supportsComplex:!0}),BU={kernelName:Ts,backendName:\"webgpu\",kernelFunc:Tce};var _ce=ye({opType:Z.TAN}),zU={kernelName:_s,backendName:\"webgpu\",kernelFunc:_ce};var Ece=ye({opType:Z.TANH}),VU={kernelName:Es,backendName:\"webgpu\",kernelFunc:Ece};function $ce(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return t.makeTensorInfo(n.shape,s.dtype);let d=[],f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,i]}});d.push(f);let h=pe({inputs:{x:a},backend:t,attrs:{shape:[p,u]}});d.push(h);let g=pe({inputs:{x:n},backend:t,attrs:{shape:m}});d.push(g);let x=Cm({inputs:{x:g},backend:t,attrs:{reps:Array(m.length).fill(1)}}),b=new za([p,u],i,f.shape.length,h.shape.length,c,m,n.dtype,!1),C=y.sizeFromShape([p,u]),S=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[C]}],k=t.runWebGPUProgram(b,[h,f],g.dtype,S,x);d.push(k);let _=pe({inputs:{x:k},backend:t,attrs:{shape:n.shape}});return d.forEach($=>t.disposeData($.dataId)),_}var WU={kernelName:ds,backendName:\"webgpu\",kernelFunc:$ce};var gy=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced\n // above, Figure5(a) shows that element[1] is in the second half of\n // the group when group size is 2, but it is in the first half of\n // the group when group size is 4.\n let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n var i = 0;\n if (isFirstInPair) {\n i = elemIdx;\n } else {\n i = elemIdx - uniforms.inc;\n }\n\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.inc;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.inc));\n }\n\n var x0 = f32(0.0);\n var x1 = f32(0.0);\n if (i0 < uniforms.inputSize) {\n x0 = getX(batch, i0);\n } else {\n x0 = uniforms.negativeInf;\n }\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = uniforms.negativeInf;\n }\n\n let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) {\n // Elements in opposite order of direction\n let iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}},xy=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _\n // (k=4), we only need to output the indices at positions |, the\n // indices at positions _ can be thrown away, see Figure5(b) After\n // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n // above.\n // For example, the paper shows we only need to output the orange\n // bars. The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back to\n // the previous sequence to find the corresponding value, we need\n // to double the index. When we double the index, we basically\n // interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n // position of each 2k positions by - elemIdx % k. E.g. for output\n // at index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n var i = 0;\n if (elemIdx < uniforms.k) {\n i = elemIdx;\n } else {\n i = elemIdx * 2 - elemIdx % uniforms.k;\n }\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.k;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.k));\n }\n\n let x0 = getX(batch, i0);\n var x1 = f32(0.0);\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = x0;\n }\n\n if (x0 >= x1) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}};function rl(r,e){e!==null&&r.disposeData(e.dataId)}function UU(r){let e=1;for(;ef===null?[l,l]:[l,f],g=(k,_,$)=>{let R=h(),D=new gy($),O=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[_]}],M=f;f=t.runWebGPUProgram(D,R,\"int32\",O),rl(t,M)};for(let k=1;k=1;$/=2)g(_,$,[c,d])}for(let k=d;k>m;k/=2){let _=h(),$=new xy([c,k/2]),D=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[m]}],P=f;f=t.runWebGPUProgram($,_,\"int32\",D),rl(t,P);let O=m/2,M=O*2;for(let L=O;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=Hs({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),rl(t,x);let b=c0({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});rl(t,l);let C=i.slice(0,-1);C.push(s),x=f,f=pe({inputs:{x:f},attrs:{shape:C},backend:t}),rl(t,x);let S=b;return b=pe({inputs:{x:b},attrs:{shape:C},backend:t}),rl(t,S),[b,f]}var GU={kernelName:$s,backendName:\"webgpu\",kernelFunc:Rce};var yy=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n fn mapCoord(outCoord : f32, len : f32) -> f32{\n var inCoord = outCoord;\n if(uniforms.fillModeId == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n inCoord;\n }\n if (inCoord < -len) {\n inCoord = inCoord + sz2;\n } else {\n inCoord = -inCoord - 1.0;\n }\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n }\n return outCoord;\n }\n fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n channel : i32) -> f32 {\n var outputValue : f32;\n if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = uniforms.fillValue;\n }\n return outputValue;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var outputValue : f32;\n let batch = coords[0];\n let x = coords[2];\n let y = coords[1];\n let channel = coords[3];\n let xf = f32(x);\n let yf = f32(y);\n let a1 = getTransforms(batch, 0);\n let a2 = getTransforms(batch, 1);\n let a3 = getTransforms(batch, 2);\n let b1 = getTransforms(batch, 3);\n let b2 = getTransforms(batch, 4);\n let b3 = getTransforms(batch, 5);\n let c1 = getTransforms(batch, 6);\n let c2 = getTransforms(batch, 7);\n let projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = uniforms.fillValue;\n } else {\n let inX = (a1 * xf + a2 * yf + a3) / projection;\n let inY = (b1 * xf + b2 * yf + b3) / projection;\n let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n if (uniforms.interpolationModeId == 1) {\n let coordY = i32(round(mapY));\n let coordX = i32(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n let yFloor = floor(mapY);\n let xFloor = floor(mapX);\n let yCeil = yFloor + 1.0;\n let xCeil = xFloor + 1.0;\n let valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n let valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};function Dce(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new yy(g),b=a===\"nearest\"?1:2,C;switch(i){case\"constant\":C=1;break;case\"reflect\":C=2;break;case\"wrap\":C=3;break;case\"nearest\":C=4;break;default:C=1;break}let S=[{type:\"int32\",data:[b]},{type:\"int32\",data:[C]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(x,[n,s],\"float32\",S)}var HU={kernelName:Rs,backendName:\"webgpu\",kernelFunc:Dce};function Ace(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var KU={kernelName:wa,backendName:\"webgpu\",kernelFunc:Ace};var by=class{constructor(e,t,o){if(this.outputShape=[],this.variableNames=[\"x\",\"segmentIds\"],this.uniforms=\"numSegments : i32, xSize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t,this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`UnsortedSegmentSum only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"unsortedSegmentSum\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.xSize) {\n let coords = getXCoordsFromIndex(index);\n let b = coords[0];\n let inCol = coords[1];\n\n let segmentId = i32(getSegmentIds(inCol));\n if (segmentId >= 0) {\n let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments;\n let value = getX(b, inCol);\n\n ${Qr(\"&result[flatIndex]\",\"value\",this.type)}\n }\n }\n }\n `}};function Fce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=xr({inputs:{x:n},backend:t,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=pe({inputs:{x:l},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=n.dtype,g=[f.shape[0],a],x=vt({backend:t,attrs:{shape:g,value:0,dtype:h}}),b=new by(f.shape,g,h),C=[{type:\"int32\",data:[a]},{type:\"int32\",data:[y.sizeFromShape(f.shape)]}],S=t.runWebGPUProgram(b,[f,s],h,C,x),k=pe({inputs:{x:S},backend:t,attrs:{shape:m}});p.push(S);let _=k;if(c!=null){p.push(k);let $=w.getUndoAxesPermutation(c);_=xr({inputs:{x:_},backend:t,attrs:{perm:$}})}return p.forEach($=>t.disposeData($.dataId)),_}var qU={kernelName:Qi,backendName:\"webgpu\",kernelFunc:Fce};var Pce=[pz,Kz,qz,jz,Xz,Yz,Zz,Jz,eV,tV,rV,oV,nV,sV,aV,pV,cV,lV,mV,dV,hV,gV,xV,wV,SV,IV,lz,kV,TV,_V,EV,$V,RV,DV,AV,FV,PV,OV,BV,zV,VV,WV,GV,HV,UV,KV,qV,jV,XV,YV,JV,eW,tW,rW,oW,nW,sW,aW,iW,iz,uW,lW,pW,cW,mW,dW,fW,hW,gW,xW,yW,cz,bW,NV,CW,wW,SW,IW,vW,kW,NW,_W,TW,EW,$W,RW,AW,FW,iV,PW,OW,BW,MW,LW,zW,uV,VW,WW,UW,GW,KW,QV,qW,jW,XW,yV,YW,JW,eU,tU,rU,oU,nU,sU,bV,aU,iU,uU,pU,uz,cU,lU,mU,dU,fU,hU,gU,xU,yU,bU,CU,wU,SU,IU,vU,kU,fV,OU,MU,LU,HW,NU,TU,_U,EU,RU,DU,AU,FU,PU,BU,ZV,zU,VU,WU,$U,GU,HU,Qz,KU,qU,QW];for(let r of Pce)ti(r);var jU=\"4.22.0\",Oce=\"4.22.0\",Mce=\"4.22.0\",Lce=\"4.22.0\",Bce=\"4.22.0\",zce=\"4.22.0\",Vce={tfjs:jU,\"tfjs-core\":jU,\"tfjs-converter\":Oce,\"tfjs-backend-cpu\":Mce,\"tfjs-backend-webgl\":Lce,\"tfjs-backend-wasm\":Bce,\"tfjs-backend-webgpu\":zce};var EQt=void 0;export{Xs as Abs,Vo as Acos,Wo as Acosh,Ju as AdadeltaOptimizer,ep as AdagradOptimizer,tp as AdamOptimizer,rp as AdamaxOptimizer,uo as Add,Uo as AddN,Go as All,Ho as Any,Ys as ArgMax,Qs as ArgMin,Ko as Asin,qo as Asinh,jo as Atan,Yo as Atan2,Xo as Atanh,Qo as AvgPool,Zs as AvgPool3D,Ri as AvgPool3DGrad,$i as AvgPoolGrad,pm as BackendWasm,Zo as BatchMatMul,Js as BatchToSpaceND,Jo as Bincount,qa as BitwiseAnd,ea as BroadcastArgs,qce as BroadcastTo,yo as Cast,en as Ceil,bo as ClipByValue,Di as Complex,Ai as ComplexAbs,ta as Concat,tn as Conv2D,Fi as Conv2DBackpropFilter,rn as Conv2DBackpropInput,on as Conv3D,ja as Conv3DBackpropFilterV2,nn as Conv3DBackpropInputV2,sn as Cos,an as Cosh,cn as CropAndResize,un as Cumprod,pn as Cumsum,Bo as DataStorage,ra as DenseBincount,ln as DepthToSpace,mn as DepthwiseConv2dNative,Pi as DepthwiseConv2dNativeBackpropFilter,Oi as DepthwiseConv2dNativeBackpropInput,oa as Diag,dn as Dilation2D,Li as Dilation2DBackpropFilter,Mi as Dilation2DBackpropInput,$u as Draw,nw as ENV,Bi as Einsum,hn as Elu,Xa as EluGrad,dl as Environment,xn as Equal,gn as Erf,yn as Exp,na as ExpandDims,bn as Expm1,zi as FFT,sa as Fill,Cn as FlipLeftRight,wn as Floor,Sn as FloorDiv,Du as FromPixels,In as FusedBatchNorm,Io as FusedConv2D,vo as FusedDepthwiseConv2D,bp as GPGPUContext,vn as GatherNd,aa as GatherV2,Bl as GraphModel,kn as Greater,Nn as GreaterEqual,Vi as IFFT,Co as Identity,Wi as Imag,Tn as IsFinite,_n as IsInf,En as IsNan,ao as KernelBackend,Bn as LRN,Ya as LRNGrad,$n as LeakyRelu,Rn as Less,Dn as LessEqual,An as LinSpace,Fn as Log,Pn as Log1p,jce as LogSoftmax,On as LogicalAnd,Mn as LogicalNot,Ln as LogicalOr,R0 as LogicalXor,Xce as LowerBound,xc as MathBackendCPU,Lc as MathBackendWebGL,Yce as MatrixBandPart,zn as Max,Wn as MaxPool,ia as MaxPool3D,Gi as MaxPool3DGrad,Ui as MaxPoolGrad,ua as MaxPoolWithArgmax,Vn as Maximum,Un as Mean,Gn as Min,Hn as Minimum,Kn as MirrorPad,qn as Mod,op as MomentumOptimizer,jn as Multinomial,Xn as Multiply,pa as Neg,Qn as NonMaxSuppressionV3,Qa as NonMaxSuppressionV4,Zn as NonMaxSuppressionV5,Yn as NotEqual,Nw as OP_SCOPE_SUFFIX,Jn as OneHot,ca as OnesLike,kr as Optimizer,Fl as OptimizerConstructors,la as Pack,es as PadV2,Qce as Pool,ts as Pow,rs as Prelu,os as Prod,np as RMSPropOptimizer,Hp as RaggedGather,Kp as RaggedRange,qp as RaggedTensorToTensor,ma as Range,gw as Rank,Hi as Real,fn as RealDiv,ns as Reciprocal,$t as Reduction,ss as Relu,us as Relu6,da as Reshape,is as ResizeBilinear,Ja as ResizeBilinearGrad,as as ResizeNearestNeighbor,Za as ResizeNearestNeighborGrad,ps as Reverse,Ds as RotateWithOffset,cs as Round,ls as Rsqrt,mi as SGDOptimizer,ms as ScatterNd,fs as SearchSorted,fa as Select,hs as Selu,bs as Sigmoid,ys as Sign,gs as Sin,xs as Sinh,ha as Slice,Is as Softmax,Cs as Softplus,ga as SpaceToBatchND,Ki as SparseFillEmptyRows,ei as SparseReshape,ya as SparseSegmentMean,ba as SparseSegmentSum,vs as SparseToDense,xa as SplitV,ws as Sqrt,qi as Square,ks as SquaredDifference,Ru as StaticRegexReplace,wo as Step,Ns as StridedSlice,Ca as StringNGrams,ji as StringSplit,Xi as StringToHashBucketFast,Ts as Sub,Ss as Sum,_s as Tan,Es as Tanh,mt as Tensor,tt as TensorBuffer,ds as TensorScatterUpdate,po as Tile,$s as TopK,Rs as Transform,co as Transpose,Yi as Unique,wa as Unpack,Qi as UnsortedSegmentSum,Zce as UpperBound,ri as Variable,jc as WebGPUBackend,Sa as ZerosLike,So as _FusedMatMul,Qt as abs,Rk as acos,Dk as acosh,Ce as add,Ak as addN,Fk as all,Pk as any,Ok as argMax,Mk as argMin,Lk as asin,Bk as asinh,zk as atan,Vk as atan2,Wk as atanh,dd as avgPool,Hk as avgPool3d,ak as backend,w as backend_util,Kk as basicLSTMCell,nu as batchNorm,jk as batchNorm2d,Xk as batchNorm3d,Yk as batchNorm4d,fd as batchToSpaceND,hd as bincount,Qk as bitwiseAnd,L6 as booleanMaskAsync,Zk as broadcastArgs,su as broadcastTo,Sr as broadcast_util,cT as browser,me as buffer,Ue as cast,Jk as ceil,e2 as clipByValue,Ur as clone,Er as complex,yt as concat,t2 as concat1d,r2 as concat2d,o2 as concat3d,n2 as concat4d,s2 as conv1d,au as conv2d,a2 as conv2dTranspose,i2 as conv3d,p2 as conv3dTranspose,ale as copyRegisteredKernels,c2 as cos,l2 as cosh,$l as cosineWindow,m2 as cumprod,d2 as cumsum,Ir as customGrad,f2 as denseBincount,Tw as deprecationWarn,h2 as depthToSpace,sc as depthwiseConv2d,V5 as deregisterOp,eu as device_util,g2 as diag,x2 as dilation2d,xme as disableDeprecationWarnings,Ot as dispose,yme as disposeVariables,je as div,b2 as divNoNan,C2 as dot,Y6 as dropout,iu as einsum,bd as elu,gme as enableDebugMode,hme as enableProdMode,Zw as enclosingPowerOfTwo,ur as engine,w2 as ensureShape,A as env,yd as equal,S2 as erf,k2 as euclideanNorm,_o as exp,Ms as expandDims,N2 as expm1,Cd as eye,uc as fft,$a as fill,kme as findBackend,Nme as findBackendFactory,wd as floor,md as floorDiv,GD as forceHalfFloat,Jw as fused,Sd as gather,j6 as gatherND,af as gather_util,sk as getBackend,iw as getGradient,Xp as getKernel,Ym as getKernelsForBackend,aae as getThreadsCount,mv as gpgpu_util,VK as grad,WK as grads,Wu as greater,Id as greaterEqual,ju as ifft,pu as imag,eX as image,Z6 as inTopKAsync,di as io,Hd as irfft,T2 as isFinite,_2 as isInf,E2 as isNaN,$r as keep,Vt as kernel_impls,vd as leakyRelu,Tl as less,ac as lessEqual,tX as linalg,$2 as linspace,M8 as loadGraphModel,L8 as loadGraphModelSync,R2 as localResponseNormalization,pi as log,kd as log1p,D2 as logSigmoid,A2 as logSoftmax,_d as logSumExp,Uu as logicalAnd,Ed as logicalNot,$d as logicalOr,F2 as logicalXor,rX as losses,P2 as lowerBound,Ze as matMul,aT as math,Ra as max,Dd as maxPool,O2 as maxPool3d,M2 as maxPoolWithArgmax,Ad as maximum,Gu as mean,bme as memory,L2 as meshgrid,Nl as min,Hu as minimum,B2 as mirrorPad,z2 as mod,V2 as moments,V6 as movingAverage,se as mul,W2 as multiRNNCell,U2 as multinomial,pr as neg,cS as nextFrame,EQt as node,Vu as norm,Fd as notEqual,El as oneHot,Da as ones,G2 as onesLike,N as op,H2 as outerProduct,Aa as pad,K2 as pad1d,q2 as pad2d,j2 as pad3d,X2 as pad4d,Y2 as pool,ui as pow,Od as prelu,ld as print,Q2 as prod,Cme as profile,Z2 as raggedGather,J2 as raggedRange,e1 as raggedTensorToTensor,t1 as rand,S1 as randomGamma,Wd as randomNormal,I1 as randomStandardNormal,ic as randomUniform,v1 as randomUniformInt,cu as range,Ime as ready,ci as real,k1 as reciprocal,tu as registerBackend,ole as registerGradient,ti as registerKernel,z5 as registerOp,lu as relu,Ud as relu6,vme as removeBackend,W as reshape,mo as reverse,N1 as reverse1d,T1 as reverse2d,_1 as reverse3d,E1 as reverse4d,pc as rfft,Gd as round,$1 as rsqrt,ke as scalar,U6 as scatterND,du as scatter_util,_l as searchSorted,R1 as selu,D1 as separableConv2d,jN as serialization,Sme as setBackend,Tme as setPlatform,sae as setThreadsCount,oae as setWasmPath,nae as setWasmPaths,NI as setWebGLContext,A1 as setdiff1dAsync,Ic as shared,Ea as sigmoid,F1 as sign,Jj as signal,P1 as sin,O1 as sinh,Xe as slice,M1 as slice1d,L1 as slice2d,B1 as slice3d,z1 as slice4d,pt as slice_util,V1 as softmax,Td as softplus,Pd as spaceToBatchND,oX as sparse,K6 as sparseToDense,Zj as spectral,li as split,Rr as sqrt,Zt as square,Kd as squaredDifference,cc as squeeze,vr as stack,qd as step,W1 as stridedSlice,nX as string,Te as sub,ot as sum,oi as sumOutType,U1 as tan,kl as tanh,ar as tensor,Jt as tensor1d,mu as tensor2d,jd as tensor3d,G1 as tensor4d,H1 as tensor5d,K1 as tensor6d,j1 as tensorScatterUpdate,rk as tensor_util,w1 as test_util,De as tidy,uu as tile,wme as time,X1 as topk,OGe as train,mc as transpose,Y1 as truncatedNormal,Q1 as unique,sle as unregisterGradient,nle as unregisterKernel,Z1 as unsortedSegmentSum,fo as unstack,dt as upcastType,J1 as upperBound,y as util,UK as valueAndGrad,GK as valueAndGrads,eN as variable,Vw as variableGrads,Vce as version,z8 as version_converter,OX as version_core,yY as version_cpu,iae as version_wasm,d9 as version_webgl,$at as webgl,Ec as webgl_util,Zv as webgpu_util,lo as where,Yd as whereAsync,Gr as zeros,Gt as zerosLike};\n", "import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial, config: Partial, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n for (const key of Object.keys(config)) {\n if (typeof config[key] === 'object') {\n validate(defaults[key], config[key], key, msgs);\n } else {\n const defined = defaults && (typeof defaults[key] !== 'undefined');\n if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n const same = defaults && typeof defaults[key] === typeof config[key];\n if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n }\n // ok = ok && defined && same;\n }\n if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: number[]) => data.reduce((acc: number[], val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n await waiting;\n}\n", "/* eslint-disable no-multi-spaces */\n\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu' | 'none';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/** Possible segmentation model behavior */\nexport type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n /** is module enabled? */\n enabled: boolean,\n /** path to model json file (relative to `modelBasePath` */\n modelPath: string,\n /** how many max frames to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipFrames: number,\n /** how many max milliseconds to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n /** is face rotation correction performed after detecting face?\n * used to correctly analyze faces under high angles\n */\n rotation: boolean,\n /** maximum number of detected faces */\n maxDetected: number,\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n /** minimum size in pixels of a detected face box before resutls are discared */\n minSize: number,\n /** minimum overlap between two detected faces before one is discarded */\n iouThreshold: number,\n /** how much should face box be enlarged over the min/max facial coordinates */\n scale: number,\n /** automatically pad image to square */\n square: boolean,\n /** should child models perform on masked image of a face */\n mask: boolean,\n /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n * if enabled it must be manually deallocated to avoid memory leak */\n return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n /** Keep detected faces that cannot be verified using facemesh */\n keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {\n /** how much should iris box be enlarged over the min/max iris coordinates */\n scale: number,\n}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n /** minimum confidence for a detected race before results are discarded */\n minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n detector: Partial,\n mesh: Partial,\n attention: Partial,\n iris: Partial,\n description: Partial,\n emotion: Partial,\n antispoof: Partial,\n liveness: Partial,\n gear: Partial,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n /** maximum number of detected bodies */\n maxDetected: number,\n /** minimum confidence for a detected body before results are discarded */\n minConfidence: number,\n /* experimental\n /** experimental: detector used for body model before actual analysis\n detector?: {\n /** experimental: enable body detector before body landmarks\n enabled: boolean,\n /** experimental: path to optional body detector model json file\n modelPath: string,\n /** experimental: minimum confidence for a detected body before results are discarded\n minConfidence: number,\n /** experimental: minimum overlap between two detected bodies before one is discarded\n iouThreshold: number\n },\n */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n /** should hand rotation correction be performed after hand detection? */\n rotation: boolean,\n /** minimum confidence for a detected hand before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected hands before one is discarded */\n iouThreshold: number,\n /** maximum number of detected hands */\n maxDetected: number,\n /** should hand landmarks be detected or just return detected hand box */\n landmarks: boolean,\n detector: {\n /** path to hand detector model json */\n modelPath?: string,\n },\n skeleton: {\n /** path to hand skeleton model json */\n modelPath?: string,\n },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n /** minimum confidence for a detected objects before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected objects before one is discarded */\n iouThreshold: number,\n /** maximum number of detected objects */\n maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n /** downsample ratio, adjust to reflect approximately how much of input is taken by body */\n ratio: number,\n /** possible rvm segmentation mode */\n mode: SegmentationEnum,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n /** are image filters enabled? */\n enabled: boolean,\n /** perform image histogram equalization\n * - equalization is performed on input as a whole and detected face before its passed for further analysis\n */\n equalization: boolean,\n /** resize input width\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n width: number,\n /** resize input height\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n height: number,\n /** return processed canvas imagedata in result */\n return: boolean,\n /** flip input as mirror image */\n flip: boolean,\n /** apply auto-brighness */\n autoBrightness: boolean,\n /** range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** image negative */\n negative: boolean,\n /** image sepia colors */\n sepia: boolean,\n /** image vintage colors */\n vintage: boolean,\n /** image kodachrome colors */\n kodachrome: boolean,\n /** image technicolor colors */\n technicolor: boolean,\n /** image polaroid camera effect */\n polaroid: boolean,\n /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n /** is gesture detection enabled? */\n enabled: boolean,\n}\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n /** Backend used for TFJS operations\n * valid build-in backends are:\n * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n * - NodeJS: `cpu`, `wasm`, `tensorflow`\n * default: `webgl` for browser and `tensorflow` for nodejs\n */\n backend: BackendEnum,\n\n /** Path to *.wasm files if backend is set to `wasm`\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPath: string,\n\n /** Force WASM loader to use platform fetch\n *\n * default: false\n */\n wasmPlatformFetch: boolean,\n\n /** Print debug statements to console\n *\n * default: `true`\n */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially\n *\n * default: `true`\n */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - used by `webgl`, `humangl` and `webgpu` backends\n *\n * default: `full`\n */\n warmup: WarmupEnum,\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n *\n * default: `../models/` for browsers and `file://models/` for nodejs\n */\n modelBasePath: string,\n\n /** Cache models in IndexDB on first sucessfull load\n * default: true if indexdb is available (browsers), false if its not (nodejs)\n */\n cacheModels: boolean,\n\n /** Validate kernel ops used in model during model load\n * default: true\n * any errors will be printed on console but will be treated as non-fatal\n */\n validateModels: boolean,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n *\n * default: 0.7\n */\n cacheSensitivity: number;\n\n /** Explicit flags passed to initialize TFJS */\n flags: Record,\n\n /** Software Kernels\n * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n */\n softwareKernels: boolean,\n\n /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n deallocate: boolean;\n\n /** Internal Variable */\n skipAllowed: boolean;\n\n /** Filter config {@link FilterConfig} */\n filter: Partial,\n\n /** Gesture config {@link GestureConfig} */\n gesture: Partial;\n\n /** Face config {@link FaceConfig} */\n face: Partial,\n\n /** Body config {@link BodyConfig} */\n body: Partial,\n\n /** Hand config {@link HandConfig} */\n hand: Partial,\n\n /** Object config {@link ObjectConfig} */\n object: Partial,\n\n /** Segmentation config {@link SegmentationConfig} */\n segmentation: Partial,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n backend: '',\n modelBasePath: '',\n cacheModels: true,\n validateModels: true,\n wasmPath: '',\n wasmPlatformFetch: false,\n debug: false,\n async: true,\n warmup: 'full',\n cacheSensitivity: 0.70,\n skipAllowed: false,\n deallocate: false,\n flags: {},\n softwareKernels: false,\n filter: {\n enabled: true,\n equalization: false,\n width: 0,\n height: 0,\n flip: false,\n return: true,\n autoBrightness: true,\n brightness: 0,\n contrast: 0,\n sharpness: 0,\n blur: 0,\n saturation: 0,\n hue: 0,\n negative: false,\n sepia: false,\n vintage: false,\n kodachrome: false,\n technicolor: false,\n polaroid: false,\n pixelate: 0,\n },\n gesture: {\n enabled: true,\n },\n face: {\n enabled: true,\n detector: {\n modelPath: 'blazeface.json',\n rotation: false,\n maxDetected: 1,\n skipFrames: 99,\n skipTime: 2500,\n minConfidence: 0.2,\n minSize: 0,\n iouThreshold: 0.1,\n scale: 1.4,\n mask: false,\n return: false,\n },\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json',\n keepInvalid: false,\n },\n attention: {\n enabled: false,\n modelPath: 'facemesh-attention.json',\n },\n iris: {\n enabled: true,\n scale: 2.3,\n modelPath: 'iris.json',\n },\n emotion: {\n enabled: true,\n minConfidence: 0.1,\n skipFrames: 99,\n skipTime: 1500,\n modelPath: 'emotion.json',\n },\n description: {\n enabled: true,\n modelPath: 'faceres.json',\n skipFrames: 99,\n skipTime: 3000,\n minConfidence: 0.1,\n },\n antispoof: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'antispoof.json',\n },\n liveness: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'liveness.json',\n },\n },\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json',\n maxDetected: -1,\n minConfidence: 0.3,\n skipFrames: 1,\n skipTime: 200,\n },\n hand: {\n enabled: true,\n rotation: true,\n skipFrames: 99,\n skipTime: 1000,\n minConfidence: 0.50,\n iouThreshold: 0.2,\n maxDetected: -1,\n landmarks: true,\n detector: {\n modelPath: 'handtrack.json',\n },\n skeleton: {\n modelPath: 'handlandmark-lite.json',\n },\n },\n object: {\n enabled: false,\n modelPath: 'centernet.json',\n minConfidence: 0.2,\n iouThreshold: 0.4,\n maxDetected: 10,\n skipFrames: 99,\n skipTime: 2000,\n },\n segmentation: {\n enabled: false,\n modelPath: 'rvm.json',\n ratio: 0.5,\n mode: 'default',\n },\n};\n\nexport { config as defaults };\n", "export const vertexIdentity = `\n precision highp float;\n attribute vec2 pos;\n attribute vec2 uv;\n varying vec2 vUv;\n uniform float flipY;\n void main(void) {\n vUv = uv;\n gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n }\n`;\n\nexport const fragmentIdentity = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n void main(void) {\n gl_FragColor = texture2D(texture, vUv);\n }\n`;\n\nexport const colorMatrixWithAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n gl_FragColor.a = c.a;\n }\n`;\n\nexport const pixelate = `\n precision highp float;\n varying vec2 vUv;\n uniform vec2 size;\n uniform sampler2D texture;\n vec2 pixelate(vec2 coord, vec2 size) {\n return floor( coord / size ) * size;\n }\n void main(void) {\n gl_FragColor = vec4(0.0);\n vec2 coord = pixelate(vUv, size);\n gl_FragColor += texture2D(texture, coord);\n }\n`;\n\nexport const blur = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n void main(void) {\n gl_FragColor = vec4(0.0);\n gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv )*0.159576912161;\n gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;\n }\n`;\n\nexport const convolution = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n uniform float m[9];\n void main(void) {\n vec4 c11 = texture2D(texture, vUv - px); // top left\n vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n vec4 c22 = texture2D(texture, vUv); // mid center\n vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n gl_FragColor = \n c11 * m[0] + c12 * m[1] + c22 * m[2] +\n c21 * m[3] + c22 * m[4] + c23 * m[5] +\n c31 * m[6] + c32 * m[7] + c33 * m[8];\n gl_FragColor.a = c22.a;\n }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, collection) => {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n};\n\nclass GLProgram {\n uniform = {};\n attribute = {};\n gl: WebGLRenderingContext;\n id: WebGLProgram;\n\n constructor(gl, vertexSource, fragmentSource) {\n this.gl = gl;\n const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n this.id = this.gl.createProgram();\n if (!vertexShader || !fragmentShader) return;\n if (!this.id) {\n log('filter: could not create webgl program');\n return;\n }\n this.gl.attachShader(this.id, vertexShader);\n this.gl.attachShader(this.id, fragmentShader);\n this.gl.linkProgram(this.id);\n if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || 'unknown'}`);\n return;\n }\n this.gl.useProgram(this.id);\n collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n }\n\n compile = (source, type): WebGLShader | null => {\n const shader = this.gl.createShader(type);\n if (!shader) {\n log('filter: could not create shader');\n return null;\n }\n this.gl.shaderSource(shader, source);\n this.gl.compileShader(shader);\n if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || 'unknown'}`);\n return null;\n }\n return shader;\n };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n let drawCount = 0;\n let sourceTexture: WebGLTexture | null = null;\n let lastInChain = false;\n let currentFramebufferIndex = -1;\n let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n let filterChain: Record[] = [];\n let vertexBuffer: WebGLBuffer | null = null;\n let currentProgram: GLProgram | null = null;\n const fxcanvas = canvas(100, 100) as HTMLCanvasElement;\n const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n const DRAW = { INTERMEDIATE: 1 };\n const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n if (!gl) {\n log('filter: cannot get webgl context');\n return;\n }\n // @ts-ignore used for sanity checks outside of imagefx\n this.gl = gl;\n\n function resize(width, height) {\n if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n fxcanvas.width = width;\n fxcanvas.height = height;\n if (!vertexBuffer) { // Create the context if we don't have it yet\n const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n vertexBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n tempFramebuffers = [null, null]; // Delete old temp framebuffers\n }\n\n function createFramebufferTexture(width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n }\n\n function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n }\n\n function draw(flags = 0) {\n if (!currentProgram) return;\n let source: WebGLTexture | null = null;\n let target: WebGLFramebuffer | null = null;\n let flipY = false;\n if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n drawCount++;\n if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n target = null;\n flipY = drawCount % 2 === 0;\n } else {\n currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n }\n gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n }\n\n function compileShader(fragmentSource): GLProgram | null {\n if (shaderProgramCache[fragmentSource]) {\n currentProgram = shaderProgramCache[fragmentSource];\n gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n return currentProgram;\n }\n currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n if (!currentProgram) {\n log('filter: could not get webgl program');\n return null;\n }\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n shaderProgramCache[fragmentSource] = currentProgram;\n return currentProgram;\n }\n\n const filter = {\n colorMatrix: (matrix: number[]) => { // general color matrix filter\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n ? shaders.colorMatrixWithoutAlpha\n : shaders.colorMatrixWithAlpha;\n const program = compileShader(shader);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n draw();\n },\n\n brightness: (brightness: number) => {\n const b = (brightness || 0) + 1;\n filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n saturation: (amount: number) => {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturate: () => {\n filter.saturation(-1);\n },\n\n contrast: (amount: number) => {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n negative: () => {\n filter.contrast(-2);\n },\n\n hue: (rotation: number) => {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturateLuminance: () => {\n filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n sepia: () => {\n filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n brownie: () => {\n filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n vintagePinhole: () => {\n filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n kodachrome: () => {\n filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n technicolor: () => {\n filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n polaroid: () => {\n filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n shiftToBGR: () => {\n filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n convolution: (matrix: number[]) => { // general convolution Filter\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / fxcanvas.width;\n const pixelSizeY = 1 / fxcanvas.height;\n const program = compileShader(shaders.convolution);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n draw();\n },\n\n detectEdges: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n },\n\n sobelX: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n },\n\n sobelY: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n },\n\n sharpen: (amount) => {\n const a = amount || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n },\n\n emboss: (size: number) => {\n const s = size || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n },\n\n blur: (size: number) => {\n const blurSizeX = (size / 7) / fxcanvas.width;\n const blurSizeY = (size / 7) / fxcanvas.height;\n const program = compileShader(shaders.blur);\n if (!program) return;\n // Vertical\n gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n draw();\n },\n\n pixelate: (size: number) => {\n const blurSizeX = (size) / fxcanvas.width;\n const blurSizeY = (size) / fxcanvas.height;\n const program = compileShader(shaders.pixelate);\n if (!program) return;\n gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n draw();\n },\n };\n\n // @ts-ignore this\n this.add = function (name) {\n const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\n const func = filter[name];\n filterChain.push({ func, args });\n };\n\n // @ts-ignore this\n this.reset = function () {\n filterChain = [];\n };\n\n // @ts-ignore this\n this.get = function () {\n return filterChain;\n };\n\n // @ts-ignore this\n this.apply = function (image) {\n resize(image.width, image.height);\n drawCount = 0;\n if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n for (let i = 0; i < filterChain.length; i++) {\n lastInChain = (i === filterChain.length - 1);\n const f = filterChain[i];\n // @ts-ignore function assigment\n f.func.apply(this, f.args || []);\n }\n return fxcanvas;\n };\n\n // @ts-ignore this\n this.draw = function (image) {\n this.add('brightness', 0);\n return this.apply(image);\n };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise {\n const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n const rgb = tf.split(squeeze, 3, 2);\n const min: Tensor[] = [tf.min(rgb[0]), tf.min(rgb[1]), tf.min(rgb[2])]; // minimum pixel value per channel T[]\n const max: Tensor[] = [tf.max(rgb[0]), tf.max(rgb[1]), tf.max(rgb[2])]; // maximum pixel value per channel T[]\n // const absMin = await Promise.all(min.map((channel) => channel.data())); // minimum pixel value per channel A[]\n // const minValue = Math.min(absMax[0][0], absMin[1][0], absMin[2][0]);\n const absMax = await Promise.all(max.map((channel) => channel.data())); // maximum pixel value per channel A[]\n const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n const maxRange = maxValue > 1 ? 255 : 1;\n const factor = maxRange / maxValue;\n let final: Tensor;\n if (factor > 1) {\n const sub = [tf.sub(rgb[0], min[0]), tf.sub(rgb[1], min[1]), tf.sub(rgb[2], min[2])]; // channels offset by min values\n const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])]; // channel ranges\n // const fact = [tf.div(maxRange, absMax[0]), tf.div(maxRange, absMax[1]), tf.div(maxRange, absMax[1])]; // factors between\n const enh = [tf.mul(sub[0], factor), tf.mul(sub[1], factor), tf.mul(sub[2], factor)];\n const stack = tf.stack([enh[0], enh[1], enh[2]], 2);\n final = tf.reshape(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);\n tf.dispose([...sub, ...range, ...enh, stack]);\n } else {\n final = tf.expandDims(squeeze, 0);\n }\n tf.dispose([...rgb, ...min, ...max, rgb, squeeze, inputImage]);\n return final;\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Config } from '../exports';\nimport type { Tensor, Tensor3D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // eslint-disable-line @typescript-eslint/no-redundant-type-constituents\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n inputSum: 0,\n cacheDiff: 1,\n sumMethod: 0,\n inputTensor: undefined,\n};\n\nexport function reset() {\n last.inputSum = 0;\n last.cacheDiff = 1;\n last.sumMethod = 0;\n last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n let c: AnyCanvas;\n if (env.browser) { // browser defines canvas object\n if (env.worker) { // if runing in web worker use OffscreenCanvas\n if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n c = new OffscreenCanvas(width, height);\n } else { // otherwise use DOM canvas\n if (typeof document !== 'undefined') {\n c = document.createElement('canvas');\n c.width = width;\n c.height = height;\n } else if (typeof navigator !== 'undefined' && navigator.product === 'ReactNative') {\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n else throw new Error('canvas error: attempted to use canvas in react-native without canvas support installed');\n } else {\n throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n }\n }\n } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n }\n // @ts-ignore its either defined or we already threw an error\n return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n const outputCanvas = output || canvas(input.width, input.height);\n const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctx.drawImage(input, 0, 0);\n return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor4D | null, canvas: AnyCanvas | null }> {\n if (!input) {\n // throw new Error('input is missing');\n if (config.debug) log('input error: input is missing');\n return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n }\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('input error: type not recognized');\n }\n if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n let tensor: Tensor | null = null;\n if (input['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n if (!(input as Tensor).shape) throw new Error('input error: attempted to use tensor without a shape');\n if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n tensor = tf.expandDims(input, 0);\n } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n const rgb = tf.slice3d(input as Tensor3D, [0, 0, 0], [-1, -1, 3]);\n tensor = tf.expandDims(rgb, 0);\n tf.dispose(rgb);\n }\n } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n tensor = tf.clone(input);\n } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n tensor = tf.slice4d(input as Tensor4D, [0, 0, 0, 0], [-1, -1, -1, 3]);\n }\n }\n // at the end shape must be [1, height, width, 3]\n if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${((input as Tensor).shape).toString()}`);\n if ((tensor).dtype === 'int32') {\n const cast = tf.cast(tensor, 'float32');\n tf.dispose(tensor);\n tensor = cast;\n }\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n }\n // check if resizing will be needed\n if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).readyState <= 2) {\n if (config.debug) log('input stream is not ready');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n const originalWidth: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n const originalHeight: number = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n if (!originalWidth || !originalHeight) {\n if (config.debug) log('cannot determine input dimensions');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n let targetWidth: number = originalWidth;\n let targetHeight: number = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n }\n\n // create our canvas and resize it if needed\n if ((config.filter?.width || 0) > 0) targetWidth = config.filter.width as number;\n else if ((config.filter?.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n if ((config.filter.height || 0) > 0) targetHeight = config.filter.height as number;\n else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n // draw input to our canvas\n const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n inCtx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n inCtx.translate(originalWidth, 0);\n inCtx.scale(-1, 1);\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n }\n }\n\n if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n // imagefx transforms using gl from input canvas to output canvas\n if (config.filter.enabled && env.webgl.supported) {\n if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n env.filter = !!fx;\n if (!fx?.add) {\n if (config.debug) log('input process error: cannot initialize filters');\n env.webgl.supported = false;\n config.filter.enabled = false;\n copy(inCanvas, outCanvas); // filter failed to initialize\n // return { tensor: null, canvas: inCanvas };\n } else {\n fx.reset();\n if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n if (config.filter.negative) fx.add('negative');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.vintage) fx.add('brownie');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.kodachrome) fx.add('kodachrome');\n if (config.filter.technicolor) fx.add('technicolor');\n if (config.filter.polaroid) fx.add('polaroid');\n if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n if (fx.get()?.length > 1) outCanvas = fx.apply(inCanvas);\n else outCanvas = fx.draw(inCanvas);\n }\n } else {\n copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n if (fx) fx = null;\n env.filter = !!fx;\n }\n\n if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n // create tensor from image unless input was a tensor already\n let pixels;\n let depth = 3;\n if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || ((input as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n if (env.browser && tf.browser) {\n pixels = tf.browser ? tf.browser.fromPixels(input as ImageData) : null;\n } else {\n depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n // const arr = Uint8Array.from(input['data']);\n const arr = new Uint8Array((input as ImageData).data.buffer);\n pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).width, depth], 'int32');\n }\n } else {\n if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n if (tf.browser && env.browser) {\n if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n pixels = tf.browser.fromPixels(outCanvas as HTMLCanvasElement); // safe to reuse since both backend and context are gl based\n } else {\n tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n pixels = tf.browser.fromPixels(tmpCanvas as HTMLCanvasElement);\n }\n } else {\n const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n depth = tempData.data.length / targetWidth / targetHeight;\n const arr = new Uint8Array(tempData.data.buffer);\n pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n }\n }\n if (depth === 4) { // rgba to rgb\n const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n tf.dispose(pixels);\n pixels = rgb;\n }\n if (!pixels) throw new Error('input error: cannot create tensor');\n const casted: Tensor = tf.cast(pixels, 'float32');\n const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n tf.dispose([pixels, casted]);\n\n if (config.filter.autoBrightness) {\n const max = tf.max(tensor);\n const maxVal = await max.data();\n config.filter.brightness = maxVal[0] > 1 ? (1 - maxVal[0] / 255) : (1 - maxVal[0]);\n tf.dispose(max);\n }\n\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n}\n\n/*\nconst checksum = async (input: Tensor): Promise => { // use tf sum or js based sum loop depending on which is faster\n const resizeFact = 48;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n const tfSum = async (): Promise => {\n const sumT = tf.sum(reduced);\n const sum0 = await sumT.data();\n tf.dispose(sumT);\n return sum0[0];\n };\n const jsSum = async (): Promise => {\n const reducedData = await reduced.data(); // raw image rgb array\n let sum0 = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n return sum0;\n };\n if (last.sumMethod === 0) {\n const t0 = now();\n await jsSum();\n const t1 = now();\n await tfSum();\n const t2 = now();\n last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n }\n const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n tf.dispose(reduced);\n return res;\n};\n*/\n\nexport async function skip(config: Partial, input: Tensor) {\n let skipFrame = false;\n if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n /*\n const checkSum = await checksum(input);\n const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n last.inputSum = checkSum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n */\n\n if (!last.inputTensor) {\n last.inputTensor = tf.clone(input);\n } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n tf.dispose(last.inputTensor);\n last.inputTensor = tf.clone(input);\n } else {\n const t: Record = {};\n t.diff = tf.sub(input, last.inputTensor);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n last.inputTensor = tf.clone(input);\n skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n }\n return skipFrame;\n}\n\nexport async function compare(config: Partial, input1: Tensor, input2: Tensor): Promise {\n const t: Record = {};\n if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n return 0;\n }\n if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n return 0;\n }\n t.input1 = tf.clone(input1);\n t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2 as Tensor3D, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n t.diff = tf.sub(t.input1, t.input2);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n return diffRelative;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n /** Running in Browser */\n browser: boolean;\n /** Running in NodeJS */\n node: boolean;\n /** Running in WebWorker thread */\n worker: boolean;\n /** Detected platform */\n platform: string = '';\n /** Detected agent */\n agent: string = '';\n /** List of supported backends */\n backends: string[] = [];\n /** Has any work been performed so far */\n initial: boolean;\n /** Are image filters supported? */\n filter: boolean | undefined;\n /** TFJS instance details */\n tfjs: {\n version: undefined | string,\n };\n /** Is offscreenCanvas supported? */\n offscreen: undefined | boolean;\n /** Are performance counter instant values or additive */\n perfadd: boolean = false;\n /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n tensorflow: {\n version: undefined | string,\n gpu: undefined | boolean,\n } = {\n version: undefined,\n gpu: undefined,\n };\n /** WASM detected capabilities */\n wasm: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n simd: undefined | boolean,\n multithread: undefined | boolean,\n } = {\n supported: undefined,\n backend: undefined,\n simd: undefined,\n multithread: undefined,\n };\n /** WebGL detected capabilities */\n webgl: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n version: undefined | string,\n renderer: undefined | string,\n shader: undefined | string,\n vendor: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n version: undefined,\n renderer: undefined,\n shader: undefined,\n vendor: undefined,\n };\n /** WebGPU detected capabilities */\n webgpu: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n adapter: undefined | GPUAdapterInfo,\n } = {\n supported: undefined,\n backend: undefined,\n adapter: undefined,\n };\n /** CPU info */\n cpu: {\n model: undefined | string,\n flags: string[],\n } = {\n model: undefined,\n flags: [],\n };\n /** List of supported kernels for current backend */\n kernels: string[] = [];\n\n /** MonkeyPatch for Canvas/Image/ImageData */\n #canvas: undefined;\n #image: undefined;\n #imageData: undefined;\n\n get Canvas() { return this.#canvas; }\n set Canvas(val) { this.#canvas = val; globalThis.Canvas = val; }\n get Image() { return this.#image; }\n // @ts-ignore monkey-patch;\n set Image(val) { this.#image = val; globalThis.Image = val; }\n get ImageData() { return this.#imageData; }\n // @ts-ignore monkey-patch;\n set ImageData(val) { this.#imageData = val; globalThis.ImageData = val; }\n\n constructor() {\n this.browser = (typeof navigator !== 'undefined') && (typeof navigator.appVersion !== 'undefined');\n this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n this.tfjs = { version: tf.version['tfjs-core'] };\n this.offscreen = typeof OffscreenCanvas !== 'undefined';\n this.initial = true;\n\n // @ts-ignore WorkerGlobalScope evaluated in browser only\n this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n if ((typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined')) { // TBD replace with navigator.userAgentData once in mainline\n const agent = navigator.userAgent || '';\n const raw = agent.match(/\\(([^()]+)\\)/g);\n if (raw?.[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n this.agent = agent.replace(raw[0], '');\n if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n this.agent = this.agent.replace(/ /g, ' ');\n }\n } else if (typeof process !== 'undefined') {\n this.platform = `${process.platform} ${process.arch}`;\n this.agent = `NodeJS ${process.version}`;\n }\n }\n\n /** update backend information */\n async updateBackend() {\n // analyze backends\n this.backends = Object.keys(tf.engine().registryFactory);\n try { // backend may not be initialized\n this.tensorflow = {\n version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),\n gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),\n };\n } catch { /**/ }\n this.wasm.supported = typeof WebAssembly !== 'undefined';\n this.wasm.backend = this.backends.includes('wasm');\n if (this.wasm.supported && this.wasm.backend) {\n this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n }\n const c = image.canvas(100, 100);\n const gl = c ? c.getContext('webgl2') as WebGL2RenderingContext : undefined; // causes too many gl contexts\n this.webgl.supported = typeof gl !== 'undefined';\n this.webgl.backend = this.backends.includes('webgl');\n if (this.webgl.supported && this.webgl.backend && gl) {\n this.webgl.version = gl.getParameter(gl.VERSION);\n this.webgl.vendor = gl.getParameter(gl.VENDOR);\n this.webgl.renderer = gl.getParameter(gl.RENDERER);\n this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);\n }\n this.webgpu.supported = this.browser && typeof navigator !== 'undefined' && typeof navigator.gpu !== 'undefined';\n this.webgpu.backend = this.backends.includes('webgpu');\n try {\n if (this.webgpu.supported) {\n const adapter = await navigator.gpu.requestAdapter();\n if (adapter) {\n // @ts-ignore requestAdapterInfo is not in tslib\n if ('requestAdapterInfo' in adapter) this.webgpu.adapter = await adapter.requestAdapterInfo();\n // @ts-ignore adapter.info is not in tslib\n else this.webgpu.adapter = await adapter.info;\n }\n }\n } catch {\n this.webgpu.supported = false;\n }\n try {\n this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n } catch { /**/ }\n }\n\n /** update cpu information */\n updateCPU() {\n const cpu = { model: '', flags: [] };\n if (this.node && this.platform.startsWith('linux')) {\n /*\n const fs = require('fs');\n try {\n const data = fs.readFileSync('/proc/cpuinfo').toString();\n for (const line of data.split('\\n')) {\n if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n }\n } catch { }\n */\n }\n if (!this.cpu) Object.defineProperty(this, 'cpu', { value: cpu });\n else this.cpu = cpu;\n }\n}\n\nexport const env = new Env();\n", "import { log } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n /**\n * element can be:\n * - string which indicates dom element id\n * - actual HTMLVideo dom element\n * - undefined in which case a new HTMLVideoElement will be created\n */\n element: string | HTMLVideoElement | undefined,\n /** print messages on console */\n debug: boolean,\n /** use front or back camera */\n mode: 'front' | 'back',\n /** camera crop mode */\n crop: boolean,\n /** desired webcam width */\n width: number,\n /** desired webcam height */\n height: number,\n /** deviceId of the video device to use */\n id?: string,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n /** current webcam configuration */\n config: WebCamConfig;\n /** instance of dom element associated with webcam stream */\n element: HTMLVideoElement | undefined;\n /** active webcam stream */\n stream: MediaStream | undefined;\n /** enumerated video devices */\n devices: MediaDeviceInfo[] = [];\n\n constructor() {\n this.config = {\n element: undefined,\n debug: true,\n mode: 'front',\n crop: false,\n width: 0,\n height: 0,\n };\n }\n\n /** get active webcam stream track */\n public get track(): MediaStreamTrack | undefined {\n if (!this.stream) return undefined;\n return this.stream.getVideoTracks()[0];\n }\n\n /** get webcam capabilities */\n public get capabilities(): MediaTrackCapabilities | undefined {\n if (!this.track) return undefined;\n return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n }\n\n /** get webcam constraints */\n public get constraints(): MediaTrackConstraints | undefined {\n if (!this.track) return undefined;\n return this.track.getConstraints ? this.track.getConstraints() : undefined;\n }\n\n /** get webcam settings */\n public get settings(): MediaTrackSettings | undefined {\n if (!this.stream) return undefined;\n const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n return track.getSettings ? track.getSettings() : undefined;\n }\n\n /** get webcam label */\n public get label(): string {\n if (!this.track) return '';\n return this.track.label;\n }\n\n /** is webcam paused */\n public get paused(): boolean {\n return this.element?.paused || false;\n }\n\n /** webcam current width */\n public get width(): number {\n return this.element?.videoWidth || 0;\n }\n\n /** webcam current height */\n public get height(): number {\n return this.element?.videoHeight || 0;\n }\n\n public enumerate = async (): Promise => {\n try {\n const devices = await navigator.mediaDevices.enumerateDevices();\n this.devices = devices.filter((device) => device.kind === 'videoinput');\n } catch {\n this.devices = [];\n }\n return this.devices;\n };\n\n /** start method initializizes webcam stream and associates it with a dom video element */\n public start = async (webcamConfig?: Partial): Promise => {\n // set config\n if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n if (webcamConfig?.id) this.config.id = webcamConfig?.id;\n\n // use or create dom element\n if (webcamConfig?.element) {\n if (typeof webcamConfig.element === 'string') {\n const el = document.getElementById(webcamConfig.element);\n if (el && el instanceof HTMLVideoElement) {\n this.element = el;\n } else {\n if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n return `webcam error: cannot get dom element: ${webcamConfig.element}`;\n }\n } else if (webcamConfig.element instanceof HTMLVideoElement) {\n this.element = webcamConfig.element;\n } else {\n if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n return `webcam error: unknown dom element: ${webcamConfig.element}`;\n }\n } else {\n this.element = document.createElement('video');\n }\n\n // set constraints to use\n const requestedConstraints: MediaStreamConstraints = {\n audio: false,\n video: {\n facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n // @ts-ignore // resizeMode is still not defined in tslib\n resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n },\n };\n if (this.config?.width > 0) (requestedConstraints.video as MediaTrackConstraints).width = { ideal: this.config.width };\n if (this.config?.height > 0) (requestedConstraints.video as MediaTrackConstraints).height = { ideal: this.config.height };\n if (this.config.id) (requestedConstraints.video as MediaTrackConstraintSet).deviceId = this.config.id;\n\n // set default event listeners\n this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n if (!this.element || !this.stream) return;\n if (this.element.paused) await this.element.play();\n else this.element.pause();\n });\n\n // get webcam and set it to run in dom element\n if (!navigator?.mediaDevices) {\n if (this.config.debug) log('webcam error', 'no devices');\n return 'webcam error: no devices';\n }\n try {\n this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n } catch (err) {\n log('webcam', err);\n return `webcam error: ${err}`;\n }\n if (!this.stream) {\n if (this.config.debug) log('webcam error', 'no stream');\n return 'webcam error no stream';\n }\n this.element.srcObject = this.stream; // assign it to dom element\n const ready = new Promise((resolve) => { // wait until stream is ready\n if (!this.element) resolve(false);\n else this.element.onloadeddata = () => resolve(true);\n });\n await ready;\n await this.element.play(); // start playing\n\n if (this.config.debug) {\n log('webcam', {\n width: this.width,\n height: this.height,\n label: this.label,\n stream: this.stream,\n track: this.track,\n settings: this.settings,\n constraints: this.constraints,\n capabilities: this.capabilities,\n });\n }\n return `webcam: ${this.label}`;\n };\n\n /** pause webcam video method */\n public pause = (): void => {\n if (this.element) this.element.pause();\n };\n\n /** play webcam video method */\n public play = async (): Promise => {\n if (this.element) await this.element.play();\n };\n\n /** stop method stops active webcam stream track and disconnects webcam */\n public stop = (): void => {\n if (this.config.debug) log('webcam', 'stop');\n if (this.track) this.track.stop();\n };\n}\n", "{\n \"antispoof\": 853098,\n \"blazeface\": 538928,\n \"centernet\": 4030290,\n \"emotion\": 820516,\n \"facemesh\": 1477958,\n \"faceres\": 6978814,\n \"handlandmark-lite\": 2023432,\n \"handtrack\": 2964837,\n \"iris\": 2599092,\n \"liveness\": 592976,\n \"models\": 0,\n \"movenet-lightning\": 4650216,\n \"affectnet-mobilenet\": 6920630,\n \"age\": 161240,\n \"blazeface-back\": 538928,\n \"blazeface-front\": 402048,\n \"blazepose-detector\": 5928856,\n \"blazepose-full\": 6339202,\n \"blazepose-heavy\": 27502466,\n \"blazepose-lite\": 2726402,\n \"efficientpose\": 5651240,\n \"faceboxes\": 2013002,\n \"facemesh-attention-pinto\": 2387598,\n \"facemesh-attention\": 2382414,\n \"facemesh-detection-full\": 1026192,\n \"facemesh-detection-short\": 201268,\n \"faceres-deep\": 13957620,\n \"gear-e1\": 112438,\n \"gear-e2\": 112438,\n \"gear\": 1498916,\n \"gender-ssrnet-imdb\": 161236,\n \"gender\": 201808,\n \"handdetect\": 3515612,\n \"handlandmark-full\": 5431368,\n \"handlandmark-sparse\": 5286322,\n \"handskeleton\": 5502280,\n \"meet\": 372228,\n \"mobileface\": 2183192,\n \"mobilefacenet\": 5171976,\n \"movenet-multipose\": 9448838,\n \"movenet-thunder\": 12477112,\n \"nanodet\": 7574558,\n \"posenet\": 5032780,\n \"rvm\": 3739355,\n \"selfie\": 212886,\n \"anti-spoofing\": 853098,\n \"efficientpose-i-lite\": 2269064,\n \"efficientpose-ii-lite\": 5651240,\n \"efficientpose-iv\": 25643252,\n \"insightface-efficientnet-b0\": 13013224,\n \"insightface-ghostnet-strides1\": 8093408,\n \"insightface-ghostnet-strides2\": 8049584,\n \"insightface-mobilenet-emore\": 6938536,\n \"insightface-mobilenet-swish\": 12168584,\n \"nanodet-e\": 12319156,\n \"nanodet-g\": 7574558,\n \"nanodet-m\": 1887474,\n \"nanodet-t\": 5294216\n}", "import * as tf from 'dist/tfjs.esm.js';\nimport { log, join } from '../util/util';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n cacheModels: true,\n cacheSupported: true,\n verbose: true,\n debug: false,\n modelBasePath: '',\n};\n\nexport interface ModelInfo {\n name: string,\n loaded: boolean,\n inCache: boolean,\n sizeDesired: number,\n sizeFromManifest: number,\n sizeLoadedWeights: number,\n url: string,\n}\n\nexport const modelStats: Record = {};\n\nasync function httpHandler(url: string, init?: RequestInit): Promise {\n if (options.debug) log('load model fetch:', url, init);\n return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n options.cacheModels = config.cacheModels;\n options.verbose = config.debug;\n options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise {\n let modelUrl = join(options.modelBasePath, modelPath || '');\n if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n modelStats[shortModelName] = {\n name: shortModelName,\n loaded: false,\n sizeFromManifest: 0,\n sizeLoadedWeights: 0,\n sizeDesired: modelsDefs[shortModelName],\n inCache: false,\n url: '',\n };\n options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n let cachedModels = {};\n try {\n cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n } catch {\n options.cacheSupported = false;\n }\n modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;\n const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].url, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n modelStats[shortModelName].loaded = false;\n try {\n // @ts-ignore private function\n model.findIOHandler(); // decide how to actually load a model\n if (options.debug) log('model load handler:', model['handler']);\n } catch (err) {\n log('error finding model i/o handler:', modelUrl, err);\n }\n try {\n // @ts-ignore private property\n const artifacts = await model.handler?.load() || null; // load manifest\n modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n if (artifacts) model.loadSync(artifacts); // load weights\n else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n // @ts-ignore private property\n modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || model.artifacts?.weightData?.[0].byteLength || 0;\n if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n modelStats[shortModelName].loaded = true;\n } catch (err) {\n log('error loading model:', modelUrl, err);\n }\n if (modelStats[shortModelName].loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n try {\n const saveResult = await model.save(cachedModelName);\n if (options.debug) log('model saved:', cachedModelName, saveResult);\n } catch (err) {\n log('error saving model:', modelUrl, err);\n }\n }\n return model;\n}\n", "{\n \"name\": \"@vladmandic/human\",\n \"version\": \"3.3.5\",\n \"description\": \"Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition\",\n \"sideEffects\": false,\n \"main\": \"dist/human.node.js\",\n \"module\": \"dist/human.esm.js\",\n \"browser\": \"dist/human.esm.js\",\n \"types\": \"types/human.d.ts\",\n \"exports\": {\n \"node\": \"./dist/human.node.js\",\n \"script\": \"./dist/human.js\",\n \"module\": \"./dist/human.esm.js\",\n \"types\": \"./types/human.d.ts\",\n \"dist/human\": \"./dist/human.js\",\n \"dist/human.js\": \"./dist/human.js\",\n \"dist/human.esm\": \"./dist/human.esm.js\",\n \"dist/human.esm.js\": \"./dist/human.esm.js\",\n \"dist/human.esm-nobundle\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.esm-nobundle.js\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.node\": \"./dist/human.node.js\",\n \"dist/human.node.js\": \"./dist/human.node.js\",\n \"dist/human.node-wasm\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-wasm.js\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-gpu\": \"./dist/human.node-gpu.js\",\n \"dist/human.node-gpu.js\": \"./dist/human.node-gpu.js\",\n \"require\": \"./dist/human.node.js\",\n \"import\": \"./dist/human.esm.js\"\n },\n \"author\": \"Vladimir Mandic \",\n \"bugs\": {\n \"url\": \"https://github.com/vladmandic/human/issues\"\n },\n \"homepage\": \"https://vladmandic.github.io/human/demo/index.html\",\n \"license\": \"MIT\",\n \"engines\": {\n \"node\": \">=14.0.0\"\n },\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/vladmandic/human.git\"\n },\n \"scripts\": {\n \"start\": \"node --no-warnings demo/nodejs/node.js\",\n \"dev\": \"build --profile development\",\n \"clean\": \"build --profile clean\",\n \"build\": \"rimraf test/build.log && node build.js\",\n \"test\": \"node --no-warnings --unhandled-rejections=strict --trace-uncaught test/node.js\",\n \"lint\": \"eslint *.json *.js src demo test models wiki\",\n \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n },\n \"keywords\": [\n \"human\",\n \"human-library\",\n \"face-detection\",\n \"faceid\",\n \"face-geometry\",\n \"face-embedding\",\n \"face-recognition\",\n \"face-description\",\n \"face-matching\",\n \"body-tracking\",\n \"body-segmentation\",\n \"hand-tracking\",\n \"iris-tracking\",\n \"age-estimation\",\n \"emotion-detection\",\n \"gender-prediction\",\n \"gesture-recognition\",\n \"gaze-tracking\",\n \"age-gender\",\n \"tensorflowjs\",\n \"tfjs\",\n \"tensorflow\"\n ],\n \"devDependencies\": {\n \"@html-eslint/eslint-plugin\": \"^0.26.0\",\n \"@html-eslint/parser\": \"^0.26.0\",\n \"@microsoft/api-extractor\": \"^7.49.2\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"4.22.0\",\n \"@tensorflow/tfjs-converter\": \"^4.22.0\",\n \"@tensorflow/tfjs-core\": \"^4.22.0\",\n \"@tensorflow/tfjs-data\": \"^4.22.0\",\n \"@tensorflow/tfjs-layers\": \"^4.22.0\",\n \"@tensorflow/tfjs-node\": \"^4.22.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.22.0\",\n \"@types/emscripten\": \"^1.40.0\",\n \"@types/node\": \"^22.13.1\",\n \"@types/offscreencanvas\": \"^2019.7.3\",\n \"@typescript-eslint/eslint-plugin\": \"^8.8.1\",\n \"@typescript-eslint/parser\": \"^8.8.1\",\n \"@vladmandic/build\": \"^0.10.2\",\n \"@vladmandic/pilogger\": \"^0.5.1\",\n \"@vladmandic/tfjs\": \"github:vladmandic/tfjs\",\n \"canvas\": \"^3.1.0\",\n \"esbuild\": \"^0.24.2\",\n \"eslint\": \"8.57.0\",\n \"eslint-config-airbnb-base\": \"^15.0.0\",\n \"eslint-plugin-html\": \"^8.1.2\",\n \"eslint-plugin-import\": \"^2.31.0\",\n \"eslint-plugin-json\": \"^4.0.1\",\n \"eslint-plugin-markdown\": \"^5.1.0\",\n \"eslint-plugin-node\": \"^11.1.0\",\n \"eslint-plugin-promise\": \"^7.1.0\",\n \"rimraf\": \"^6.0.1\",\n \"tslib\": \"^2.8.1\",\n \"typedoc\": \"0.27.6\",\n \"typescript\": \"5.7.3\"\n }\n}\n", "/** TFJS custom backend registration */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as image from '../image/image';\nimport type { AnyCanvas } from '../exports';\n\nexport const config = {\n name: 'humangl',\n priority: 999,\n canvas: null as null | AnyCanvas,\n gl: null as null | WebGL2RenderingContext,\n extensions: [] as string[] | null,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false, // default=true\n desynchronized: true, // default=undefined\n },\n};\n\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions();\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(instance: Human): void {\n // force backend reload if gl context is not valid\n if (instance.config.backend !== 'humangl') return;\n if ((config.name in tf.engine().registry) && !config?.gl?.getParameter(config.gl.VERSION)) {\n log('humangl error: backend invalid context');\n instance.models.reset();\n /*\n log('resetting humangl backend');\n await tf.removeBackend(config.name);\n await register(instance); // re-register\n */\n }\n if (!tf.findBackend(config.name)) {\n try {\n config.canvas = image.canvas(100, 100);\n } catch (err) {\n log('humangl error: cannot create canvas:', err);\n return;\n }\n try {\n config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n if (!config.gl) {\n log('humangl error: cannot get webgl context');\n return;\n }\n const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n if (!glv2) {\n log('backend override: using fallback webgl backend as webgl 2.0 is not detected');\n instance.config.backend = 'webgl';\n return;\n }\n if (config.canvas) {\n config.canvas.addEventListener('webglcontextlost', (e) => {\n log('humangl error:', e.type);\n log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n instance.emit('error');\n throw new Error('backend error: webgl context lost');\n });\n config.canvas.addEventListener('webglcontextrestored', (e) => {\n log('humangl error: context restored:', e);\n });\n config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n log('humangl error: context create:', e);\n });\n }\n } catch (err) {\n log('humangl error: cannot get webgl context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('humangl error: cannot set webgl context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n // @ts-ignore uncompatible kernelMs timing info\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('humangl error: cannot register webgl backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('humangl error: cannot update webgl backend registration:', err);\n return;\n }\n try {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n } catch (err) {\n log('humangl error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n const backend = tf.backend();\n const current = typeof backend['gpgpu'] !== 'undefined' ? backend['getGPGPUContext']().gl : null;\n if (current) {\n if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n } else {\n log('humangl error: no current gl context:', current, config.gl);\n }\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record = {\n tf255: 255.0,\n tf1: 1.0,\n tf2: 2.0,\n tf05: 0.5,\n tf127: 127.5,\n rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n constants.tf255 = tf.scalar(255.0, 'float32');\n constants.tf1 = tf.scalar(1.0, 'float32');\n constants.tf2 = tf.scalar(2.0, 'float32');\n constants.tf05 = tf.scalar(0.5, 'float32');\n constants.tf127 = tf.scalar(127.5, 'float32');\n constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/** TFJS backend initialization and customization */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human, Config, BackendEnum } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as constants from './constants';\nimport type { TensorInfo } from './types';\n\nexport async function getBestBackend(): Promise {\n await env.updateBackend(); // update env on backend init\n if (env.tensorflow?.version) return 'tensorflow';\n if (env.webgpu.supported && env.webgpu.backend) return 'webgpu';\n if (env.webgl.supported && env.webgl.backend) return 'webgl';\n if (env.wasm.supported && env.wasm.backend) return 'wasm';\n return 'cpu';\n}\n\nfunction registerCustomOps(config: Config) {\n const newKernels: string[] = [];\n if (!env.kernels.includes('mod')) {\n const kernelMod = {\n kernelName: 'Mod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('mod');\n newKernels.push('mod');\n }\n if (!env.kernels.includes('floormod')) {\n const kernelFloorMod = {\n kernelName: 'FloorMod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n };\n tf.registerKernel(kernelFloorMod);\n env.kernels.push('floormod');\n newKernels.push('floormod');\n }\n /*\n if (!env.kernels.includes('atan2') && config.softwareKernels) {\n const kernelAtan2 = {\n kernelName: 'Atan2',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu');\n const t = tf.atan2(op.inputs.a, op.inputs.b);\n tf.setBackend(backend);\n return t;\n }),\n };\n if (config.debug) log('registered kernel:', 'atan2');\n log('registered kernel:', 'atan2');\n tf.registerKernel(kernelAtan2);\n env.kernels.push('atan2');\n newKernels.push('atan2');\n }\n */\n if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n const kernelRotateWithOffset = {\n kernelName: 'RotateWithOffset',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu'); // eslint-disable-line @typescript-eslint/no-floating-promises\n const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n tf.setBackend(backend); // eslint-disable-line @typescript-eslint/no-floating-promises\n return t;\n }),\n };\n tf.registerKernel(kernelRotateWithOffset);\n env.kernels.push('rotatewithoffset');\n newKernels.push('rotatewithoffset');\n }\n if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record = {};\n\nexport async function check(instance: Human, force = false) {\n instance.state = 'backend';\n if (instance.config.backend?.length === 0) instance.config.backend = await getBestBackend();\n if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n const timeStamp = now();\n\n if (instance.config.backend && instance.config.backend.length > 0) {\n // detect web worker\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n if (instance.config.debug) log('running inside web worker');\n }\n\n if (typeof navigator !== 'undefined' && navigator?.userAgent?.toLowerCase().includes('electron')) {\n if (instance.config.debug) log('running inside electron');\n }\n\n // check available backends\n let available = Object.keys(tf.engine().registryFactory as Record);\n if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n humangl.register(instance);\n available = Object.keys(tf.engine().registryFactory as Record);\n }\n if (instance.config.debug) log('available backends:', available);\n\n // force browser vs node backend\n if (env.browser && !env.node && (instance.config.backend === 'tensorflow') && available.includes('webgl')) {\n if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n instance.config.backend = 'webgl';\n }\n if (env.node && !env.browser && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl') && available.includes('tensorflow')) {\n if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n instance.config.backend = 'tensorflow';\n }\n\n // handle webgpu\n if (env.browser && instance.config.backend === 'webgpu') {\n if (typeof navigator === 'undefined' || typeof navigator.gpu === 'undefined') {\n log('override: backend set to webgpu but browser does not support webgpu');\n instance.config.backend = 'webgl';\n } else {\n const adapter: GPUAdapter = await navigator.gpu.requestAdapter() as GPUAdapter;\n if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n if (!adapter) {\n log('override: backend set to webgpu but browser reports no available gpu');\n instance.config.backend = 'webgl';\n } else {\n let adapterInfo;\n // @ts-ignore gpu adapter info\n if ('requestAdapterInfo' in adapter) adapterInfo = await adapter?.requestAdapterInfo();\n // @ts-ignore gpu adapter info\n else adapterInfo = adapter.info;\n // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n log('webgpu adapter info:', adapterInfo);\n }\n }\n }\n\n if (!available.includes(instance.config.backend)) {\n log(`error: backend ${instance.config.backend} not found in registry`);\n instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n }\n\n if (instance.config.debug) log('setting backend:', [instance.config.backend]);\n\n // customize wasm\n if (instance.config.backend === 'wasm') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n if (typeof tf.setWasmPaths !== 'undefined') tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n let mt = false;\n let simd = false;\n try {\n mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n if (instance.config.debug) log(`wasm execution: ${simd ? 'simd' : 'no simd'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n } catch {\n log('wasm detection failed');\n }\n }\n\n try {\n await tf.setBackend(instance.config.backend);\n await tf.ready();\n } catch (err) {\n log('error: cannot set backend:', instance.config.backend, err);\n return false;\n }\n // @ts-ignore private property\n if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n }\n\n // customize humangl\n if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false \n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false \n // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV']) tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true \n // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false \n // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (instance.config.debug && typeof instance.config.deallocate !== 'undefined' && instance.config.deallocate) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n }\n\n // customize webgpu\n if (tf.getBackend() === 'webgpu') {\n // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n }\n\n if (instance.config.debug) {\n // @ts-ignore private property\n const newFlags = tf.env().flags;\n const updatedFlags = {};\n for (const key of Object.keys(newFlags)) {\n if (defaultFlags[key] === newFlags[key]) continue;\n updatedFlags[key] = newFlags[key];\n }\n if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n }\n\n if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n if (instance.config.debug) log('flags:', instance.config['flags']);\n for (const [key, val] of Object.entries(instance.config.flags)) {\n tf.env().set(key, val as number | boolean);\n }\n }\n\n tf.enableProdMode();\n constants.init();\n instance.performance.initBackend = Math.trunc(now() - timeStamp);\n instance.config.backend = tf.getBackend() as BackendEnum;\n await env.updateBackend(); // update env on backend init\n registerCustomOps(instance.config);\n // await env.updateBackend(); // update env on backend init\n // env.initial = false;\n }\n return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: string[], config) {\n // if (config.debug) log('registerKernel:', kernelNames);\n for (const kernelName of kernelNames) {\n const kernelConfig = {\n kernelName,\n backendName: config.backend,\n kernelFunc: (param): TensorInfo => {\n if (config.debug) log('kernelFunc', kernelName, config.backend, param);\n return param?.inputs?.info as TensorInfo;\n },\n // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n };\n tf.registerKernel(kernelConfig);\n }\n env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport { defaultLabels } from './labels';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\nimport type { Tensor2D } from '../tfjs/types';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\n/** draw processed canvas */\nexport function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n if (!input || !output) return;\n const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.drawImage(input, 0, 0);\n}\n\n/** draw processed canvas */\nexport async function tensor(input: Tensor2D, output: HTMLCanvasElement) {\n if (!input || !output) return;\n if (!env.browser) return;\n // const backend = tf.getBackend();\n // if (backend === 'webgpu') tf.browser.draw(input, output);\n // else await tf.browser.toPixels(input, output);\n await tf.browser.toPixels(input, output);\n // const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\n // if (!ctx) return;\n // const image = await process(input);\n // result.canvas = image.canvas;\n // human.tf.dispose(image.tensor);\n // ctx.drawImage(image.canvas, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial) {\n if (!result?.performance || !inCanvas) return null;\n const timeStamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n const promise = Promise.all([\n face(inCanvas, result.face, localOptions),\n body(inCanvas, result.body, localOptions),\n hand(inCanvas, result.hand, localOptions),\n object(inCanvas, result.object, localOptions),\n gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n // person(inCanvas, result.persons, localOptions); // already included above\n ]);\n drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n result.performance.draw = drawTime;\n return promise;\n}\n\n/** sets default label templates for face/body/hand/object/gestures */\nexport function init() {\n options.faceLabels = defaultLabels.face;\n options.bodyLabels = defaultLabels.body;\n options.bodyPartLabels = defaultLabels.bodyPart;\n options.handLabels = defaultLabels.hand;\n options.fingerLabels = defaultLabels.finger;\n options.objectLabels = defaultLabels.object;\n options.gestureLabels = defaultLabels.gesture;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n if (!input) log('draw error: invalid canvas');\n else if (!input.getContext) log('draw error: canvas context not defined');\n else {\n const ctx = input.getContext('2d', { willReadFrequently: true });\n if (!ctx) log('draw error: cannot get canvas context');\n else return ctx;\n }\n return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const replace = (str: string, source: string, target: string | number) => str.replace(source, typeof target === 'number' ? target.toFixed(1) : target);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function labels(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, str: string, startX: number, startY: number, localOptions: DrawOptions) {\n const line: string[] = str.replace(/\\[.*\\]/g, '').split('\\n').map((l) => l.trim()); // remove unmatched templates and split into array\n const x = Math.max(0, startX);\n for (let i = line.length - 1; i >= 0; i--) {\n const y = i * localOptions.lineHeight + startY;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(line[i], x + 5, y + 16);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(line[i], x + 4, y + 15);\n }\n}\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n ctx.beginPath();\n ctx.lineWidth = localOptions.lineWidth;\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.lineWidth = localOptions.lineWidth;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n let angle;\n let x;\n let y;\n ctx.beginPath();\n ctx.moveTo(from[0], from[1]);\n ctx.lineTo(to[0], to[1]);\n angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.moveTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n ctx.closePath();\n ctx.stroke();\n ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\n\nexport interface DrawOptions {\n /** draw line color */\n color: string,\n /** alpha value used for lines */\n alpha: number,\n /** label color */\n labelColor: string,\n /** label shadow color */\n shadowColor: string,\n /** label font */\n font: string,\n /** line spacing between labels */\n lineHeight: number,\n /** line width for drawn lines */\n lineWidth: number,\n /** size of drawn points */\n pointSize: number,\n /** draw rounded boxes by n pixels */\n roundRect: number,\n /** should points be drawn? */\n drawPoints: boolean,\n /** should labels be drawn? */\n drawLabels: boolean,\n /** should face attention keypoints be highlighted */\n drawAttention: boolean;\n /** should detected gestures be drawn? */\n drawGestures: boolean,\n /** should draw boxes around detection results? */\n drawBoxes: boolean,\n /** should draw polygons from detection points? */\n drawPolygons: boolean,\n /** should draw gaze arrows? */\n drawGaze: boolean,\n /** should fill polygons? */\n fillPolygons: boolean,\n /** use z-coordinate when available */\n useDepth: boolean,\n /** should lines be curved? */\n useCurves: boolean,\n /** string template for face labels */\n faceLabels: string,\n /** string template for body labels */\n bodyLabels: string,\n /** string template for body part labels */\n bodyPartLabels: string,\n /** string template for hand labels */\n handLabels: string,\n /** string template for hand labels */\n fingerLabels: string,\n /** string template for object labels */\n objectLabels: string,\n /** string template for gesture labels */\n gestureLabels: string,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n shadowColor: 'black' as string,\n alpha: 0.5 as number,\n font: 'small-caps 16px \"Segoe UI\"' as string,\n lineHeight: 18 as number,\n lineWidth: 4 as number,\n pointSize: 2 as number,\n roundRect: 8 as number,\n drawPoints: false as boolean,\n drawLabels: true as boolean,\n drawBoxes: true as boolean,\n drawAttention: true as boolean,\n drawGestures: true as boolean,\n drawPolygons: true as boolean,\n drawGaze: true as boolean,\n fillPolygons: false as boolean,\n useDepth: true as boolean,\n useCurves: false as boolean,\n faceLabels: '' as string,\n bodyLabels: '' as string,\n bodyPartLabels: '' as string,\n objectLabels: '' as string,\n handLabels: '' as string,\n fingerLabels: '' as string,\n gestureLabels: '' as string,\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n rightEyeIris: [473, 474, 475, 476, 477], // 5\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const meshLandmarks: Record = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]],\n};\n\nexport const blazeFaceLandmarks: Record = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nexport const irisIndices: { key: string, indices: number[] }[] = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: number[] = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: number[] = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: number[] = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: number[] = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: number[] = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: number[] = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const pairsLips: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n lips: connectionsToIndices(pairsLips),\n leftEye: connectionsToIndices(pairsLeftEye),\n leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n leftIris: connectionsToIndices(pairsLeftIris),\n rightEye: connectionsToIndices(pairsRightEye),\n rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n rightIris: connectionsToIndices(pairsRightIris),\n faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111],\n [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198],\n [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109],\n [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124],\n [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3],\n [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n lips: connectionsToIndices(LIPS_CONNECTIONS),\n leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n 61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n 185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n 78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n 191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n 76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n 184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n 62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n 183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n 130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n 247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n 226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n 113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n 35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n 143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n 156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n 359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n 467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n 446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n 342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n 265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n 372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n 383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n 468, // Center.\n 469, // Iris right edge.\n 470, // Iris top edge.\n 471, // Iris left edge.\n 472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n 473, // Center.\n 474, // Iris right edge.\n 475, // Iris top edge.\n 476, // Iris left edge.\n 477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow, labels, replace } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet localOptions: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (!localOptions.drawLabels || (localOptions.faceLabels?.length === 0)) return;\n let l = localOptions.faceLabels.slice();\n l = replace(l, '[id]', f.id.toFixed(0));\n if (f.score) l = replace(l, '[score]', 100 * f.score);\n if (f.gender) l = replace(l, '[gender]', f.gender);\n if (f.genderScore) l = replace(l, '[genderScore]', 100 * f.genderScore);\n if (f.age) l = replace(l, '[age]', f.age);\n if (f.distance) l = replace(l, '[distance]', 100 * f.distance);\n if (f.real) l = replace(l, '[real]', 100 * f.real);\n if (f.live) l = replace(l, '[live]', 100 * f.live);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n l = replace(l, '[emotions]', emotion.join(' '));\n }\n if (f.rotation?.angle?.roll) l = replace(l, '[roll]', rad2deg(f.rotation.angle.roll));\n if (f.rotation?.angle?.yaw) l = replace(l, '[yaw]', rad2deg(f.rotation.angle.yaw));\n if (f.rotation?.angle?.pitch) l = replace(l, '[pitch]', rad2deg(f.rotation.angle.pitch));\n if (f.rotation?.gaze?.bearing) l = replace(l, '[gaze]', rad2deg(f.rotation.gaze.bearing));\n labels(ctx, l, f.box[0], f.box[1], localOptions);\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n // iris: array[center, left, top, right, bottom]\n if (f.annotations?.leftEyeIris && f.annotations?.leftEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (f.annotations?.rightEyeIris && f.annotations?.rightEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n ctx.strokeStyle = 'pink';\n const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n const pathV = new Path2D(`\n M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n C\n ${valX} ${f.box[1]},\n ${valX} ${f.box[1] + f.box[3]},\n ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n `);\n const pathH = new Path2D(`\n M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n C \n ${f.box[0]} ${valY},\n ${f.box[0] + f.box[2]} ${valY},\n ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n `);\n ctx.stroke(pathH);\n ctx.stroke(pathV);\n }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.gaze.strength && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {\n ctx.strokeStyle = 'pink';\n ctx.fillStyle = 'pink';\n const leftGaze = [\n f.annotations.leftEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.leftEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);\n const rightGaze = [\n f.annotations.rightEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.rightEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);\n }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPolygons && f.mesh.length >= 468) {\n ctx.lineWidth = 1;\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n lines(ctx, points, localOptions);\n }\n drawIrisElipse(f, ctx);\n }\n /*\n if (localOptions.drawPolygons && f.contours.length > 1) {\n ctx.lineWidth = 5;\n lines(ctx, f.contours, opt);\n }\n ctx.lineWidth = 1;\n */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPoints) {\n if (f?.mesh.length >= 468) {\n for (let i = 0; i < f.mesh.length; i++) {\n point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n if (localOptions.drawAttention) {\n if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n }\n }\n } else {\n for (const [k, v] of Object.entries(f?.annotations || {})) {\n if (!v?.[0]) continue;\n const pt = v[0];\n point(ctx, pt[0], pt[1], 0, localOptions);\n if (localOptions.drawLabels) labels(ctx, k, pt[0], pt[1], localOptions);\n }\n }\n }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n if (localOptions.drawBoxes) {\n rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial) {\n localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n for (const f of result) {\n drawFaceBoxes(f, ctx);\n drawLabels(f, ctx);\n if (f.mesh && f.mesh.length > 0) {\n drawFacePoints(f, ctx);\n drawFacePolygons(f, ctx);\n drawGazeSpheres(f, ctx);\n drawGazeArrows(f, ctx);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.bodyLabels?.length > 0)) {\n let l = localOptions.bodyLabels.slice();\n l = replace(l, '[id]', result[i].id.toFixed(0));\n l = replace(l, '[score]', 100 * result[i].score);\n labels(ctx, l, result[i].box[0], result[i].box[1], localOptions);\n }\n }\n if (localOptions.drawPoints && result[i].keypoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels && (localOptions.bodyPartLabels?.length > 0) && result[i].keypoints) {\n ctx.font = localOptions.font;\n for (const pt of result[i].keypoints) {\n if (!pt.score || (pt.score === 0)) continue;\n let l = localOptions.bodyPartLabels.slice();\n l = replace(l, '[label]', pt.part);\n l = replace(l, '[score]', 100 * pt.score);\n labels(ctx, l, pt.position[0], pt.position[1], localOptions);\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n for (const part of Object.values(result[i].annotations)) {\n for (const connected of part) curves(ctx, connected, localOptions);\n }\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.handLabels?.length > 0)) {\n let l = localOptions.handLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = colorDepth(pt[2], localOptions);\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels && h.annotations && (localOptions.fingerLabels?.length > 0)) {\n for (const [part, pt] of Object.entries(h.annotations)) {\n let l = localOptions.fingerLabels.slice();\n l = replace(l, '[label]', part);\n labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions);\n }\n }\n if (localOptions.drawPolygons && h.annotations) {\n const addHandLine = (part: Point[]) => {\n if (!part || part.length === 0 || !part[0]) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n const z = part[i][2] || 0;\n ctx.strokeStyle = colorDepth(i * z, localOptions);\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations.index);\n addHandLine(h.annotations.middle);\n addHandLine(h.annotations.ring);\n addHandLine(h.annotations.pinky);\n addHandLine(h.annotations.thumb);\n // addPart(h.annotations.palm);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.objectLabels?.length > 0)) {\n let l = localOptions.objectLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (localOptions.drawGestures && (localOptions.gestureLabels?.length > 0)) {\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n const [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n let l = localOptions.gestureLabels.slice();\n l = replace(l, '[where]', where[0]);\n l = replace(l, '[who]', who);\n l = replace(l, '[what]', what[1]);\n labels(ctx, l, 8, 2 + (i * localOptions.lineHeight), localOptions);\n i += 1;\n }\n }\n }\n}\n", "export const defaultLabels = {\n face: `face\n confidence: [score]%\n [gender] [genderScore]%\n age: [age] years\n distance: [distance]cm\n real: [real]%\n live: [live]%\n [emotions]\n roll: [roll]\u00B0 yaw:[yaw]\u00B0 pitch:[pitch]\u00B0\n gaze: [gaze]\u00B0`,\n body: 'body [score]%',\n bodyPart: '[label] [score]%',\n object: '[label] [score]%',\n hand: '[label] [score]%',\n finger: '[label]',\n gesture: '[where] [who]: [what]',\n};\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: string[] = [\n 'nose', // 0\n 'leftEyeInside', // 1\n 'leftEye', // 2\n 'leftEyeOutside', // 3\n 'rightEyeInside', // 4\n 'rightEye', // 5\n 'rightEyeOutside', // 6\n 'leftEar', // 7\n 'rightEar', // 8\n 'leftMouth', // 9\n 'rightMouth', // 10\n 'leftShoulder', // 11\n 'rightShoulder', // 12\n 'leftElbow', // 13\n 'rightElbow', // 14\n 'leftWrist', // 15\n 'rightWrist', // 16\n 'leftPinky', // 17\n 'rightPinky', // 18\n 'leftIndex', // 19\n 'rightIndex', // 20\n 'leftThumb', // 21\n 'rightThumb', // 22\n 'leftHip', // 23\n 'rightHip', // 24\n 'leftKnee', // 25\n 'rightKnee', // 26\n 'leftAnkle', // 27\n 'rightAnkle', // 28\n 'leftHeel', // 29\n 'rightHeel', // 30\n 'leftFoot', // 31\n 'rightFoot', // 32\n 'bodyCenter', // 33\n 'bodyTop', // 34\n 'leftPalm', // 35 // z-coord not ok\n 'leftHand', // 36 // similar to wrist but z-coord not ok\n 'rightPalm', // 37 // z-coord not ok\n 'rightHand', // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record = {\n shoulders: ['leftShoulder', 'rightShoulder'],\n hips: ['rightHip', 'leftHip'],\n mouth: ['leftMouth', 'rightMouth'],\n leftLegUpper: ['leftHip', 'leftKnee'],\n leftLegLower: ['leftKnee', 'leftAnkle'],\n leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n leftTorso: ['leftShoulder', 'leftHip'],\n leftArmUpper: ['leftShoulder', 'leftElbow'],\n leftArmLower: ['leftElbow', 'leftWrist'],\n leftHand: ['leftWrist', 'leftPalm'],\n leftHandPinky: ['leftPalm', 'leftPinky'],\n leftHandIndex: ['leftPalm', 'leftIndex'],\n leftHandThumb: ['leftPalm', 'leftThumb'],\n leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n rightLegUpper: ['rightHip', 'rightKnee'],\n rightLegLower: ['rightKnee', 'rightAnkle'],\n rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n rightTorso: ['rightShoulder', 'rightHip'],\n rightArmUpper: ['rightShoulder', 'rightElbow'],\n rightArmLower: ['rightElbow', 'rightWrist'],\n rightHand: ['rightWrist', 'rightPalm'],\n rightHandPinky: ['rightPalm', 'rightPinky'],\n rightHandIndex: ['rightPalm', 'rightIndex'],\n rightHandThumb: ['rightPalm', 'rightThumb'],\n rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D } from '../tfjs/types';\n\nexport interface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nlet model: GraphModel | null;\nlet inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n const anchors: { x: number, y: number }[] = [];\n let layerId = 0;\n while (layerId < numLayers) {\n let anchorCount = 0;\n let lastSameStrideLayer = layerId;\n while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n anchorCount += 2;\n lastSameStrideLayer++;\n }\n const stride = strides[layerId];\n const featureMapHeight = Math.ceil(inputSize / stride);\n const featureMapWidth = Math.ceil(inputSize / stride);\n for (let y = 0; y < featureMapHeight; ++y) {\n for (let x = 0; x < featureMapWidth; ++x) {\n for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n }\n }\n }\n layerId = lastSameStrideLayer;\n }\n anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nexport async function loadDetector(config: Config): Promise {\n if (env.initial) model = null;\n if (!model && config.body['detector'] && config.body['detector'].modelPath || '') {\n model = await loadModel(config.body['detector'].modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug && model) log('cached model:', model['modelUrl']);\n createAnchors();\n return model as GraphModel;\n}\n\nconst cropFactor = [5.0, 5.0];\nexport function decodeBoxes(boxesTensor, anchor) {\n return tf.tidy(() => {\n const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n let xCenter = tf.squeeze(split[0]);\n let yCenter = tf.squeeze(split[1]);\n let width = tf.squeeze(split[2]);\n let height = tf.squeeze(split[3]);\n xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n const xMin = tf.sub(xCenter, tf.div(width, 2));\n const yMin = tf.sub(yCenter, tf.div(height, 2));\n const xMax = tf.add(xMin, width);\n const yMax = tf.add(yMin, height);\n const boxes = tf.stack([xMin, yMin, xMax, yMax], 1);\n return boxes;\n });\n}\n\nasync function decodeResults(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise {\n const detectedBoxes: DetectedBox[] = [];\n const t: Record = {};\n t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n t.scores = tf.sigmoid(logitsTensor);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, 1, config.body['detector']?.minConfidence || 0.1, config.body['detector']?.iouThreshold || 0.1);\n const nms = await t.nms.data();\n const scores = await t.scores.data();\n const boxes = await t.boxes.array();\n for (const i of Array.from(nms)) {\n const score = scores[i];\n const boxRaw: Box = boxes[i];\n const box: Box = [Math.round(boxRaw[0] * outputSize[0]), Math.round(boxRaw[1] * outputSize[1]), Math.round(boxRaw[2] * outputSize[0]), Math.round(boxRaw[3] * outputSize[1])];\n const detectedBox: DetectedBox = { score, boxRaw, box };\n detectedBoxes.push(detectedBox);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return detectedBoxes;\n}\n\nexport async function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n const t: Record = {};\n t.res = model?.execute(input, ['Identity']) as Tensor; //\n t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n t.logits = tf.squeeze(t.logitsRaw);\n t.boxes = tf.squeeze(t.boxesRaw);\n const boxes = await decodeResults(t.boxes, t.logits, config, outputSize);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function square(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n const dist = [box[2] * scaleFact, box[3] * scaleFact];\n const newBox: Box = [\n box[0] - (dist[0] - box[2]) / 2,\n box[1] - (dist[1] - box[3]) / 2,\n dist[0],\n dist[1],\n ];\n return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport { loadDetector, detectBoxes, DetectedBox } from './blazeposedetector';\nimport * as box from '../util/box';\nimport { env } from '../util/env';\n\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nlet model: GraphModel | null;\nlet inputSize = 256;\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n detector: [],\n};\n\nconst cache: BodyResult[] = [];\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport const loadDetect = (config: Config): Promise => loadDetector(config);\n\nexport async function loadPose(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.body.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nfunction prepareImage(input: Tensor4D, size: number, cropBox?: Box): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n let final: Tensor;\n if (cropBox) {\n t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n }\n if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n const height: [number, number] = [\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n ];\n const width: [number, number] = [\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n ];\n padding = [\n [0, 0], // dont touch batch\n height, // height before&after\n width, // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n t.resize = tf.image.resizeBilinear(t.pad as Tensor4D, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else if (input.shape[1] !== size) { // if input needs resizing\n t.resize = tf.image.resizeBilinear(t.cropped as Tensor4D || input, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else { // if input is already in a correct resolution just normalize it\n final = tf.div(t.cropped || input, constants.tf255);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nfunction rescaleKeypoints(keypoints: BodyKeypoint[], outputSize: [number, number], cropBox?: Box): BodyKeypoint[] {\n for (const kpt of keypoints) { // first rescale due to padding\n kpt.position = [\n Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n kpt.position[2] as number,\n ];\n kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n }\n if (cropBox) { // second rescale due to cropping\n const width = cropBox[2] - cropBox[0];\n const height = cropBox[3] - cropBox[1];\n for (const kpt of keypoints) {\n kpt.positionRaw = [\n kpt.positionRaw[0] / height + cropBox[1], // correct offset due to crop\n kpt.positionRaw[1] / width + cropBox[0], // correct offset due to crop\n kpt.positionRaw[2] as number,\n ];\n kpt.position = [\n Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n kpt.positionRaw[2] as number,\n ];\n }\n }\n return keypoints;\n}\n\nfunction fixKeypoints(keypoints: BodyKeypoint[]) {\n // palm z-coord is incorrect around near-zero so we approximate it\n const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise {\n /**\n * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n * t.segmentation:\n * t.heatmap:\n * t.world: 39 keypoints [x,y,z] normalized to -1..1\n * t.poseflag: body score\n */\n if (!model?.['executor']) return null;\n const t: Record = {};\n [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = model?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n const poseScore = (await t.poseflag.data())[0];\n const points = await t.ld.data();\n const distances = await t.world.data();\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n const keypointsRelative: BodyKeypoint[] = [];\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n const score = sigmoid(points[depth * i + 3]);\n const presence = sigmoid(points[depth * i + 4]);\n const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n const positionRaw: Point = [points[depth * i + 0] / inputSize, points[depth * i + 1] / inputSize, points[depth * i + 2] + 0];\n const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n }\n if (poseScore < (config.body.minConfidence || 0)) return null;\n fixKeypoints(keypointsRelative);\n const keypoints: BodyKeypoint[] = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n const kpts = keypoints.map((k) => k.position);\n const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n return body;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n skipped++;\n } else {\n let boxes: DetectedBox[] = [];\n if (config.body?.['detector']?.['enabled']) {\n const preparedImage = prepareImage(input, 224);\n boxes = await detectBoxes(preparedImage, config, outputSize);\n tf.dispose(preparedImage);\n } else {\n boxes = [{ box: [0, 0, 0, 0] as Box, boxRaw: [0, 0, 1, 1], score: 0 }]; // running without detector\n }\n for (let i = 0; i < boxes.length; i++) {\n const preparedBox = prepareImage(input, 256, boxes[i]?.boxRaw); // padded and resized\n cache.length = 0;\n const bodyResult = await detectLandmarks(preparedBox, config, outputSize);\n tf.dispose(preparedBox);\n if (!bodyResult) continue;\n bodyResult.id = i;\n // bodyResult.score = 0; // TBD\n cache.push(bodyResult);\n }\n /*\n cropBox = [0, 0, 1, 1]; // reset crop coordinates\n if (cache?.boxRaw && config.skipAllowed) {\n const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n size = (size * 1.0) / 2; // enlarge and half it\n if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n const y = 0; // cy - size;\n const x = cx - size;\n cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n }\n }\n */\n lastTime = now();\n skipped = 0;\n }\n return cache;\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n // fakeOps(['floormod'], config);\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n if (!res) return [];\n const t: Record = {};\n const results: ObjectResult[] = [];\n const detections = await res.array() as number[][][];\n t.squeeze = tf.squeeze(res);\n const arr = tf.split(t.squeeze, 6, 1); // x1, y1, x2, y2, score, class\n t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n t.boxes = tf.squeeze(t.stack);\n t.scores = tf.squeeze(arr[4]);\n t.classes = tf.squeeze(arr[5]);\n tf.dispose([res, ...arr]);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, config.object.maxDetected || 0, config.object.iouThreshold, (config.object.minConfidence || 0));\n const nms = await t.nms.data();\n let i = 0;\n for (const id of Array.from(nms)) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n if (Number.isNaN(classVal)) continue;\n const label = labels[classVal].label as ObjectType;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw: Box = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ];\n const box: Box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return results;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n lastTime = now();\n tf.dispose(resize);\n\n const obj = await process(objectT, outputSize, config);\n last = obj;\n\n resolve(obj);\n });\n}\n", "export const kpt: string[] = [\n 'head',\n 'neck',\n 'rightShoulder',\n 'rightElbow',\n 'rightWrist',\n 'chest',\n 'leftShoulder',\n 'leftElbow',\n 'leftWrist',\n 'bodyCenter',\n 'rightHip',\n 'rightKnee',\n 'rightAnkle',\n 'leftHip',\n 'leftKnee',\n 'leftAnkle',\n];\n\nexport const connected: Record = {\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record };\n\n// const keypoints: Array = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore): Promise<[number, number, number]> {\n const [width, height] = inputs.shape;\n const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n const max = tf.max(reshaped, 0);\n const newScore: number = (await max.data())[0]; // get highest score\n if (newScore > minScore) { // skip coordinate calculation is score is too low\n const coordinates = tf.argMax(reshaped, 0);\n const mod = tf.mod(coordinates, width);\n const x = (await mod.data())[0];\n const div = tf.div(coordinates, width);\n const y: number = (await div.data())[0];\n tf.dispose([reshaped, max, coordinates, mod, div]);\n return [x, y, newScore];\n }\n tf.dispose([reshaped, max]);\n return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs[0].shape) return [];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n skipped++;\n return [cache];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape?.[2] || 0, model?.inputs[0].shape?.[1] || 0], false);\n const enhance = tf.mul(resize, constants.tf2);\n const norm = tf.sub(enhance, constants.tf1);\n return norm;\n });\n let resT;\n if (config.body.enabled) resT = model?.execute(tensor);\n lastTime = now();\n tf.dispose(tensor);\n\n if (resT) {\n cache.keypoints.length = 0;\n const squeeze = tf.squeeze(resT);\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = tf.unstack(squeeze, 2);\n tf.dispose(squeeze);\n\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n if (partScore > (config.body.minConfidence || 0)) {\n cache.keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = cache.keypoints.map((a) => a.position[0]);\n const y = cache.keypoints.map((a) => a.position[1]);\n cache.box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n cache.boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n cache.annotations[name] = pt;\n }\n resolve([cache]);\n });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n Math.trunc(Math.max(0, box.startPoint[0])),\n Math.trunc(Math.max(0, box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n box.startPoint[0] / (input.shape[2] || 0),\n box.startPoint[1] / (input.shape[1] || 0),\n (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor, anchor) => {\n const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n // const centerPoint = [(startPoint[0] + endPoint[0]) / 2, (startPoint[1] + endPoint[1]) / 2];\n const landmarks = box.landmarks.map((pt) => [(pt[0] + anchor[0]) * factor[0], (pt[1] + anchor[1]) * factor[1]]);\n /**\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0]) / 2) + (pt[0] * input.shape[2] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1]) / 2) + (pt[1] * input.shape[1] / blazeface.size()),\n ]);\n */\n\n return { startPoint, endPoint, landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n const h = image.shape[1];\n const w = image.shape[2];\n const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n const norm = tf.div(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n return {\n startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point,\n endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size,\n };\n};\n\nexport const squarifyBox = (box) => {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize = Math.max(...size) / 2;\n return {\n startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point,\n endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size: [Math.round(size[0]), Math.round(size[1])] as [number, number],\n };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n const x = landmarks.map((d) => d[0]);\n const y = landmarks.map((d) => d[1]);\n return {\n startPoint: [Math.min(...x), Math.min(...y)] as Point,\n endPoint: [Math.max(...x), Math.max(...y)] as Point,\n landmarks,\n };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n let product = 0;\n for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize: number) {\n const spec = inputSize === 192\n ? { strides: [4], anchors: [1] } // facemesh-detector\n : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n const anchors: [number, number][] = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n }\n }\n }\n return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n const boxSize = getBoxSize(box);\n const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n (coord[2] || 0),\n ]));\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n const boxCenter = getBoxCenter(box);\n const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n return coordsRotated.map((coord) => ([\n Math.trunc(coord[0] + offsets[0]),\n Math.trunc(coord[1] + offsets[1]),\n Math.trunc(coord[2] || 0),\n ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n ? coords.meshLandmarks.symmetryLine\n : coords.blazeFaceLandmarks.symmetryLine;\n let angle = 0; // default\n let rotationMatrix = fixedRotationMatrix; // default\n let face; // default\n\n if (rotate && env.kernels.includes('rotatewithoffset')) {\n angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n if (largeAngle) { // perform rotation only if angle is sufficiently high\n const center: Point = getBoxCenter(box);\n const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n const rotated = tf.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);\n rotationMatrix = buildRotationMatrix(-angle, center);\n face = cutAndResize(box, rotated, [inputSize, inputSize]);\n tf.dispose(rotated);\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n const x = mesh.map((m) => m[0]);\n const y = mesh.map((m) => m[1]);\n // weighted center\n /*\n const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n return [sum(x) / mesh.length, sum(y) / mesh.length];\n */\n // absolute center\n return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n const center = findFaceCenter(mesh);\n const boxSize = getBoxSize(previousBox);\n const calculatedBox = {\n startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n };\n return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\nexport interface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number, size: [number, number] }\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.detector?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n if (!anchors || !inputSizeT) return tf.zeros([0, 0]);\n const t: Record = {};\n t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n t.centers = tf.add(t.boxStarts, anchors);\n t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n t.centersNormalized = tf.div(t.centers, inputSizeT);\n t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n t.startNormalized = tf.mul(t.starts, inputSizeT);\n t.endNormalized = tf.mul(t.ends, inputSizeT);\n const boxes = tf.concat2d([t.startNormalized as Tensor2D, t.endNormalized as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor4D, config: Config): Promise {\n // sanity check on input\n if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n const t: Record = {};\n let pad = [0, 0];\n let scale = [1, 1];\n if (config?.face?.detector?.square) {\n const xy = Math.max(inputImage.shape[2], inputImage.shape[1]);\n pad = [Math.floor((xy - inputImage.shape[2]) / 2), Math.floor((xy - inputImage.shape[1]) / 2)];\n t.padded = tf.pad(inputImage, [[0, 0], [pad[1], pad[1]], [pad[0], pad[0]], [0, 0]]);\n scale = [inputImage.shape[2] / xy, inputImage.shape[1] / xy];\n pad = [pad[0] / inputSize, pad[1] / inputSize];\n } else {\n t.padded = inputImage.clone();\n }\n t.resized = tf.image.resizeBilinear(t.padded as Tensor4D, [inputSize, inputSize]);\n t.div = tf.div(t.resized, constants.tf127);\n t.normalized = tf.sub(t.div, constants.tf1);\n const res = model?.execute(t.normalized) as Tensor[];\n if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n const sorted = res.sort((a, b) => a.size - b.size);\n t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n t.concat = tf.concat([t.concat512, t.concat384], 1);\n t.batch = tf.squeeze(t.concat, [0]);\n } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n t.batch = tf.squeeze(res[0]);\n } else { // original blazeface tfhub model\n t.batch = tf.squeeze(res);\n }\n tf.dispose(res);\n t.boxes = decodeBoxes(t.batch);\n t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.logits);\n t.scores = tf.squeeze(t.sigmoid);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n const nms = await t.nms.array() as number[];\n const boxes: DetectBox[] = [];\n const scores = await t.scores.data();\n for (let i = 0; i < nms.length; i++) {\n const confidence = scores[nms[i]];\n if (confidence > (config.face.detector?.minConfidence || 0)) {\n const b: Record = {};\n b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n b.squeeze = tf.squeeze(b.slice);\n b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n const points = await b.bbox.data();\n const unpadded = [ // TODO fix this math\n points[0] * scale[0] - pad[0],\n points[1] * scale[1] - pad[1],\n points[2] * scale[0] - pad[0],\n points[3] * scale[1] - pad[1],\n ];\n const rawBox = {\n startPoint: [unpadded[0], unpadded[1]] as Point,\n endPoint: [unpadded[2], unpadded[3]] as Point,\n landmarks: (await b.landmarks.array()) as Point[],\n confidence,\n };\n b.anchor = tf.slice(anchors as Tensor, [nms[i], 0], [1, 2]);\n const anchor = await b.anchor.data();\n const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize], anchor);\n const enlargedBox = util.enlargeBox(scaledBox, config.face.detector?.scale || 1.4);\n const squaredBox = util.squarifyBox(enlargedBox);\n if (squaredBox.size[0] > (config.face.detector?.['minSize'] || 0) && squaredBox.size[1] > (config.face.detector?.['minSize'] || 0)) boxes.push(squaredBox);\n Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst leftOutline = coords.meshAnnotations.leftEyeLower0;\nconst rightOutline = coords.meshAnnotations.rightEyeLower0;\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.iris?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize === -1) inputSize = 64;\n return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.irisIndices.length; i++) {\n const { key, indices } = coords.irisIndices[i];\n const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0],\n newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false, scale = 2.3) => {\n const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), scale));\n const boxSize = util.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / meshSize,\n box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n box.endPoint[0] / meshSize,\n ]], [0], [inputSize, inputSize]);\n if (flip && env.kernels.includes('flipleftright')) {\n const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n tf.dispose(crop);\n crop = flipped;\n }\n return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n const eyeRawCoords: Point[] = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n};\n\nexport async function augmentIris(rawCoords, face, meshSize, config: Config) {\n if (!model?.['executor']) return rawCoords;\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true, config.face.iris?.scale || 2.3);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true, config.face.iris?.scale || 2.3);\n const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n tf.dispose(leftEyeCrop);\n tf.dispose(rightEyeCrop);\n const eyePredictions = model.execute(combined) as Tensor;\n tf.dispose(combined);\n const eyePredictionsData = await eyePredictions.data();\n tf.dispose(eyePredictions);\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n return newCoords;\n}\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n const t: Record = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n // flag: results[1], // already processed in parent // conv_faceflag\n lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n };\n for (const val of Object.values(t)) {\n if (!val) return rawCoords; // could not find tensor\n }\n\n // augment iris: adds additional 5 keypoints per eye\n const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n // augment eyes: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n // augment lips: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\nimport type { DetectBox } from './blazeface';\n\nconst cache = {\n boxes: [] as DetectBox[],\n skipped: Number.MAX_SAFE_INTEGER,\n timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n // reset cached boxes\n const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n cache.timestamp = now();\n cache.skipped = 0;\n } else {\n cache.skipped++;\n }\n const faces: FaceResult[] = [];\n const newCache: DetectBox[] = [];\n let id = 0;\n const size = inputSize;\n for (let i = 0; i < cache.boxes.length; i++) {\n const box = cache.boxes[i];\n let angle = 0;\n let rotationMatrix;\n const face: FaceResult = { // init face result\n id: id++,\n mesh: [],\n meshRaw: [],\n box: [0, 0, 0, 0],\n boxRaw: [0, 0, 0, 0],\n score: 0,\n boxScore: 0,\n faceScore: 0,\n size: [0, 0],\n // contoursRaw: [],\n // contours: [],\n annotations: {} as Record,\n };\n\n // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n if (config.filter.equalization) {\n const equilized = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n tf.dispose(face.tensor);\n if (equilized) face.tensor = equilized;\n }\n face.boxScore = Math.round(100 * box.confidence) / 100;\n if (!config.face.mesh?.enabled || !model?.['executor']) { // mesh not enabled or not loaded, return resuts from detector only\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.size = box.size;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n } else if (!model) { // mesh enabled, but not loaded\n if (config.debug) log('face mesh detection requested, but model is not loaded');\n } else { // mesh enabled\n if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n config.face.attention.enabled = false;\n tf.dispose(face.tensor);\n return faces;\n }\n const results = model.execute(face.tensor as Tensor) as Tensor[];\n const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n const faceConfidence = await confidenceT.data();\n face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n box.confidence = face.faceScore; // reset confidence of cached box\n if (config.face.mesh['keepInvalid']) {\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.size = box.size;\n face.score = face.boxScore;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n }\n } else {\n const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n let rawCoords = await coordsReshaped.array();\n tf.dispose(coordsReshaped);\n if (config.face.attention?.enabled) {\n rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n } else if (config.face.iris?.enabled) {\n rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize, config); // run iris model and augment results\n }\n face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n face.score = face.faceScore;\n const calculatedBox = {\n ...util.calculateFaceBox(face.mesh, box),\n confidence: box.confidence,\n landmarks: box.landmarks,\n size: box.size,\n };\n face.box = util.clampBox(calculatedBox, input);\n face.boxRaw = util.getRawBox(calculatedBox, input);\n face.size = calculatedBox.size;\n /*\n const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n face.contoursRaw = [];\n for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n */\n newCache.push(calculatedBox);\n }\n tf.dispose(results);\n }\n if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n else tf.dispose(face.tensor);\n }\n cache.boxes = newCache; // reset cache\n return faces;\n}\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (config.face.attention?.enabled && model?.['signature']) {\n if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n }\n if (!model) {\n if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n else model = await loadModel(config.face.mesh?.modelPath);\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nlet annotations: string[] = [];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet rgb = false;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.face.emotion?.modelPath);\n rgb = model?.inputs?.[0].shape?.[3] === 3;\n if (!rgb) annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']; // oarriaga and gear\n else annotations = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']; // affectnet\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ score: number, emotion: Emotion }[]> {\n if (!model) return [];\n const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj: { score: number, emotion: Emotion }[] = [];\n if (config.face.emotion?.enabled) {\n const t: Record = {};\n const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n if (config.face.emotion?.['crop'] > 0) { // optional crop\n const crop = config.face.emotion?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n }\n if (rgb) {\n t.mul = tf.mul(t.resize, 255);\n t.normalize = tf.sub(t.mul, [103.939, 116.779, 123.68]); // affectnet uses specific norm values\n t.emotion = model?.execute(t.normalize) as Tensor; // result is already in range 0..1, no need for additional activation\n } else {\n // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n // t.redNorm = tf.mul(t.red, rgb[0]);\n // t.greenNorm = tf.mul(t.green, rgb[1]);\n // t.blueNorm = tf.mul(t.blue, rgb[2]);\n // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n t.channels = tf.mul(t.resize, constants.rgb);\n t.grayscale = tf.sum(t.channels, 3, true);\n t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n }\n lastTime = now();\n const data = await t.emotion.data();\n for (let i = 0; i < data.length; i++) {\n if (data[i] > (config.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n }\n obj.sort((a, b) => b.score - a.score);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face similarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel, Tensor4D, Tensor1D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.description?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport function enhance(input, config: Config): Tensor {\n const tensor = (input.image || input.tensor || input) as Tensor4D; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n let crop: Tensor;\n if (config.face.description?.['crop'] > 0) { // optional crop\n const cropval = config.face.description?.['crop'];\n const box = [[cropval, cropval, 1 - cropval, 1 - cropval]];\n crop = tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n const norm: Tensor = tf.mul(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n /*\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n */\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n const obj: FaceRes = {\n age: 0 as number,\n gender: 'unknown' as Gender,\n genderScore: 0 as number,\n descriptor: [] as number[],\n };\n if (!model?.['executor']) return obj;\n const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && (last?.[idx]?.age > 0) && (last?.[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (config.face.description?.enabled) {\n const enhanced = enhance(image, config);\n const resT = model?.execute(enhanced) as Tensor[];\n lastTime = now();\n tf.dispose(enhanced);\n const genderT = resT.find((t) => t.shape[1] === 1) as Tensor;\n const gender = await genderT.data();\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > (config.face.description.minConfidence || 0)) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100) as Tensor1D, 1);\n const ageIdx: number = (await argmax.data())[0];\n tf.dispose(argmax);\n const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n const all = await ageT.data();\n obj.age = Math.round(all[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n const descriptor = desc ? await desc.data() : [] as number[];\n obj.descriptor = Array.from(descriptor);\n resT.forEach((t) => tf.dispose(t));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\n// import * as tf from 'dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: { x: number, y: number }[]): boolean {\n let inside = false;\n let j = polygon.length - 1;\n for (let i = 0; i < polygon.length; j = i++) {\n if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n }\n return inside;\n}\n\nexport async function mask(face: FaceResult): Promise {\n if (!face.tensor) return face.tensor;\n if (!face.mesh || face.mesh.length < 100) return face.tensor;\n const width = face.tensor.shape[2] || 0;\n const height = face.tensor.shape[1] || 0;\n const buffer = await face.tensor.buffer();\n let silhouette: { x: number, y: number }[] = [];\n for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n for (let x = 0; x < width; x++) {\n for (let y = 0; y < height; y++) {\n const inside = insidePoly(x / width, y / width, silhouette);\n if (!inside) {\n buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n }\n }\n }\n const output = buffer.toTensor();\n // tf.dispose(buffer);\n return output;\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.liveness?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: GearType[] = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.gear?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n const skipFrame = skipped < (config.face.gear?.skipFrames || 0);\n const skipTime = (config.face.gear?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n let box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n if (config.face.gear?.['crop'] > 0) { // optional crop config value\n const crop = config.face.gear?.['crop'];\n box = [[crop, crop, 1 - crop, 1 - crop]];\n }\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n if (config.face.gear?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n const gender = await t.gender.data();\n obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n const race = await t.race.data();\n for (let i = 0; i < race.length; i++) {\n if (race[i] > (config.face.gear?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n }\n obj.race.sort((a, b) => b.score - a.score);\n // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n const ageDistribution = Array.from(await t.age.data());\n const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n let age = ageSorted[0][0]; // pick best starting point\n for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n obj.age = Math.round(10 * age) / 10;\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ age: number }> {\n if (!model) return { age: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.mul(t.resize, constants.tf255);\n const obj = { age: 0 };\n if (config.face['ssrnet']?.enabled) t.age = model.execute(t.enhance) as Tensor;\n if (t.age) {\n const data = await t.age.data();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { gender: Gender, genderScore: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet']?.modelPathGender);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n if (!model) return { gender: 'unknown', genderScore: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.tidy(() => {\n let normalize: Tensor;\n if (model?.inputs?.[0].shape?.[3] === 1) {\n const [red, green, blue] = tf.split(t.resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n } else {\n normalize = tf.mul(tf.sub(t.resize, constants.tf05), 2); // range rgb:-1..1\n }\n return normalize;\n });\n const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n if (config.face['ssrnet']?.enabled) t.gender = model.execute(t.enhance) as Tensor;\n const data = await t.gender.data();\n obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * MobileFaceNet model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['mobilefacenet']?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n const skipTime = (config.face['mobilefacenet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['mobilefacenet']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n /*\n // optional normalize outputs with l2 normalization\n const scaled = tf.tidy(() => {\n const l2 = res.norm('euclidean');\n const scale = res.div(l2);\n return scale;\n });\n\n // optional reduce feature vector complexity\n const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n */\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['insightface'].modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n const skipTime = (config.face['insightface']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['insightface']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations.rightEyeIris || !face.annotations.leftEyeIris) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v: Vector): Vector => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\n let thetaX: number;\n let thetaY: number;\n let thetaZ: number;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n // thetaZ = Math.asin(r10);\n const cosThetaZ = Math.sqrt(r00 * r00 + r20 * r20); // \n thetaZ = Math.atan2(r10, cosThetaZ);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n if (Number.isNaN(thetaX)) thetaX = 0;\n if (Number.isNaN(thetaY)) thetaY = 0;\n if (Number.isNaN(thetaZ)) thetaZ = 0;\n // return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n return { pitch: -thetaX, yaw: -thetaY, roll: -thetaZ };\n };\n /*\n const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n };\n };\n */\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n const yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n const zAxis = normalize(crossVectors(xAxis, yAxis));\n // adjust xAxis to make sure that all axes are perpendicular to each other\n xAxis = crossVectors(yAxis, zAxis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n xAxis[0], xAxis[1], xAxis[2],\n yAxis[0], yAxis[1], yAxis[2],\n zAxis[0], zAxis[1], zAxis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n", "import type { FaceResult } from '../result';\n\nexport function calculateCameraDistance(face: FaceResult, width: number): number {\n // iris points are [center, left, top, right, bottom]\n // average size of human iris is 11.7mm - fairly constant for all ages/genders/races\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // distance of eye from camera in meters\n const cameraDistance = Math.round(1.17 / irisSize) / 100;\n return cameraDistance;\n}\n\nexport function calculateEyesDistance(face: FaceResult, width: number): number {\n // average distance between eyes is 65mm - fairly constant for typical adult male, but varies otherwise\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // pixel x and y distance of centers of left and right iris, you can use edges instead\n const irisDistanceXY = [f.leftEyeIris[0][0] - f.rightEyeIris[0][0], f.leftEyeIris[0][1] - f.rightEyeIris[0][1]];\n // absolute distance bewtween eyes in 0..1 range to account for head pitch (we can ignore yaw)\n const irisDistance = Math.sqrt((irisDistanceXY[0] * irisDistanceXY[0]) + (irisDistanceXY[1] * irisDistanceXY[1])) / width;\n // distance between eyes in meters\n const eyesDistance = Math.round(1.17 * irisDistance / irisSize) / 100;\n return eyesDistance;\n}\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor4D } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\nimport { calculateCameraDistance } from './anthropometry';\n\ninterface DescRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor4D): Promise => {\n // run facemesh, includes blazeface and iris\n let timeStamp: number = now();\n let ageRes: { age: number } | Promise<{ age: number }> | null;\n let gearRes: gear.GearType | Promise | null;\n let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n let mobilefacenetRes: number[] | Promise | null;\n let insightfaceRes: number[] | Promise | null;\n let antispoofRes: number | Promise | null;\n let livenessRes: number | Promise | null;\n let descRes: DescRes | Promise | null;\n\n const faceRes: FaceResult[] = [];\n instance.state = 'run:face';\n const faces: FaceResult[] = await facemesh.predict(input, instance.config);\n instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n instance.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefied\n if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n // optional face mask\n if (instance.config.face.detector?.mask) {\n const masked = await mask.mask(faces[i]);\n tf.dispose(faces[i].tensor);\n if (masked) faces[i].tensor = masked;\n }\n\n // calculate face angles\n const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Emotion:');\n if (instance.config.async) {\n emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n } else {\n instance.state = 'run:emotion';\n timeStamp = now();\n emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Emotion:');\n\n // run antispoof, inherits face from blazeface\n instance.analyze('Start AntiSpoof:');\n if (instance.config.async) {\n antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:antispoof';\n timeStamp = now();\n antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End AntiSpoof:');\n\n // run liveness, inherits face from blazeface\n instance.analyze('Start Liveness:');\n if (instance.config.async) {\n livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:liveness';\n timeStamp = now();\n livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Liveness:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start GEAR:');\n if (instance.config.async) {\n gearRes = instance.config.face.gear?.enabled ? gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:gear';\n timeStamp = now();\n gearRes = instance.config.face.gear?.enabled ? await gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.gear = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End GEAR:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start SSRNet:');\n if (instance.config.async) {\n ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:ssrnet';\n timeStamp = now();\n ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End SSRNet:');\n\n // run mobilefacenet alternative, inherits face from blazeface\n instance.analyze('Start MobileFaceNet:');\n if (instance.config.async) {\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End MobileFaceNet:');\n\n // run insightface alternative, inherits face from blazeface\n instance.analyze('Start InsightFace:');\n if (instance.config.async) {\n insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End InsightFace:');\n\n // run faceres, inherits face from blazeface\n instance.analyze('Start Description:');\n if (instance.config.async) {\n descRes = faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n } else {\n instance.state = 'run:description';\n timeStamp = now();\n descRes = await faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Description:');\n\n // if async wait for results\n if (instance.config.async) {\n [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);\n }\n instance.analyze('Finish Face:');\n\n if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n descRes = {\n ...(descRes as DescRes),\n age: (ageRes as { age: number}).age,\n gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n };\n }\n if (instance.config.face.gear?.enabled && gearRes) { // override age/gender/race if gear model is used\n descRes = {\n ...(descRes as DescRes),\n age: (gearRes as gear.GearType).age,\n gender: (gearRes as gear.GearType).gender,\n genderScore: (gearRes as gear.GearType).genderScore,\n race: (gearRes as gear.GearType).race,\n };\n }\n if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if mobilefacenet model is used\n (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n }\n\n if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n (descRes as DescRes).descriptor = insightfaceRes as number[];\n }\n\n const irisSize = instance.config.face.iris?.enabled ? calculateCameraDistance(faces[i], input.shape[2]) : 0;\n\n // optionally return tensor\n const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor as Tensor4D) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n const res: FaceResult = {\n ...faces[i],\n id: i,\n };\n if ((descRes as DescRes).age) res.age = (descRes as DescRes).age;\n if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n if (antispoofRes) res.real = antispoofRes as number;\n if (livenessRes) res.live = livenessRes as number;\n if (irisSize > 0) res.distance = irisSize;\n if (rotation) res.rotation = rotation;\n if (tensor) res.tensor = tensor;\n faceRes.push(res);\n instance.analyze('End Face');\n }\n instance.analyze('End FaceMesh:');\n if (instance.config.async) {\n if (instance.performance.face) delete instance.performance.face;\n if (instance.performance.age) delete instance.performance.age;\n if (instance.performance.gender) delete instance.performance.gender;\n if (instance.performance.emotion) delete instance.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n thumb: 0,\n index: 1,\n middle: 2,\n ring: 3,\n pinky: 4,\n all: [0, 1, 2, 3, 4], // just for convenience\n nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n // Describes mapping of joints based on the 21 points returned by handpose.\n // [0] Palm\n // [1-4] Thumb\n // [5-8] Index\n // [9-12] Middle\n // [13-16] Ring\n // [17-20] Pinky\n pointsMapping: {\n 0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n 1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n 2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n 3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n 4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n },\n getName: (value) => Finger.nameMapping[value],\n getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n none: 0,\n half: 1,\n full: 2,\n nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n verticalUp: 0,\n verticalDown: 1,\n horizontalLeft: 2,\n horizontalRight: 3,\n diagonalUpRight: 4,\n diagonalUpLeft: 5,\n diagonalDownRight: 6,\n diagonalDownLeft: 7,\n nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n name;\n curls;\n directions;\n weights;\n weightsRelative;\n\n constructor(name) {\n // name (should be unique)\n this.name = name;\n this.curls = {};\n this.directions = {};\n this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n }\n\n curl(finger, curl, confidence) {\n if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n this.curls[finger].push([curl, confidence]);\n }\n\n direction(finger, position, confidence) {\n if (!this.directions[finger]) this.directions[finger] = [];\n this.directions[finger].push([position, confidence]);\n }\n\n weight(finger, weight) {\n this.weights[finger] = weight;\n // recalculate relative weights\n const total = this.weights.reduce((a, b) => a + b, 0);\n this.weightsRelative = this.weights.map((el) => el * 5 / total);\n }\n\n matchAgainst(detectedCurls, detectedDirections) {\n let confidence = 0.0;\n // look at the detected curl of each finger and compare with\n // the expected curl of this finger inside current gesture\n for (const fingerIdx in detectedCurls) {\n const detectedCurl = detectedCurls[fingerIdx];\n const expectedCurls = this.curls[fingerIdx];\n if (typeof expectedCurls === 'undefined') {\n // no curl description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible curl of this specific finger\n for (const [expectedCurl, score] of expectedCurls) {\n if (detectedCurl === expectedCurl) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n // same for detected direction of each finger\n for (const fingerIdx in detectedDirections) {\n const detectedDirection = detectedDirections[fingerIdx];\n const expectedDirections = this.directions[fingerIdx];\n if (typeof expectedDirections === 'undefined') {\n // no direction description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible direction of this specific finger\n for (const [expectedDirection, score] of expectedDirections) {\n if (detectedDirection === expectedDirection) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n return confidence / 10;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n ThumbsUp.curl(finger, full, 1.0);\n ThumbsUp.direction(finger, horizontalLeft, 1.0);\n ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\n/* eslint-disable camelcase */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n // curl estimation\n HALF_CURL_START_LIMIT: 60.0,\n NO_CURL_START_LIMIT: 130.0,\n // direction estimation\n DISTANCE_VOTE_POWER: 1.1,\n SINGLE_ANGLE_VOTE_POWER: 0.9,\n TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n const value = (point1y - point2y) / (point1x - point2x);\n let slope = Math.atan(value) * 180 / Math.PI;\n if (slope <= 0) slope = -slope;\n else if (slope > 0) slope = 180 - slope;\n return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n if (!point1 || !point2) return [0, 0];\n const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n if (point1.length === 2) return slopeXY;\n const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n let isVertical = 0;\n let isDiagonal = 0;\n let isHorizontal = 0;\n if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n else isHorizontal = 1 * weightageAt;\n return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const start_mid_z_dist = startPoint[2] - midPoint[2];\n const start_end_z_dist = startPoint[2] - endPoint[2];\n const mid_end_z_dist = midPoint[2] - endPoint[2];\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n if (cos_in > 1.0) cos_in = 1.0;\n else if (cos_in < -1.0) cos_in = -1.0;\n let angleOfCurve = Math.acos(cos_in);\n angleOfCurve = (57.2958 * angleOfCurve) % 180;\n let fingerCurl;\n if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n else fingerCurl = FingerCurl.full;\n return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n if (max_dist_x === Math.abs(start_end_x_dist)) {\n if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else {\n if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n }\n return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n let estimatedDirection;\n if (max_dist_y === Math.abs(start_end_y_dist)) {\n if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else {\n if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n }\n return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n if (reqd_vertical_direction === FingerDirection.verticalUp) {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n else estimatedDirection = FingerDirection.diagonalUpRight;\n } else {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n else estimatedDirection = FingerDirection.diagonalDownRight;\n }\n return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n let voteVertical = 0.0;\n let voteDiagonal = 0.0;\n let voteHorizontal = 0.0;\n const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n else voteHorizontal += options.DISTANCE_VOTE_POWER;\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n let calc_start_point_x = startPoint[0];\n let calc_start_point_y = startPoint[1];\n let calc_end_point_x = endPoint[0];\n let calc_end_point_y = endPoint[1];\n if (max_dist === start_mid_dist) {\n calc_end_point_x = endPoint[0];\n calc_end_point_y = endPoint[1];\n } else if (max_dist === mid_end_dist) {\n calc_start_point_x = midPoint[0];\n calc_start_point_y = midPoint[1];\n }\n const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n voteVertical += votes[0];\n voteDiagonal += votes[1];\n voteHorizontal += votes[2];\n for (const fingerSlope of fingerSlopes) {\n const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n voteVertical += fingerVotes[0];\n voteDiagonal += fingerVotes[1];\n voteHorizontal += fingerVotes[2];\n }\n // in case of tie, highest preference goes to Vertical,\n // followed by horizontal and then diagonal\n let estimatedDirection;\n if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n } else {\n estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n }\n return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n // step 1: calculate slopes\n const slopesXY: number[][] = [];\n const slopesYZ: number[][] = [];\n const fingerCurls: number[] = [];\n const fingerDirections: number[] = [];\n if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n // step 1: calculate slopes\n for (const finger of Finger.all) {\n const points = Finger.getPoints(finger);\n const slopeAtXY: number[] = [];\n const slopeAtYZ: number[] = [];\n for (const point of points) {\n const point1 = landmarks[point[0]];\n const point2 = landmarks[point[1]];\n // calculate single slope\n const slopes = getSlopes(point1, point2);\n const slopeXY = slopes[0];\n const slopeYZ = slopes[1];\n slopeAtXY.push(slopeXY);\n slopeAtYZ.push(slopeYZ);\n }\n slopesXY.push(slopeAtXY);\n slopesYZ.push(slopeAtYZ);\n }\n\n // step 2: calculate orientations\n for (const finger of Finger.all) {\n // start finger predictions from palm - except for thumb\n const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n const fingerPointsAt = Finger.getPoints(finger);\n const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n const endPoint = landmarks[fingerPointsAt[3][1]];\n // check if finger is curled\n const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n fingerCurls[finger] = fingerCurled;\n fingerDirections[finger] = fingerPosition;\n }\n return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n if (!keypoints || keypoints.length === 0) return null;\n const estimatorRes = estimate(keypoints);\n const landmarks = {};\n for (const fingerIdx of Finger.all) {\n landmarks[Finger.getName(fingerIdx)] = {\n curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n };\n }\n return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n const poses: { name: string, confidence: number }[] = [];\n if (!keypoints || keypoints.length === 0) return poses;\n const estimatorRes = estimate(keypoints);\n for (const gesture of Gestures) {\n const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n }\n return poses;\n}\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n | 'victory'\n | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { body: number, gesture: BodyGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n }\n }\n return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { face: number, gesture: FaceGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 450) {\n const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2] || 0;\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { iris: number, gesture: IrisGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations?.leftEyeIris?.[0] || !res[i].annotations?.rightEyeIris?.[0]) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n if (rightIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking right' });\n } else {\n if (leftIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking left' });\n }\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { hand: number, gesture: HandGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: { name: string, position: Point }[] = [];\n if (res[i].annotations) {\n for (const [finger, pos] of Object.entries(res[i].annotations)) {\n if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n if (res[i].keypoints) {\n const poses = fingerPose.match(res[i].keypoints);\n for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n }\n }\n return gestures;\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 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y: 0.40625 },\n { x: 0.84375, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.84375, y: 0.46875 },\n { x: 0.84375, y: 0.46875 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y: 0.59375 },\n { x: 0.96875, y: 0.59375 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.96875, y: 0.65625 },\n { x: 0.96875, y: 0.65625 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0.15625, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n 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0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 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0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 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},\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, Tensor1D, Tensor2D, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model: GraphModel) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSize = this?.model?.inputs?.[0]?.shape?.[2] || 0;\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n const t: Record = {};\n t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n const res = tf.concat2d([t.startPoints as Tensor2D, t.endPoints as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res as Tensor;\n }\n\n normalizeLandmarks(rawPalmLandmarks, index: number): Tensor {\n const t: Record = {};\n t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n t.div = tf.div(t.reshape, this.inputSizeTensor);\n t.landmarks = tf.add(t.div, this.anchors[index] ? this.anchors[index] : 0);\n const res = tf.mul(t.landmarks, this.inputSizeTensor);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n async predict(input: Tensor4D, config: Config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n t.div = tf.div(t.resize, constants.tf127);\n t.image = tf.sub(t.div, constants.tf1);\n t.batched = this.model.execute(t.image) as Tensor;\n t.predictions = tf.squeeze(t.batched);\n t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.slice);\n t.scores = tf.squeeze(t.sigmoid);\n const scores = await t.scores.data();\n t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n t.norm = this.normalizeBoxes(t.boxes);\n // box detection is flaky so we look for 3x boxes than we need results\n t.nms = await tf.image.nonMaxSuppressionAsync(t.norm as Tensor2D, t.scores as Tensor1D, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n const nms = await t.nms.array() as number[];\n const hands: { startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }[] = [];\n for (const index of nms) {\n const p: Record = {};\n p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n p.norm = this.normalizeLandmarks(p.slice, index);\n p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n const box = await p.box.data();\n const startPoint = box.slice(0, 2) as unknown as Point;\n const endPoint = box.slice(2, 4) as unknown as Point;\n const palmLandmarks = await p.palmLandmarks.array();\n const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n const scaled = util.scaleBoxCoordinates(hand, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);\n hands.push(scaled);\n Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: ({ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null)[];\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n this.inputSize = this.handPoseModel?.inputs?.[0].shape?.[2] || 0;\n this.storedBoxes = [];\n this.skipped = Number.MAX_SAFE_INTEGER;\n this.detectedHands = 0;\n }\n\n calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = util.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...util.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(coord[2]),\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames\n let boxes;\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n this.skipped++;\n } else {\n boxes = await this.handDetector.predict(image, config);\n this.skipped = 0;\n }\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: { landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }[] = [];\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n const palmCenter = util.getBoxCenter(currentBox);\n const palmCenterNormalized: [number, number] = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = tf.div(croppedInput, constants.tf255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Tensor[];\n lastTime = now();\n tf.dispose(handImage);\n const confidence = (await confidenceT.data())[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence / 4) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n const result = {\n landmarks: coords,\n confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n const result = {\n confidence: currentBox.confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: 0,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n landmarks: [],\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport function initPipeline() {\n const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!handPipeline) initPipeline();\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: HandResult[] = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n const keypoints = predictions[i].landmarks as unknown as Point[];\n let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: Box = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n const landmarks = fingerPose.analyze(keypoints);\n hands.push({\n id: i,\n score: Math.round(100 * predictions[i].confidence) / 100,\n boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n label: 'hand',\n box,\n boxRaw,\n keypoints,\n annotations: annotations as HandResult['annotations'],\n landmarks: landmarks as HandResult['landmarks'],\n });\n }\n return hands;\n}\n\nexport async function loadDetect(config: Config): Promise {\n if (env.initial) handDetectorModel = null;\n if (!handDetectorModel) handDetectorModel = await loadModel(config.hand.detector?.modelPath);\n else if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n return handDetectorModel;\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) handPoseModel = null;\n if (!handPoseModel) handPoseModel = await loadModel(config.hand.skeleton?.modelPath);\n else if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n return handPoseModel;\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ninterface HandDetectResult {\n id: number,\n score: number,\n box: Box,\n boxRaw: Box,\n label: HandType,\n}\n\nconst cache: {\n boxes: HandDetectResult[],\n hands: HandResult[];\n} = {\n boxes: [],\n hands: [],\n};\n\nconst fingerMap = {\n /*\n thumb: [0, 1, 2, 3, 4],\n index: [0, 5, 6, 7, 8],\n middle: [0, 9, 10, 11, 12],\n ring: [0, 13, 14, 15, 16],\n pinky: [0, 17, 18, 19, 20],\n palm: [0],\n */\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n base: [0],\n palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise {\n // HandTrack Model: Original: TFJS Port: \n if (env.initial) models[0] = null;\n if (!models[0]) {\n // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n // ideally need to prune the model itself\n fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n models[0] = await loadModel(config.hand.detector?.modelPath);\n const inputs = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) models[1] = null;\n if (!models[1]) {\n models[1] = await loadModel(config.hand.skeleton?.modelPath);\n const inputs = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models[0]) await loadDetect(config);\n if (!models[1]) await loadSkeleton(config);\n return models;\n}\n\nasync function detectHands(input: Tensor4D, config: Config): Promise {\n const hands: HandDetectResult[] = [];\n if (!input || !models[0]) return hands;\n const t: Record = {};\n const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n const width = Math.round(height * ratio / 8) * 8;\n t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n t.cast = tf.cast(t.resize, 'int32');\n [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n t.scores = tf.squeeze(t.rawScores, [0]);\n const classScores: Tensor[] = tf.unstack(t.scores, 1); // unstack scores based on classes\n tf.dispose(classScores[faceIndex]);\n classScores.splice(faceIndex, 1); // remove faces\n t.filtered = tf.stack(classScores, 1); // restack\n tf.dispose(classScores);\n // t.filtered = t.scores;\n t.max = tf.max(t.filtered, 1); // max overall score\n t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n let id = 0;\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.max as Tensor1D, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n const nms = await t.nms.data();\n const scores = await t.max.data();\n const classNum = await t.argmax.data();\n for (const nmsIndex of Array.from(nms)) { // generates results for each class\n const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n const boxYX = await boxSlice.data();\n tf.dispose(boxSlice);\n const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n const score = scores[nmsIndex];\n const label = classes[classNum[nmsIndex]] as HandType;\n const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n hands.push(hand);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n hands.sort((a, b) => b.score - a.score);\n if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n return hands;\n}\n\nasync function detectFingers(input: Tensor4D, h: HandDetectResult, config: Config): Promise {\n const hand: HandResult = { // initial values inherited from hand detect\n id: h.id,\n score: Math.round(100 * h.score) / 100,\n boxScore: Math.round(100 * h.score) / 100,\n fingerScore: 0,\n box: h.box,\n boxRaw: h.boxRaw,\n label: h.label,\n keypoints: [],\n landmarks: {} as HandResult['landmarks'],\n annotations: {} as HandResult['annotations'],\n };\n if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n const t: Record = {};\n const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n t.div = tf.div(t.crop, constants.tf255);\n [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n const rawScore = (await t.score.data())[0];\n const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n if (score >= (config.hand.minConfidence || 0)) {\n hand.fingerScore = score;\n t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n const coordsData: Point[] = await t.reshaped.array() as Point[];\n const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n return hand;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!models[0]?.['executor'] || !models[1]?.['executor'] || !models[0].inputs[0].shape || !models[1].inputs[0].shape) return []; // something is wrong with the model\n outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n skipped++; // increment skip frames\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.hands; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else { // finally rerun detector\n cache.boxes = await detectHands(input, config);\n lastTime = now();\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n skipped = 0;\n }\n\n const oldCache = [...cache.boxes];\n cache.boxes.length = 0; // reset cache\n if (config.cacheSensitivity > 0) {\n for (let i = 0; i < cache.hands.length; i++) {\n const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n const boxScale = box.scale(boxKpt.box, boxExpandFact);\n const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n // const boxCrop = box.crop(boxScaleRaw);\n cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n }\n }\n }\n for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n cache.hands[i].box = bbox.box;\n cache.hands[i].boxRaw = bbox.boxRaw;\n }\n resolve(cache.hands);\n });\n}\n", "/**\n * Type definitions for Human result object\n */\n\nimport type { Tensor } from './tfjs/types';\nimport type { FaceGesture, BodyGesture, HandGesture, IrisGesture } from './gesture/gesture';\nimport type { AnyCanvas } from './exports';\n\n/** generic box as [x, y, width, height] */\nexport type Box = [number, number, number, number];\n/** generic point as [x, y, z?] */\nexport type Point = [number, number, number?];\n\nexport type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';\nexport type Gender = 'male' | 'female' | 'unknown';\nexport type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';\nexport type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette'\n | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner'\n | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris'\n | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris'\n | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';\n\n/** Face results\n * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models\n * - Some values may be null if specific model is not enabled\n */\nexport interface FaceResult {\n /** face id */\n id: number\n /** overall face score */\n score: number,\n /** detection score */\n boxScore: number,\n /** mesh score */\n faceScore: number,\n /** detected face box */\n box: Box,\n /** detected face box normalized to 0..1 */\n boxRaw: Box,\n /** detected face box size */\n size: [number, number],\n /** detected face mesh */\n mesh: Point[]\n /** detected face mesh normalized to 0..1 */\n meshRaw: Point[],\n /** face contours as array of 2d points normalized to 0..1 */\n // contoursRaw: Array<[number, number]>,\n /** face contours as array of 2d points */\n // contours: Array<[number, number]>,\n /** mesh keypoints combined into annotated results */\n annotations: Record,\n /** detected age */\n age?: number,\n /** detected gender */\n gender?: Gender,\n /** gender detection score */\n genderScore?: number,\n /** detected emotions */\n emotion?: { score: number, emotion: Emotion }[],\n /** detected race */\n race?: { score: number, race: Race }[],\n /** face descriptor */\n embedding?: number[],\n /** face distance from camera */\n distance?: number,\n /** face anti-spoofing result confidence */\n real?: number,\n /** face liveness result confidence */\n live?: number,\n /** face rotation details */\n rotation?: {\n angle: { roll: number, yaw: number, pitch: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n } | null,\n /** detected face as tensor that can be used in further pipelines */\n tensor?: Tensor,\n}\n\nexport type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';\nexport type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder'\n | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle'\n | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';\nexport type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;\nexport type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';\nexport type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';\nexport type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;\n\n/** Body Result keypoints */\nexport interface BodyKeypoint {\n /** body part name */\n part: BodyLandmark,\n /** body part position */\n position: Point,\n /** body part position normalized to 0..1 */\n positionRaw: Point,\n /** body part position relative to body center in meters */\n distance?: Point,\n /** body part detection score */\n score: number,\n}\n\n/** Body results */\nexport interface BodyResult {\n /** body id */\n id: number,\n /** body detection score */\n score: number,\n /** detected body box */\n box: Box,\n /** detected body box normalized to 0..1 */\n boxRaw: Box,\n /** detected body keypoints */\n keypoints: BodyKeypoint[]\n /** detected body keypoints combined into annotated parts */\n annotations: Record,\n}\n\nexport type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';\nexport type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';\nexport type FingerCurl = 'none' | 'half' | 'full';\nexport type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';\n\n/** Hand results */\nexport interface HandResult {\n /** hand id */\n id: number,\n /** hand overal score */\n score: number,\n /** hand detection score */\n boxScore: number,\n /** hand skelton score */\n fingerScore: number,\n /** detected hand box */\n box: Box,\n /** detected hand box normalized to 0..1 */\n boxRaw: Box,\n /** detected hand keypoints */\n keypoints: Point[],\n /** detected hand class */\n label: HandType,\n /** detected hand keypoints combined into annotated parts */\n annotations: Record,\n /** detected hand parts annotated with part gestures */\n landmarks: Record,\n}\n\nexport type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter'\n | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee'\n | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork'\n | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant'\n | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book'\n | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';\n\n/** Object results */\nexport interface ObjectResult {\n /** object id */\n id: number,\n /** object detection score */\n score: number,\n /** detected object class id */\n class: number,\n /** detected object class name */\n label: ObjectType,\n /** detected object box */\n box: Box,\n /** detected object box normalized to 0..1 */\n boxRaw: Box,\n}\n\n/** Gesture combined results\n * Each result has:\n * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`\n * - gesture: gesture detected\n */\nexport type GestureResult =\n { 'face': number, gesture: FaceGesture }\n | { 'iris': number, gesture: IrisGesture }\n | { 'body': number, gesture: BodyGesture }\n | { 'hand': number, gesture: HandGesture }\n\n/** Person getter\n* - Triggers combining all individual results into a virtual person object\n*/\nexport interface PersonResult {\n /** person id */\n id: number,\n /** face result that belongs to this person */\n face: FaceResult,\n /** body result that belongs to this person */\n body: BodyResult | null,\n /** left and right hand results that belong to this person */\n hands: { left: HandResult | null, right: HandResult | null },\n /** detected gestures specific to this person */\n gestures: GestureResult[],\n /** box that defines the person */\n box: Box,\n /** box that defines the person normalized to 0..1 */\n boxRaw?: Box,\n}\n\n/**\n * Result interface definition for **Human** library\n *\n * Contains all possible detection results\n */\nexport interface Result {\n /** {@link FaceResult}: detection & analysis results */\n face: FaceResult[],\n /** {@link BodyResult}: detection & analysis results */\n body: BodyResult[],\n /** {@link HandResult}: detection & analysis results */\n hand: HandResult[],\n /** {@link GestureResult}: detection & analysis results */\n gesture: GestureResult[],\n /** {@link ObjectResult}: detection & analysis results */\n object: ObjectResult[]\n /** global performance object with timing values for each operation */\n performance: Record,\n /** optional processed canvas that can be used to draw input on screen */\n canvas?: AnyCanvas | null,\n /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */\n readonly timestamp: number,\n /** getter property that returns unified persons object */\n persons: PersonResult[],\n /** Last known error message */\n error: string | null;\n /** Resolution width */\n width: number,\n /** Resolution height */\n height: number,\n}\n\nexport const empty = (error: string | null = null): Result => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });\n", "export const kpt: string[] = [ // used to create part labels\n 'nose',\n 'leftEye',\n 'rightEye',\n 'leftEar',\n 'rightEar',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n];\n\nexport const horizontal: string[][] = [ // used to fix left vs right\n ['leftEye', 'rightEye'],\n ['leftEar', 'rightEar'],\n ['leftShoulder', 'rightShoulder'],\n ['leftElbow', 'rightElbow'],\n ['leftWrist', 'rightWrist'],\n ['leftHip', 'rightHip'],\n ['leftKnee', 'rightKnee'],\n ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: string[][] = [ // used to remove unlikely keypoint positions\n ['leftKnee', 'leftShoulder'],\n ['rightKnee', 'rightShoulder'],\n ['leftAnkle', 'leftKnee'],\n ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: string[][][] = [ // used to match relative body parts\n [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record = { // used to create body outline in annotations\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation, empty, FaceLandmark } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = empty();\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n const t0 = now();\n if (!newResult) return empty();\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n\n /* curve fitted: buffer = 8 - ln(delay)\n interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n - at 50ms delay buffer = ~4.1 => 28% towards live data\n - at 250ms delay buffer = ~2.5 => 40% towards live data\n - at 500ms delay buffer = ~1.8 => 55% towards live data\n - at 750ms delay buffer = ~1.4 => 71% towards live data\n - at 1sec delay buffer = 1 which means live data is used\n */\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n if (newResult.error) bufferedResult.error = newResult.error;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)) as BodyResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((newKpt, j) => ({\n score: newKpt.score,\n part: newKpt.part,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n ],\n distance: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n ],\n }))) as { score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }[];\n\n const annotations: Record = {} as Record; // recreate annotations\n let coords = { connected: {} };\n if (config.body.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n else if (config.body.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n else if (config.body.modelPath?.includes('movenet')) coords = moveNetCoords;\n for (const [name, indexes] of Object.entries(coords.connected as Record)) {\n const pt: Point[][] = [];\n for (let j = 0; j < indexes.length - 1; j++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n : [];\n let annotations = {};\n if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.hand[i].annotations;\n } else if (newResult.hand[i].annotations) {\n for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n annotations[key] = newResult.hand[i]?.annotations?.[key]?.[0]\n ? newResult.hand[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)) as FaceResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n let annotations: Record = newResult.face[i].annotations;\n if (Object.keys(bufferedResult.face[i].annotations).length !== Object.keys(newResult.face[i].annotations).length) {\n bufferedResult.face[i].annotations = newResult.face[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.face[i].annotations;\n } else if (newResult.face[i].annotations) {\n for (const key of Object.keys(newResult.face[i].annotations)) { // update annotations\n annotations[key] = newResult.face[i]?.annotations?.[key]?.[0]\n ? newResult.face[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n if (newResult.face[i].rotation) {\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.bearing || 0) + (newResult.face[i].rotation?.gaze.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.strength || 0) + (newResult.face[i].rotation?.gaze.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw, annotations }; // shallow clone plus updated values\n } else {\n bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw, annotations }; // shallow clone plus updated values\n }\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)) as ObjectResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)) as PersonResult[];\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n }\n }\n }\n\n // copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n // copy resolution info\n bufferedResult.width = newResult.width;\n bufferedResult.height = newResult.height;\n\n // append interpolation performance data\n const t1 = now();\n interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n return bufferedResult;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]);\n // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);\n // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel\n t.fg = tf.softmax(t.fgRaw);\n t.mul = tf.mul(t.fg, constants.tf255);\n t.expand = tf.expandDims(t.mul, 2);\n t.output = tf.image.resizeBilinear(t.expand as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.output], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.output, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = number[]\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n // general minkowski distance, euclidean distance is limited case where order is 2\n if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\n let sum = 0;\n for (let i = 0; i < descriptor1.length; i++) {\n const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n }\n const dist = Math.round(100 * (options.multiplier || 20) * sum) / 100;\n return dist;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n if (dist === 0) return 1; // short circuit for identical inputs\n const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n const clamp = Math.round(100 * Math.max(Math.min(norm, 1), 0)) / 100; // clamp to 0..1\n return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n * default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n const dist = distance(descriptor1, descriptor2, options);\n return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function find(descriptor: Descriptor, descriptors: Descriptor[], options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { // validate input\n return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n }\n let lowestDistance = Number.MAX_SAFE_INTEGER;\n let index = -1;\n for (let i = 0; i < descriptors.length; i++) {\n const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\n if (res < lowestDistance) {\n lowestDistance = res;\n index = i;\n }\n if (lowestDistance < (options.threshold || 0)) break;\n }\n const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gear from './gear/gear';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as insightface from './face/insightface';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as meet from './segmentation/meet';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(instance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n if (!model) return null;\n if (!instance?.config?.validateModels) return null;\n const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];\n const ops: string[] = [];\n const missing: string[] = [];\n interface Op { name: string, category: string, op: string }\n const url = model['modelUrl'] as string;\n const executor = model['executor'];\n if (executor?.graph?.nodes) {\n for (const kernel of Object.values(executor.graph.nodes)) {\n const op = (kernel as Op).op.toLowerCase();\n if (!ops.includes(op)) ops.push(op);\n }\n } else {\n if (!executor && instance.config.debug) {\n log('model not loaded', name);\n }\n }\n for (const op of ops) {\n if (!simpleOps.includes(op) // exclude simple ops\n && !ignoreOps.includes(op) // exclude specific ops\n && !instance.env.kernels.includes(op) // check actual kernel ops\n && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n missing.push(op);\n }\n }\n if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n numLoadedModels: number,\n numDefinedModels: number,\n percentageLoaded: number,\n totalSizeFromManifest: number,\n totalSizeWeights: number,\n totalSizeLoading: number,\n modelStats: ModelInfo[],\n}\n\n/** Models class used by Human\n * - models: record of all GraphModels\n * - list: returns list of configured models with their stats\n * - loaded: returns array of loaded models\n * - reset: unloads all models\n * - validate: checks loaded models for valid kernel ops vs current backend\n * - stats: live detailed model stats that can be checked during model load phase\n */\nexport class Models {\n private instance: Human;\n models: Record = {};\n\n constructor(currentInstance: Human) {\n this.models = {};\n this.instance = currentInstance;\n }\n\n stats(): ModelStats {\n let totalSizeFromManifest = 0;\n let totalSizeWeights = 0;\n let totalSizeLoading = 0;\n for (const m of Object.values(modelStats)) {\n totalSizeFromManifest += Number.isNaN(+m.sizeFromManifest) ? 0 : m.sizeFromManifest;\n totalSizeWeights += Number.isNaN(+m.sizeLoadedWeights) ? 0 : m.sizeLoadedWeights;\n totalSizeLoading += Number.isNaN(+m.sizeDesired) ? 0 : m.sizeDesired;\n }\n const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n return {\n numLoadedModels: Object.values(modelStats).filter((m) => m?.loaded).length,\n numDefinedModels: Object.keys(this.models).length,\n percentageLoaded,\n totalSizeFromManifest,\n totalSizeWeights,\n totalSizeLoading,\n modelStats: Object.values(modelStats),\n };\n }\n\n reset(): void {\n for (const model of Object.keys(this.models)) this.models[model] = null;\n }\n\n async load(instance?: Human): Promise {\n if (env.initial) this.reset();\n if (instance) this.instance = instance;\n const m: Record> = {};\n // face main models\n m.blazeface = (this.instance.config.face.enabled && !this.models.blazeface) ? blazeface.load(this.instance.config) : null;\n m.antispoof = (this.instance.config.face.enabled && this.instance.config.face.antispoof?.enabled && !this.models.antispoof) ? antispoof.load(this.instance.config) : null;\n m.liveness = (this.instance.config.face.enabled && this.instance.config.face.liveness?.enabled && !this.models.liveness) ? liveness.load(this.instance.config) : null;\n m.faceres = (this.instance.config.face.enabled && this.instance.config.face.description?.enabled && !this.models.faceres) ? faceres.load(this.instance.config) : null;\n m.emotion = (this.instance.config.face.enabled && this.instance.config.face.emotion?.enabled && !this.models.emotion) ? emotion.load(this.instance.config) : null;\n m.iris = (this.instance.config.face.enabled && this.instance.config.face.iris?.enabled && !this.instance.config.face.attention?.enabled && !this.models.iris) ? iris.load(this.instance.config) : null;\n m.facemesh = (this.instance.config.face.enabled && this.instance.config.face.mesh?.enabled && (!this.models.facemesh)) ? facemesh.load(this.instance.config) : null;\n // face alternatives\n m.gear = (this.instance.config.face.enabled && this.instance.config.face['gear']?.enabled && !this.models.gear) ? gear.load(this.instance.config) : null;\n m.ssrnetage = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetage) ? ssrnetAge.load(this.instance.config) : null;\n m.ssrnetgender = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetgender) ? ssrnetGender.load(this.instance.config) : null;\n m.mobilefacenet = (this.instance.config.face.enabled && this.instance.config.face['mobilefacenet']?.enabled && !this.models.mobilefacenet) ? mobilefacenet.load(this.instance.config) : null;\n m.insightface = (this.instance.config.face.enabled && this.instance.config.face['insightface']?.enabled && !this.models.insightface) ? insightface.load(this.instance.config) : null;\n // body alterinatives\n m.blazepose = (this.instance.config.body.enabled && !this.models.blazepose && this.instance.config.body.modelPath?.includes('blazepose')) ? blazepose.loadPose(this.instance.config) : null;\n m.blazeposedetect = (this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body['detector'] && this.instance.config.body['detector'].modelPath) ? blazepose.loadDetect(this.instance.config) : null;\n m.efficientpose = (this.instance.config.body.enabled && !this.models.efficientpose && this.instance.config.body.modelPath?.includes('efficientpose')) ? efficientpose.load(this.instance.config) : null;\n m.movenet = (this.instance.config.body.enabled && !this.models.movenet && this.instance.config.body.modelPath?.includes('movenet')) ? movenet.load(this.instance.config) : null;\n m.posenet = (this.instance.config.body.enabled && !this.models.posenet && this.instance.config.body.modelPath?.includes('posenet')) ? posenet.load(this.instance.config) : null;\n // hand alternatives\n m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null;\n m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null;\n // if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null];\n if (this.instance.config.hand.enabled && !this.models.handdetect && this.instance.config.hand.detector?.modelPath?.includes('handdetect')) {\n m.handdetect = handpose.loadDetect(this.instance.config);\n m.handskeleton = handpose.loadSkeleton(this.instance.config);\n }\n // object detection alternatives\n m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null;\n m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null;\n // segmentation alternatives\n m.selfie = (this.instance.config.segmentation.enabled && !this.models.selfie && this.instance.config.segmentation.modelPath?.includes('selfie')) ? selfie.load(this.instance.config) : null;\n m.meet = (this.instance.config.segmentation.enabled && !this.models.meet && this.instance.config.segmentation.modelPath?.includes('meet')) ? meet.load(this.instance.config) : null;\n m.rvm = (this.instance.config.segmentation.enabled && !this.models.rvm && this.instance.config.segmentation.modelPath?.includes('rvm')) ? rvm.load(this.instance.config) : null;\n\n // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n for (const [model, promise] of Object.entries(m)) {\n if (promise?.['then']) promise['then']((val) => this.models[model] = val);\n }\n await Promise.all(Object.values(m)); // wait so this function does not resolve prematurely\n }\n\n list() {\n const models = Object.keys(this.models).map((model) => ({ name: model, loaded: (this.models[model] !== null), size: 0, url: this.models[model] ? this.models[model]?.['modelUrl'] : null }));\n for (const m of models) {\n const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));\n if (!stats) continue;\n m.size = modelStats[stats].sizeLoadedWeights;\n m.url = modelStats[stats].url;\n }\n return models;\n }\n\n loaded(): string[] {\n const list = this.list();\n const loaded = list.filter((model) => model.loaded).map((model) => model.name);\n return loaded;\n }\n\n validate(): { name: string, missing: string[] }[] {\n const missing: KernelOps[] = [];\n for (const defined of Object.keys(this.models)) {\n const model: GraphModel | null = this.models[defined as keyof Models];\n if (!model) continue;\n const res = validateModel(this.instance, model, defined);\n if (res) missing.push(res);\n }\n return missing;\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport type { Tensor, Tensor3D } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n keypoints: BodyKeypoint[],\n padding: [number, number][];\n} = {\n keypoints: [],\n padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n for (const pair of coords.horizontal) { // fix body parts left vs right\n const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n if (body.keypoints[left] && body.keypoints[right]) {\n if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n }\n for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n if (body.keypoints[lower] && body.keypoints[higher]) {\n if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n body.keypoints.splice(lower, 1);\n }\n }\n }\n for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n const distanceLeft = body.keypoints[left] ? [\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n ] : [0, 0];\n const distanceRight = body.keypoints[right] ? [\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n ] : [0, 0];\n if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n}\n\nexport function jitter(keypoints: BodyKeypoint[]): BodyKeypoint[] {\n for (let i = 0; i < keypoints.length; i++) {\n if (keypoints[i] && cache.keypoints[i]) {\n const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n if (diff[0] < maxJitter && diff[1] < maxJitter) {\n keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n } else {\n cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n }\n } else {\n cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n }\n }\n return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n cache.padding = [\n [0, 0], // dont touch batch\n [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(input, cache.padding);\n t.resize = tf.image.resizeBilinear(t.pad as Tensor3D, [inputSize, inputSize]);\n const final = tf.cast(t.resize, 'int32');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n body.keypoints = body.keypoints.filter((kpt) => kpt?.position); // filter invalid keypoints\n for (const kpt of body.keypoints) {\n kpt.position = [\n kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n ];\n kpt.positionRaw = [\n kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n ];\n }\n const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n body.box = rescaledBoxes.box;\n body.boxRaw = rescaledBoxes.boxRaw;\n return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n boxes: Box[], // unused\n bodies: BodyResult[];\n last: number,\n} = {\n boxes: [],\n bodies: [],\n last: 0,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n fakeOps(['size'], config);\n model = await loadModel(config.body.modelPath);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize < 64) inputSize = 256;\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', false); // default=false \n return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n const kpt = res[0][0];\n const keypoints: BodyKeypoint[] = [];\n let score = 0;\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw,\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * positionRaw[0]),\n Math.round((image.shape[1] || 0) * positionRaw[1]),\n ],\n });\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const bodies: BodyResult[] = [];\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n const annotations: Record = {};\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n fix.bodyParts(body);\n bodies.push(body);\n return bodies;\n}\n\nfunction parseMultiPose(res, config, image) {\n const bodies: BodyResult[] = [];\n for (let id = 0; id < res[0].length; id++) {\n const kpt = res[0][id];\n const boxScore = Math.round(100 * kpt[51 + 4]) / 100;\n if (boxScore > config.body.minConfidence) {\n const keypoints: BodyKeypoint[] = [];\n for (let i = 0; i < 17; i++) {\n const score = kpt[3 * i + 2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n keypoints.push({\n part: coords.kpt[i] as BodyLandmark,\n score: Math.round(100 * score) / 100,\n positionRaw,\n position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n });\n }\n }\n // const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n // movenet-multipose has built-in box details\n const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n const boxNorm: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n // const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n const body: BodyResult = { id, score: boxScore, box: boxNorm, boxRaw, keypoints: [...keypoints], annotations };\n fix.bodyParts(body);\n bodies.push(body);\n }\n }\n bodies.sort((a, b) => b.score - a.score);\n if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return []; // something is wrong with the model\n if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n skipped++; // increment skip frames\n const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.bodies; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const t: Record = {};\n skipped = 0;\n // run detection on squared input and no cached boxes\n t.input = fix.padInput(input, inputSize);\n t.res = model?.execute(t.input) as Tensor;\n cache.last = now();\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17)\n ? parseSinglePose(res, config, input)\n : parseMultiPose(res, config, input);\n for (const body of cache.bodies) {\n fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n fix.jitter(body.keypoints);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n resolve(cache.bodies);\n });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) {\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n let id = 0;\n let results: ObjectResult[] = [];\n const size = inputSize;\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)) as Tensor2D);\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n const featuresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)) as Tensor2D);\n const boxesMaxT = tf.reshape(featuresT, [-1, 4, (featuresT.shape?.[1] || 0) / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdxT = tf.argMax(boxesMaxT, 2); // what we need is indexes of features with highest scores, not values itself\n const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < (scoresT.shape?.[1] || 0); j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > (config.object.minConfidence || 0) && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / (size))); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label as ObjectType,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: box.map((a) => Math.trunc(a)) as Box,\n boxRaw,\n };\n results.push(result);\n }\n }\n }\n tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n }\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: number[] = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected || 0, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = Array.from(await nms.data());\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n const resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n const normT = tf.div(resizeT, constants.tf255);\n const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n let objectT;\n if (config.object.enabled) objectT = model.execute(transposeT);\n lastTime = now();\n\n const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n last = obj;\n tf.dispose([resizeT, normT, transposeT, ...objectT]);\n resolve(obj);\n });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): BodyResult[] {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i): BodyResult => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score: score as number,\n part: part as BodyLandmark,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n })),\n annotations: {} as Record,\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: unknown[]; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n // @ts-ignore getter is of unknown type\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n\nexport function getOffsetPoint(y, x, keypoint: number, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + count),\n };\n}\n\nexport function getImageCoords(part, outputStride: number, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a: { x: number, y: number }, b: { x: number, y: number }) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId: number, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: utils.partNames[root.part.id] as BodyLandmark,\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score: number, heatmapY: number, heatmapX: number, scores) {\n const [height, width]: [number, number] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: { keypoints, box: Box, score: number }[] = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n /** posenet is mostly obsolete\n * caching is not implemented\n */\n if (!model?.['executor']) return [];\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Tensor[] = model.execute(normalized, poseNetOutputs) as Tensor[];\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\n// internal state varaibles\nconst outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];\nconst t: Record = {}; // contains input tensor and recurrent states\nlet ratio = 0;\n\nfunction init(config: Config) {\n tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);\n t.r1i = tf.tensor(0.0);\n t.r2i = tf.tensor(0.0);\n t.r3i = tf.tensor(0.0);\n t.r4i = tf.tensor(0.0);\n ratio = config.segmentation.ratio || 0.5;\n t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n init(config);\n return model;\n}\n\nconst normalize = (r: Tensor): Tensor => tf.tidy(() => {\n const squeeze = tf.squeeze(r, ([0]));\n const mul = tf.mul(squeeze, constants.tf255);\n const cast = tf.cast(mul, 'int32');\n return cast;\n});\n\nfunction getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present\n const rgb = fgr\n ? normalize(fgr) // normalize and use value\n : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const a = pha\n ? normalize(pha) // normalize and use value\n : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const rgba = tf.concat([rgb, a], -1);\n tf.dispose([rgb, a]);\n return rgba;\n}\n\nfunction getState(state: Tensor): Tensor { // gets internal recurrent states\n return tf.tidy(() => {\n const r: Record = {};\n r.unstack = tf.unstack(state, -1);\n r.concat = tf.concat(r.unstack, 1);\n r.split = tf.split(r.concat, 4, 1);\n r.stack = tf.concat(r.split, 2);\n r.squeeze = tf.squeeze(r.stack, [0]);\n r.expand = tf.expandDims(r.squeeze, -1);\n r.add = tf.add(r.expand, 1);\n r.mul = tf.mul(r.add, 127.5);\n r.cast = tf.cast(r.mul, 'int32');\n r.tile = tf.tile(r.cast, [1, 1, 3]);\n r.alpha = tf.fill([(r.tile as Tensor).shape[0] || 0, (r.tile as Tensor).shape[1] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-unnecessary-type-assertion\n return tf.concat([r.tile, r.alpha], -1);\n });\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor']) return null;\n // const expand = tf.expandDims(input, 0);\n t.src = tf.div(input, 255);\n if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed\n const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n rgba = getRGBA(fgr, pha);\n break;\n case 'alpha':\n rgba = getRGBA(null, pha);\n break;\n case 'foreground':\n rgba = getRGBA(fgr, null);\n break;\n case 'state':\n rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o\n break;\n default:\n rgba = tf.tensor(0);\n }\n tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);\n [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states\n return rgba;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n t.alpha = tf.image.resizeBilinear(t.squeeze as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]); // model selfie has a single channel that we can use directly\n t.mul = tf.mul(t.alpha, constants.tf255);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.mul], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n let id = 0;\n const persons: PersonResult[] = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.left?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.right?.id) person.gestures.push(gesture);\n }\n\n // create new overarching box from all boxes belonging to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face.box);\n extractXY(person.body?.box);\n extractXY(person.hands.left?.box);\n extractXY(person.hands.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape?.[1] && shape?.[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face: string = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport { empty, Result } from './result';\nimport type { Config } from './config';\nimport type { Human } from './human';\nimport type { Tensor, DataType } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise {\n const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob: Blob | null;\n let res: Result | undefined;\n switch (instance.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'body':\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await instance.detect(bitmap, instance.config);\n bitmap.close();\n }\n return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise {\n return new Promise((resolve) => {\n let src: string;\n // let size = 0;\n switch (instance.config.warmup) {\n case 'face':\n // size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n // size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = '';\n }\n // src = encodeURI('../assets/human-sample-upper.jpg');\n let img: HTMLImageElement;\n if (typeof Image !== 'undefined') img = new Image();\n // @ts-ignore env.image is an external monkey-patch\n else if (env.Image) img = new env.Image();\n else {\n resolve(undefined);\n return;\n }\n img.onload = async () => {\n const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n if (!canvas) {\n log('Warmup: Canvas not found');\n resolve(undefined);\n } else {\n const ctx = canvas.getContext('2d') as CanvasRenderingContext2D;\n if (ctx) ctx.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const tensor = await instance.image(canvas, true);\n const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\n resolve(res);\n }\n };\n if (src) img.src = src;\n else resolve(undefined);\n });\n}\n\nasync function warmupNode(instance: Human): Promise {\n const atob = (str: string) => Buffer.from(str, 'base64');\n let img;\n if (instance.config.warmup === 'face') img = atob(sample.face);\n else img = atob(sample.body);\n let res: Result;\n if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n // @ts-ignore\n const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n const expanded: Tensor = tf.expandDims(data, 0);\n instance.tf.dispose(data);\n // log('Input:', expanded);\n res = await instance.detect(expanded, instance.config);\n instance.tf.dispose(expanded);\n } else {\n if (instance.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await instance.detect(input, instance.config);\n */\n }\n // @ts-ignore\n return res;\n}\n\nasync function runInference(instance: Human) {\n let res: Result | undefined;\n if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n else if ((typeof Image !== 'undefined') || (env.Canvas !== undefined)) res = await warmupCanvas(instance);\n else res = await warmupNode(instance);\n return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n // @ts-ignore private property\n if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n const backendType = tf.getBackend();\n const webGLBackend = tf.backend();\n if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.['checkCompileCompletion']) {\n // log('compile pass: skip');\n return;\n }\n tf.env().set('ENGINE_COMPILE_ONLY', true);\n const numTensorsStart = tf.engine().state.numTensors;\n const compiledModels: string[] = [];\n for (const [modelName, model] of Object.entries(instance.models.models)) {\n if (!model) continue;\n const shape = (model?.modelSignature && model?.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n const dtype: DataType = (model?.modelSignature && model?.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n for (let dim = 0; dim < shape.length; dim++) {\n if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n }\n const tensor = tf.zeros(shape, dtype);\n try {\n const res = model.execute(tensor);\n compiledModels.push(modelName);\n if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n else tf.dispose(res);\n } catch {\n if (instance.config.debug) log('compile fail model:', modelName);\n }\n tf.dispose(tensor);\n }\n const kernels = await webGLBackend['checkCompileCompletionAsync']();\n webGLBackend['getUniformLocations']();\n if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n tf.env().set('ENGINE_COMPILE_ONLY', false);\n const numTensorsEnd = tf.engine().state.numTensors;\n if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial): Promise {\n await backend.check(instance, false);\n const t0 = now();\n instance.state = 'warmup';\n if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n return empty();\n }\n return new Promise(async (resolve) => {\n await instance.models.load();\n await tf.ready();\n await runCompile(instance);\n const res = await runInference(instance);\n const t1 = now();\n if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n instance.emit('warmup');\n resolve(res);\n });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\n// module imports\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as draw from './draw/draw';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as meet from './segmentation/meet';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as warmups from './warmup';\n\n// type definitions\nimport { Input, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, AnyCanvas, empty } from './exports';\nimport type { Tensor, Tensor4D } from './tfjs/types';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n\n /** Current configuration\n * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\n config: Config;\n\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n\n /** currenty processed image tensor and canvas */\n process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n /** Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n * [TFJS API](https://js.tensorflow.org/api/latest/)\n */\n tf;\n\n /** Object containing environment information used for diagnostics */\n env: Env = env;\n\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - canvas: draws input to canvas\n * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n */\n // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n draw: typeof draw = draw;\n\n /** Face Matching\n * - similarity: compare two face descriptors and return similarity index\n * - distance: compare two face descriptors and return raw calculated differences\n * - find: compare face descriptor to array of face descriptors and return best match\n */\n match: typeof match = match;\n\n /** Currently loaded models\n * @internal\n * {@link models#Models}\n */\n models: models.Models;\n\n /** Container for events dispatched by Human\n * Possible events:\n * - `create`: triggered when Human object is instantiated\n * - `load`: triggered when models are loaded (explicitly or on-demand)\n * - `image`: triggered when input image is processed\n * - `result`: triggered when detection is complete\n * - `warmup`: triggered when warmup is complete\n * - `error`: triggered on some errors\n */\n events: EventTarget | undefined;\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: number[];\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: [number, number][];\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n // definition end\n\n /** Constructor for **Human** library that is futher used for all operations\n * @param userConfig - user configuration object {@link Config}\n */\n constructor(userConfig?: Partial) {\n /*\n defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n ? 'https://vladmandic.github.io/tfjs/dist/'\n : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n */\n const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n this.version = app.version; // expose version property on instance of class\n Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n this.config = JSON.parse(JSON.stringify(defaults));\n Object.seal(this.config);\n this.config.cacheModels = typeof indexedDB !== 'undefined';\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n setModelLoadOptions(this.config);\n this.tf = tf;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.performance = {};\n this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n // object that contains all initialized models\n this.models = new models.Models(this);\n // reexport draw methods\n draw.init();\n this.result = empty();\n // export access to image processing\n this.process = { tensor: null, canvas: null };\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // init model validation\n models.validateModel(this, null, '');\n // include platform info\n this.emit('create');\n if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n const envTemp = JSON.parse(JSON.stringify(this.env));\n delete envTemp.kernels;\n delete envTemp.initial;\n delete envTemp.perfadd;\n if (this.config.debug) log('environment:', envTemp);\n }\n\n /** internal function to measure tensor leaks */\n analyze = (...msg: string[]) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n };\n\n /** internal function for quick sanity check on inputs @hidden */\n #sanity = (input: Input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n };\n\n /** Reset configuration to default values */\n reset(): void {\n const currentBackend = this.config.backend; // save backend;\n this.config = JSON.parse(JSON.stringify(defaults));\n this.config.backend = currentBackend;\n image.reset();\n env.initial = true;\n }\n\n /** Validate current configuration schema */\n validate(userConfig?: Partial) {\n const msgs = validate(defaults, userConfig || this.config);\n if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n return msgs;\n }\n\n /** Utility wrapper for performance.now() */\n now(): number { // eslint-disable-line class-methods-use-this\n return now();\n }\n\n /** Process input as return canvas and tensor\n *\n * @param input - any input {@link Input}\n * @param getTensor - should image processing also return tensor or just canvas\n * Returns object with `tensor` and `canvas`\n */\n image(input: Input, getTensor: boolean = false) {\n return image.process(input, this.config, getTensor);\n }\n\n /** Segmentation method takes any input and returns RGBA tensor\n * Note: Segmentation is not triggered as part of detect process\n *\n * @param input - {@link Input}\n * Returns tensor which contains image data in RGBA format\n */\n async segmentation(input: Input, userConfig?: Partial): Promise {\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (!this.config.segmentation.enabled) return null;\n const processed = await image.process(input, this.config);\n if (!processed.tensor) return null;\n let tensor: Tensor | null = null;\n if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);\n tf.dispose(processed.tensor);\n return tensor;\n }\n\n /** Compare two input tensors for pixel similarity\n * - use `human.image` to process any valid input and get a tensor that can be used for compare\n * - when passing manually generated tensors:\n * - both input tensors must be in format [1, height, width, 3]\n * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n * - return value is pixel similarity score normalized by input resolution and rgb channels\n */\n compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise {\n return image.compare(this.config, firstImageTensor, secondImageTensor);\n }\n\n /** Explicit backend initialization\n * - Normally done implicitly during initial load phase\n * - Call to explictly register and initialize TFJS backend without any other operations\n * - Use when changing backend during runtime\n */\n async init(): Promise {\n await backend.check(this, true);\n await this.tf.ready();\n image.reset();\n }\n\n /** WebCam helper methods\n *\n */\n public webcam = new WebCam();\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n *\n * @param userConfig - {@link Config}\n */\n async load(userConfig?: Partial): Promise {\n this.state = 'load';\n const timeStamp = now();\n const count = Object.values(this.models.models).filter((model) => model).length;\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (this.env.initial) { // print version info on first run and check for correct backend setup\n if (!await backend.check(this, false)) log('error: backend check failed');\n await tf.ready();\n if (this.env.browser) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n\n await this.models.load(this); // actually loads models\n if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n this.env.initial = false;\n\n const loaded = Object.values(this.models.models).filter((model) => model).length;\n if (loaded !== count) { // number of loaded models changed\n this.models.validate(); // validate kernel ops used by model against current backend\n this.emit('load');\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n }\n\n /** emit event */\n emit = (event: string) => {\n if (this.events?.dispatchEvent) this.events.dispatchEvent(new Event(event));\n };\n\n /** Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result - {@link Result} optional use specific result set to run interpolation on\n * @returns result - {@link Result}\n */\n next(result: Result = this.result): Result {\n return interpolate.calc(result, this.config);\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async warmup(userConfig?: Partial) {\n const t0 = now();\n const res = await warmups.warmup(this, userConfig);\n const t1 = now();\n this.performance.warmup = Math.trunc(t1 - t0);\n return res;\n }\n\n /** Run detect with tensorflow profiling\n * - result object will contain total exeuction time information for top-20 kernels\n * - actual detection object can be accessed via `human.result`\n */\n async profile(input: Input, userConfig?: Partial): Promise<{ kernel: string, time: number, perc: number }[]> {\n // @ts-ignore profile wraps method return values\n const profile = await this.tf.profile(() => this.detect(input, userConfig));\n const kernels: Record = {};\n let total = 0;\n for (const kernel of profile.kernels) { // sum kernel time values per kernel\n const ms = Number(kernel.kernelTimeMs) || 0;\n if (kernels[kernel.name]) kernels[kernel.name] += ms;\n else kernels[kernel.name] = ms;\n total += ms;\n }\n const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n for (const kernel of kernelArr) {\n kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n kernel.time = Math.round(1000 * kernel.time) / 1000;\n }\n kernelArr.sort((a, b) => b.time - a.time); // sort\n kernelArr.length = 20; // crop\n return kernelArr;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input - {@link Input}\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async detect(input: Input, userConfig?: Partial): Promise {\n // detection happens inside a promise\n this.state = 'detect';\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n // sanity checks\n this.state = 'check';\n const error = this.#sanity(input);\n if (error) {\n log(error, input);\n this.emit('error');\n resolve(empty(error));\n }\n\n const timeStart = now();\n\n // load models if enabled\n await this.load();\n\n timeStamp = now();\n this.state = 'image';\n const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor4D };\n this.process = img;\n this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n if (!img.tensor) {\n if (this.config.debug) log('could not convert input to tensor');\n this.emit('error');\n resolve(empty('could not convert input to tensor'));\n return;\n }\n this.emit('image');\n\n timeStamp = now();\n this.config.skipAllowed = await image.skip(this.config, img.tensor);\n this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; // disable autoBrightness on scene change\n if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n (this.performance.totalFrames)++;\n if (this.config.skipAllowed) this.performance.cachedFrames++;\n this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes: FaceResult[] | Promise | never[] = [];\n let bodyRes: BodyResult[] | Promise | never[] = [];\n let handRes: HandResult[] | Promise | never[] = [];\n let objectRes: ObjectResult[] | Promise | never[] = [];\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n this.state = 'detect:face';\n if (this.config.async) {\n faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n\n if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n this.state = 'detect:body';\n const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n if (this.config.async) {\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n timeStamp = now();\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n this.state = 'detect:hand';\n const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n if (this.config.async) {\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n timeStamp = now();\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // run object detection\n this.analyze('Start Object:');\n this.state = 'detect:object';\n if (this.config.async) {\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n timeStamp = now();\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Object:');\n\n // if async wait for results\n this.state = 'detect:await';\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n this.state = 'detect:gesture';\n let gestureRes: GestureResult[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n const shape = this.process.tensor?.shape || [0, 0, 0, 0];\n this.result = {\n face: faceRes as FaceResult[],\n body: bodyRes as BodyResult[],\n hand: handRes as HandResult[],\n gesture: gestureRes,\n object: objectRes as ObjectResult[],\n performance: this.performance,\n canvas: this.process.canvas,\n timestamp: Date.now(),\n error: null,\n width: shape[2],\n height: shape[1],\n get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(img.tensor);\n\n // log('Result:', result);\n this.emit('detect');\n this.state = 'idle';\n resolve(this.result);\n });\n }\n\n /** Helper function\n * @param ms - sleep time in miliseconds\n */\n async sleep(ms: number): Promise { // eslint-disable-line class-methods-use-this\n return new Promise((resolve) => { setTimeout(resolve, ms); });\n }\n\n /** internal structure that keeps track of processed videos @hidden */\n #loops: Record = {};\n /** Continously detect video frames\n * @param element - HTMLVideoElement input\n * @param run - boolean run continously or stop if already running, default true\n * @param delay - number delay detection between frames for number of miliseconds, default 0\n */\n async video(element: HTMLVideoElement, run: boolean = true, delay: number = 0) {\n if (run) {\n if (!this.#loops[element.id]) {\n if (this.config.debug) log('video start', element.id);\n this.#loops[element.id] = true;\n }\n if (!element.paused && this.#loops[element.id] && (element.readyState >= 2)) await this.detect(element);\n if (delay > 0) await this.sleep(delay);\n if (this.#loops[element.id]) requestAnimationFrame(() => this.video(element, run, delay));\n } else {\n if (this.config.debug) log('video stop', element.id);\n this.#loops[element.id] = false;\n }\n }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"], - "mappings": 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yw;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(yw||(yw={}));var bw;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(bw||(bw={}));var Cw;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(Cw||(Cw={}));var f4={float32:bw,int32:xw,bool:yw,complex64:Cw};function dt(r,e){if(r===\"string\"||e===\"string\"){if(r===\"string\"&&e===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${e}`)}return f4[r][e]}function oi(r){return dt(r,\"int32\")}function rd(r){return r!=null&&typeof r==\"object\"&&\"texture\"in r&&r.texture instanceof WebGLTexture}function od(r){return typeof GPUBuffer!=\"undefined\"&&r!=null&&typeof r==\"object\"&&\"buffer\"in r&&r.buffer instanceof GPUBuffer}function Oe(r,e){if(r.dtype===e.dtype)return[r,e];let t=dt(r.dtype,e.dtype);return[r.cast(t),e.cast(t)]}function ww(r,e){E(r.dtype===e.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`)}function h4(r,e){return e.some(t=>t.id===r.id)}function Cl(r){let e=[];return tk(r,e,new Set),e}function tk(r,e,t){if(r==null)return;if(r instanceof mt){e.push(r);return}if(!g4(r))return;let o=r;for(let n in o){let s=o[n];t.has(s)||(t.add(s),tk(s,e,t))}}function g4(r){return Array.isArray(r)||typeof r==\"object\"}function Sw(r){return r.kernelName!=null}var nd=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},wl=class r{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new nd}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Ym(e).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let o=t.factory();if(o&&!(o instanceof ao)&&typeof o.then==\"function\"){let n=++this.pendingBackendInitId,s=o.then(a=>n(nthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(o),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),n))}scopedRun(e,t,o){e();try{let n=o();return t(),n}catch(n){throw t(),n}}nextTensorId(){return r.nextTensorId++}nextVariableId(){return r.nextVariableId++}clone(e){let t=T.runKernel(Co,{x:e}),o={x:e},n=a=>({x:()=>{let i=\"float32\",p={x:a},u={dtype:i};return T.runKernel(yo,p,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[t],n,s,{}),t}runKernel(e,t,o){if(this.backendName==null&&this.backend,!(Xp(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(p=>{s+=p.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=Sw(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(Sw(e)){let{kernelName:f,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=Xp(f,this.backendName);E(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let S=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,S);o=this.saveTensorsForBackwardMode(k)}return S}}else{let{forwardFunc:f}=e,h=g=>{n&&(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=e,m=Sw(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(u,c,t,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:t.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=iw(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?(E(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let p=o.filter((u,c)=>a[c]);return i.concat(p)}return[]}makeTensor(e,t,o,n){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");o=o||\"float32\",n=n||this.backend;let s=e;o===\"string\"&&zo(e[0])&&(s=e.map(p=>Ji(p)));let a=n.write(s,t,o),i=new mt(t,o,a,this.nextTensorId());if(this.trackTensor(i,n),o===\"string\"){let p=this.state.tensorInfo.get(a),u=ow(s);this.state.numBytes+=u-p.bytes,p.bytes=u}return i}makeTensorFromDataId(e,t,o,n){o=o||\"float32\";let s={dataId:e,shape:t,dtype:o};return this.makeTensorFromTensorInfo(s,n)}makeTensorFromTensorInfo(e,t){let{dataId:o,shape:n,dtype:s}=e,a=new mt(n,s,o,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let s=new ri(e,t,o,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let o=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(o=e.size*Wp(e.dtype)),this.state.numBytes+=o,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:o})),e instanceof ri||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let o=e.size*Wp(e.dtype);this.state.numBytes-=o}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,o=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,o,n,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:o,saved:s},p=iw(e);p!=null&&(n=p.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,l)=>{if(c==null){let m=o[l],d=Gp(m.size,m.dtype);return this.makeTensor(d,m.shape,m.dtype)}return c}),n(u.length>1?u:u[0],s,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Cl(e),o=new Set(t.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(e,t,o,n=!1){if(E(t.length>0,()=>\"gradients() received an empty list of xs.\"),o!=null&&o.dtype!==\"float32\")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",e));E(s instanceof mt,()=>\"The result y returned by f() must be a tensor.\");let a=q0(this.state.activeTape,t,s);if(!n&&a.length===0&&t.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");return this.tidy(\"backward\",()=>{let i={};i[s.id]=o==null?x4(s.shape):o,j0(i,a,u=>this.tidy(u),y4);let p=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let c of u.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:p}})}customGrad(e){return E(qs(e),()=>\"The f passed in customGrad(f) must be a function.\"),(...t)=>{E(t.every(i=>i instanceof mt),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let o,n={};t.forEach((i,p)=>{n[p]=i});let s=(i,p)=>(o=e(...t,p),E(o.value instanceof mt,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),E(qs(o.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),o.value),a=(i,p)=>{let u=o.gradFunc(i,p),c=Array.isArray(u)?u:[u];E(c.length===t.length,()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),E(c.every(m=>m instanceof mt),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");let l={};return c.forEach((m,d)=>{l[d]=()=>m}),l};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,inputs:n})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=Mu(),o=await this.backend.time(e);return o.wallMs=Mu()-t,o}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new nd;for(let e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};wl.nextTensorId=0;wl.nextVariableId=0;function x4(r){let e=ml(ze(r),\"float32\");return T.makeTensor(e,r,\"float32\")}function Iw(){let r=aw();if(r._tfengine==null){let e=new dl(r);r._tfengine=new wl(e)}return $0(r._tfengine.ENV),Z0(()=>r._tfengine),r._tfengine}var T=Iw();function y4(r,e){let t={a:r,b:e};return T.runKernel(\"Add\",t)}var eu={};qe(eu,{isBrowser:()=>kw,isMobile:()=>w4,mockIsMobile:()=>C4});function b4(){return typeof navigator!=\"undefined\"&&navigator!=null}var vw;function C4(r){vw=r}function w4(r){if(vw!==void 0)return vw;if(r||b4()){if(r||(r=navigator),r.product===\"ReactNative\")return!0;let e=r.userAgent||r.vendor||(typeof window!=\"undefined\"?window.opera:\"\");if(!e){let t=r;return 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a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),E(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),E(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),nu(a,i,p,c,u,s)}var Xk=N({batchNorm3d_:kH});function NH(r,e,t,o,n,s){let a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),E(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),E(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),nu(a,i,p,c,u,s)}var Yk=N({batchNorm4d_:NH});function TH(r,e,t){let o=v(r,\"x\",\"bincount\"),n=v(e,\"weights\",\"bincount\");E(o.dtype===\"int32\",()=>`Error in bincount: input dtype must be int32, but got ${o.dtype}`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(n.size===o.size||n.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`);let s={x:o,weights:n},a={size:t};return T.runKernel(Jo,s,a)}var hd=N({bincount_:TH});function _H(r,e){let t=v(r,\"x\",\"bitwiseAnd\"),o=v(e,\"y\",\"bitwiseAnd\");if(!br(t.shape,o.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${t.shape}, y: ${o.shape}`);if(t.dtype!==\"int32\"||o.dtype!==\"int32\")throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${t.dtype} and type of y: ${o.dtype}`);let n={a:t,b:o};return T.runKernel(qa,n)}var Qk=N({bitwiseAnd_:_H});function EH(r,e){let t=v(r,\"s0\",\"broadcastArgs\",\"int32\"),o=v(e,\"s1\",\"broadcastArgs\",\"int32\");if(t.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${t.rank}`);if(o.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${o.rank}`);let n={s0:t,s1:o};return T.runKernel(ea,n)}var Zk=N({broadcastArgs_:EH});function $H(r,e){let t=v(r,\"broadcastTo\",\"x\"),o=t.shape;if(Ct(e),e.lengtht.rank){let u=t.shape.slice();for(;u.length=0;u--)if(n[u]===e[u])s[u]=1;else if(t.shape[u]!==1)throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`);if(s.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Ur(t);let i={x:t},p={reps:s};return T.runKernel(po,i,p)}var su=N({broadcastTo_:$H});function RH(r){let t={x:v(r,\"x\",\"ceil\",\"float32\")};return T.runKernel(en,t)}var Jk=N({ceil_:RH});function $a(r,e,t){Ct(r),t=t||Ei(e);let o={shape:r,value:e,dtype:t};return T.runKernel(sa,{},o)}function DH(r,e,t){let o=v(r,\"x\",\"clipByValue\");if(E(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`),e===t)return $a(o.shape,e,o.dtype);let n={x:o},s={clipValueMin:e,clipValueMax:t};return T.runKernel(bo,n,s)}var e2=N({clipByValue_:DH});function AH(r){return yt(r,0)}var t2=N({concat1d_:AH});function FH(r,e){return yt(r,e)}var r2=N({concat2d_:FH});function PH(r,e){return yt(r,e)}var o2=N({concat3d_:PH});function OH(r,e){return yt(r,e)}var n2=N({concat4d_:OH});function MH(r,e,t,o,n=\"NHWC\",s=[1,1],a){let i=v(r,\"x\",\"conv2d\",\"float32\"),p=v(e,\"filter\",\"conv2d\",\"float32\"),u=i,c=!1;i.rank===3&&(c=!0,u=W(i,[1,i.shape[0],i.shape[1],i.shape[2]])),E(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),E(p.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`),Lt(\"conv2d\",o,a);let l=n===\"NHWC\"?u.shape[3]:u.shape[1];E(l===p.shape[2],()=>`Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`),E(gr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),E(Ta(s),()=>\"Error in conv2D: Dilated rates should be larger than 0.\"),E(Ta(t),()=>\"Error in conv2D: Strides should be larger than 0.\");let m={x:u,filter:p},d={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=T.runKernel(tn,m,d);return c?W(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var au=N({conv2d_:MH});function LH(r,e,t,o,n=\"NWC\",s=1,a){let i=v(r,\"x\",\"conv1d\"),p=v(e,\"filter\",\"conv1d\"),u=i,c=!1;i.rank===2&&(c=!0,u=W(i,[1,i.shape[0],i.shape[1]])),E(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),E(p.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${p.rank}.`),Lt(\"conv1d\",o,a),E(u.shape[2]===p.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${p.shape[1]}.`),E(gr(t,s),()=>`Error in conv1D: Either stride or dilation must be 1. 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Got strides ${t} and dilations '${s}'`),E(n===\"NDHWC\",()=>`Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`),E(Ta(s),()=>\"Error in conv3D: Dilated rates should be larger than 0.\"),E(Ta(t),()=>\"Error in conv3D: Strides should be larger than 0.\");let c={x:p,filter:i},l={strides:t,pad:o,dataFormat:n,dilations:s},m=T.runKernel(on,c,l);return u?W(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var i2=N({conv3d_:VH});function WH(r,e,t,o,n){E(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let s=r,a=e,i=!1;e.rank===4&&(i=!0,a=W(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]),s=[1,r[0],r[1],r[2],r[3]]);let p=s[4],u=a.shape[4];E(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),E(a.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`),E(t.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${t.rank}`),E(p===t.shape[3],()=>`Error in conv3dDerInput: depth of input (${p}) must match input depth for filter ${t.shape[3]}.`),E(u===t.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t.shape[4]}.`);let c={dy:a,filter:t},l={pad:n,strides:o,inputShape:s},m=T.runKernel(nn,c,l);return i?W(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var u2=N({conv3DBackpropInput_:WH});function UH(r,e,t,o,n){let s=v(r,\"x\",\"conv3dTranspose\"),a=v(e,\"filter\",\"conv3dTranspose\");return u2(t,s,a,o,n)}var p2=N({conv3dTranspose_:UH});function GH(r){let t={x:v(r,\"x\",\"cos\",\"float32\")};return T.runKernel(\"Cos\",t)}var c2=N({cos_:GH});function HH(r){let t={x:v(r,\"x\",\"cosh\",\"float32\")};return T.runKernel(an,t)}var l2=N({cosh_:HH});function KH(r,e=0,t=!1,o=!1){let s={x:v(r,\"x\",\"cumprod\")},a={axis:e,exclusive:t,reverse:o};return T.runKernel(un,s,a)}var m2=N({cumprod_:KH});function qH(r,e=0,t=!1,o=!1){let s={x:v(r,\"x\",\"cumsum\")},a={axis:e,exclusive:t,reverse:o};return T.runKernel(pn,s,a)}var d2=N({cumsum_:qH});function jH(r,e,t,o=!1){let n=v(r,\"x\",\"denseBincount\"),s=v(e,\"weights\",\"denseBincount\");E(n.dtype===\"int32\",()=>`Error in denseBincount: input dtype must be int32, but got ${n.dtype}`),E(n.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(s.size===n.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`);let a={x:n,weights:s},i={size:t,binaryOutput:o};return T.runKernel(ra,a,i)}var f2=N({denseBincount_:jH});function XH(r,e,t=\"NHWC\"){let o=v(r,\"x\",\"depthToSpace\",\"float32\"),n=t===\"NHWC\"?o.shape[1]:o.shape[2],s=t===\"NHWC\"?o.shape[2]:o.shape[3],a=t===\"NHWC\"?o.shape[3]:o.shape[1];E(e>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${e}`),E(n*e>=0,()=>`Negative dimension size caused by overflow when multiplying\n ${n} and ${e} for depthToSpace with input shape\n ${o.shape}`),E(s*e>=0,()=>`Negative dimension size caused by overflow when multiplying\n ${s} and ${e} for depthToSpace with input shape\n ${o.shape}`),E(a%(e*e)===0,()=>`Dimension size must be evenly divisible by ${e*e} but is ${a} for depthToSpace with input shape ${o.shape}`);let i={x:o},p={blockSize:e,dataFormat:t};return T.runKernel(ln,i,p)}var h2=N({depthToSpace_:XH});function YH(r,e,t,o,n=\"NHWC\",s=[1,1],a){let i=v(r,\"x\",\"depthwiseConv2d\",\"float32\"),p=v(e,\"filter\",\"depthwiseConv2d\",\"float32\"),u=i,c=!1;i.rank===3&&(c=!0,u=W(i,[1,i.shape[0],i.shape[1],i.shape[2]])),E(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),E(p.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`);let l=n===\"NHWC\"?u.shape[3]:u.shape[1];E(l===p.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${l}) must match the inChannels dimension in filter ${p.shape[2]}.`),Lt(\"depthwiseConv2d\",o,a);let m={x:u,filter:p},d={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=T.runKernel(mn,m,d);return c?W(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var sc=N({depthwiseConv2d_:YH});function QH(r){let t={x:v(r,\"x\",\"diag\")};return T.runKernel(oa,t)}var g2=N({diag_:QH});function ZH(r,e,t,o,n=[1,1],s=\"NHWC\"){let a=v(r,\"x\",\"dilation2d\"),i=v(e,\"filter\",\"dilation2d\");E(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),E(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),E(s===\"NHWC\",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let p=a,u=!1;a.rank===3&&(p=W(a,[1,a.shape[0],a.shape[1],a.shape[2]]),u=!0),E(p.shape[3]===i.shape[2],()=>`Error in dilation2d: input and filter must have the same depth: ${p.shape[3]} vs ${i.shape[2]}`);let c={x:p,filter:i},l={strides:t,pad:o,dilations:n},m=T.runKernel(dn,c,l);return u?W(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var x2=N({dilation2d_:ZH});var Sr={};qe(Sr,{assertAndGetBroadcastShape:()=>rt,getBroadcastDims:()=>y2,getReductionAxes:()=>xd});function y2(r,e){let t=r.length,o=[];for(let n=0;n1&&a===1&&o.unshift(s)}return o}function xd(r,e){let t=[];for(let o=0;o1)&&t.unshift(s)}return t}function rt(r,e){let t=Math.max(r.length,e.length),o=new Array(t);for(let n=0;n`Error in dot: inputs must all be rank 1 or 2, but got ranks ${t.rank} and ${o.rank}.`);let n=t.rank===1?t.size:t.shape[1],s=o.rank===1?o.size:o.shape[0];if(E(n===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${n} and ${s}.`),t.rank===1&&o.rank===1){let a=W(t,[1,-1]),i=W(o,[-1,1]),p=Ze(a,i);return W(p,[])}else if(t.rank===1&&o.rank===2){let a=W(t,[1,-1]),i=W(o,[o.shape[0],o.shape[1]]),p=Ze(a,i);return W(p,[p.size])}else if(t.rank===2&&o.rank===1){let 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Eo(r,e,t,o,n,s){o==null&&(o=.5),n==null&&(n=Number.NEGATIVE_INFINITY),s==null&&(s=0);let a=r.shape[0];return t=Math.min(t,a),E(0<=o&&o<=1,()=>`iouThreshold must be in [0, 1], but was '${o}'`),E(r.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${r.rank}'`),E(r.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`),E(e.rank===1,()=>\"scores must be a 1D tensor\"),E(e.shape[0]===a,()=>`scores has incompatible shape with boxes. 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g=0;gn&&u.push({score:e[g],boxIndex:g,suppressBeginIndex:0});u.sort(yN);let c=s>0?-.5/s:0,l=[],m=[];for(;l.length0;){let g=u.pop(),{score:x,boxIndex:b,suppressBeginIndex:C}=g;if(x=C;--k){let _=yj(r,b,l[k]);if(_>=o){S=!0;break}if(g.score=g.score*bj(o,c,_),g.score<=n)break}g.suppressBeginIndex=l.length,S||(g.score===x?(l.push(b),m.push(g.score)):g.score>n&&xN(u,g,yN))}let d=l.length,f=t-d;i&&f>0&&(l.push(...new Array(f).fill(0)),m.push(...new Array(f).fill(0)));let h={selectedIndices:l};return a&&(h.selectedScores=m),p&&(h.validOutputs=d),h}function yj(r,e,t){let o=r.subarray(e*4,e*4+4),n=r.subarray(t*4,t*4+4),s=Math.min(o[0],o[2]),a=Math.min(o[1],o[3]),i=Math.max(o[0],o[2]),p=Math.max(o[1],o[3]),u=Math.min(n[0],n[2]),c=Math.min(n[1],n[3]),l=Math.max(n[0],n[2]),m=Math.max(n[1],n[3]),d=(i-s)*(p-a),f=(l-u)*(m-c);if(d<=0||f<=0)return 0;let h=Math.max(s,u),g=Math.max(a,c),x=Math.min(i,l),b=Math.min(p,m),C=Math.max(x-h,0)*Math.max(b-g,0);return C/(d+f-C)}function bj(r,e,t){let o=Math.exp(e*t*t);return t<=r?o:0}function yN(r,e){return r.score-e.score||r.score===e.score&&e.boxIndex-r.boxIndex}async function Cj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY){let s=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),a=v(e,\"scores\",\"nonMaxSuppressionAsync\"),i=Eo(s,a,t,o,n);t=i.maxOutputSize,o=i.iouThreshold,n=i.scoreThreshold;let p=await Promise.all([s.data(),a.data()]),u=p[0],c=p[1],{selectedIndices:l}=Jd(u,c,t,o,n);return s!==r&&s.dispose(),a!==e&&a.dispose(),Jt(l,\"int32\")}var bN=Cj;function wj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=0){let a=v(r,\"boxes\",\"nonMaxSuppression\"),i=v(e,\"scores\",\"nonMaxSuppression\"),p=Eo(a,i,t,o,n,s);t=p.maxOutputSize,o=p.iouThreshold,n=p.scoreThreshold,s=p.softNmsSigma;let u={boxes:a,scores:i},c={maxOutputSize:t,iouThreshold:o,scoreThreshold:n,softNmsSigma:s},l=T.runKernel(Zn,u,c);return{selectedIndices:l[0],selectedScores:l[1]}}var CN=N({nonMaxSuppressionWithScore_:wj});async function Sj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=0){let a=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),i=v(e,\"scores\",\"nonMaxSuppressionAsync\"),p=Eo(a,i,t,o,n,s);t=p.maxOutputSize,o=p.iouThreshold,n=p.scoreThreshold,s=p.softNmsSigma;let u=await Promise.all([a.data(),i.data()]),c=u[0],l=u[1],{selectedIndices:m,selectedScores:d}=tf(c,l,t,o,n,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:Jt(m,\"int32\"),selectedScores:Jt(d)}}var wN=Sj;function Ij(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=v(r,\"boxes\",\"nonMaxSuppression\"),i=v(e,\"scores\",\"nonMaxSuppression\"),p=Eo(a,i,t,o,n,null),u=p.maxOutputSize,c=p.iouThreshold,l=p.scoreThreshold,m={boxes:a,scores:i},d={maxOutputSize:u,iouThreshold:c,scoreThreshold:l,padToMaxOutputSize:s},f=T.runKernel(Qa,m,d);return{selectedIndices:f[0],validOutputs:f[1]}}var SN=N({nonMaxSuppressionPadded_:Ij});async function vj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),i=v(e,\"scores\",\"nonMaxSuppressionAsync\"),p=Eo(a,i,t,o,n,null),u=p.maxOutputSize,c=p.iouThreshold,l=p.scoreThreshold,[m,d]=await Promise.all([a.data(),i.data()]),{selectedIndices:f,validOutputs:h}=ef(m,d,u,c,l,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:Jt(f,\"int32\"),validOutputs:ke(h,\"int32\")}}var IN=vj;function kj(r,e,t=!1,o=!1){let n=v(r,\"images\",\"resizeBilinear\");E(n.rank===3||n.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`),E(e.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${e}.`),E(o===!1||t===!1,()=>\"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.\");let s=n,a=!1;n.rank===3&&(a=!0,s=W(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},p={alignCorners:t,halfPixelCenters:o,size:e},u=T.runKernel(is,i,p);return a?W(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var vN=N({resizeBilinear_:kj});function Nj(r,e,t=!1,o=!1){let n=v(r,\"images\",\"resizeNearestNeighbor\");E(n.rank===3||n.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`),E(e.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`),E(n.dtype===\"float32\"||n.dtype===\"int32\",()=>\"`images` must have `int32` or `float32` as dtype\"),E(o===!1||t===!1,()=>\"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.\");let s=n,a=!1;n.rank===3&&(a=!0,s=W(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},p={alignCorners:t,halfPixelCenters:o,size:e},u=T.runKernel(as,i,p);return a?W(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var kN=N({resizeNearestNeighbor_:Nj});function Tj(r,e=\"binary\",t=!1,o=.5){let n=v(r,\"image\",\"threshold\"),s=.2989,a=.587,i=.114,p=n.shape[0]*n.shape[1],u=se(Jt([o]),255),c,l,m,d;if(E(n.rank===3,()=>`Error in threshold: image must be rank 3,but got rank 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p={image:a,transforms:i},u={interpolation:t,fillMode:o,fillValue:n,outputShape:s};return T.runKernel(Rs,p,u)}var TN=N({transform_:Ej});function $j(r,e,t){let o=v(r,\"a\",\"bandPart\");E(o.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${o.rank}.`);let n=o.shape,[s,a]=o.shape.slice(-2),i,p;typeof e==\"number\"?(E(e%1===0,()=>`bandPart(): numLower must be an integer, got ${e}.`),E(e<=s,()=>`bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`),i=v(e<0?s:e,\"numLower\",\"bandPart\")):(E(e.dtype===\"int32\",()=>\"bandPart(): numLower's dtype must be an int32.\"),i=lo(Tl(e,0),s,Hu(e,s))),typeof t==\"number\"?(E(t%1===0,()=>`bandPart(): numUpper must be an integer, got ${t}.`),E(t<=a,()=>`bandPart(): numUpper (${t}) must not be greater than the number of columns (${a}).`),p=v(t<0?a:t,\"numUpper\",\"bandPart\")):(E(t.dtype===\"int32\",()=>\"bandPart(): numUpper's dtype must be an int32.\"),p=lo(Tl(t,0),a,Hu(t,a)));let u=W(cu(0,s,1,\"int32\"),[-1,1]),c=cu(0,a,1,\"int32\"),l=Te(u,c),m=Uu(ac(l,i),Id(l,pr(p))),d=Gr([s,a],o.dtype);return W(vr(fo(W(o,[-1,s,a])).map(f=>lo(m,f,d))),n)}var _N=N({bandPart_:$j});function Rj(r){let e;if(Array.isArray(r)){e=!1,E(r!=null&&r.length>0,()=>\"Gram-Schmidt process: input must not be null, undefined, or empty\");let n=r[0].shape[0];for(let s=1;s`Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${n})`)}else e=!0,r=li(r,r.shape[0],0).map(n=>cc(n,[0]));E(r.length<=r[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);let t=[],o=r;for(let n=0;n{let s=o[n];if(n>0)for(let a=0;a=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`),r.rank===2)return $N(r,e);{let t=r.shape.slice(0,r.shape.length-2).reduce((p,u)=>p*u),o=fo(W(r,[t,r.shape[r.shape.length-2],r.shape[r.shape.length-1]]),0),n=[],s=[];o.forEach(p=>{let[u,c]=$N(p,e);n.push(u),s.push(c)});let a=W(vr(n,0),r.shape),i=W(vr(s,0),r.shape);return[a,i]}}function $N(r,e=!1){return T.tidy(()=>{E(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let t=r.shape[0],o=r.shape[1],n=Cd(t),s=Ur(r),a=mu([[1]],[1,1]),i=Ur(a),p=t>=o?o:t;for(let u=0;u{let d=Xe(s,[u,u],[t-u,1]),f=Vu(d),h=Xe(s,[u,u],[1,1]),g=lo(Wu(h,0),mu([[-1]]),mu([[1]])),x=Te(h,se(g,f)),b=je(d,x);b.shape[0]===1?i=Ur(a):i=yt([a,Xe(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let C=pr(je(Ze(g,x),f)),S=Xe(s,[u,0],[t-u,o]),k=se(C,i),_=mc(i);if(u===0)s=Te(S,Ze(k,Ze(_,S)));else{let D=Te(S,Ze(k,Ze(_,S)));s=yt([Xe(s,[0,0],[u,o]),D],0)}let $=mc(k),R=Xe(n,[0,u],[t,n.shape[1]-u]);if(u===0)n=Te(R,Ze(Ze(R,i),$));else{let D=Te(R,Ze(Ze(R,i),$));n=yt([Xe(n,[0,0],[t,u]),D],1)}return[i,s,n]}),Ot([c,l,m])}return!e&&t>o&&(n=Xe(n,[0,0],[t,o]),s=Xe(s,[0,0],[o,o])),[n,s]})}var RN=N({qr_:Dj});var $t;(function(r){r[r.NONE=0]=\"NONE\",r[r.MEAN=1]=\"MEAN\",r[r.SUM=2]=\"SUM\",r[r.SUM_BY_NONZERO_WEIGHTS=3]=\"SUM_BY_NONZERO_WEIGHTS\"})($t||($t={}));function Aj(r,e,t=$t.SUM_BY_NONZERO_WEIGHTS){let o=v(r,\"losses\",\"computeWeightedLoss\"),n=null;e!=null&&(n=v(e,\"weights\",\"computeWeightedLoss\"));let s=n==null?o:se(o,n);if(t===$t.NONE)return s;if(t===$t.SUM)return ot(s);if(t===$t.MEAN){if(n==null)return Gu(s);{let a=o.size/n.size,i=je(ot(s),ot(n));return a>1?je(i,ke(a)):i}}if(t===$t.SUM_BY_NONZERO_WEIGHTS){if(n==null)return je(ot(s),ke(o.size));{let a=se(n,Da(o.shape)),i=Ue(ot(Fd(a,ke(0))),\"float32\");return je(ot(s),i)}}throw Error(`Unknown reduction: ${t}`)}var cr=N({computeWeightedLoss_:Aj});function Fj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"absoluteDifference\"),s=v(e,\"predictions\",\"absoluteDifference\"),a=null;t!=null&&(a=v(t,\"weights\",\"absoluteDifference\")),xt(n.shape,s.shape,\"Error in absoluteDifference: \");let i=Qt(Te(n,s));return cr(i,a,o)}var DN=N({absoluteDifference_:Fj});function Pj(r,e,t,o,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"cosineDistance\"),a=v(e,\"predictions\",\"cosineDistance\"),i=null;o!=null&&(i=v(o,\"weights\",\"cosineDistance\")),xt(s.shape,a.shape,\"Error in cosineDistance: \");let p=ke(1),u=Te(p,ot(se(s,a),t,!0));return cr(u,i,n)}var AN=N({cosineDistance_:Pj});function Oj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"hingeLoss\"),s=v(e,\"predictions\",\"hingeLoss\"),a=null;t!=null&&(a=v(t,\"weights\",\"hingeLoss\")),xt(n.shape,s.shape,\"Error in hingeLoss: \");let i=ke(1);n=Te(se(ke(2),n),i);let p=lu(Te(i,se(n,s)));return cr(p,a,o)}var FN=N({hingeLoss_:Oj});function Mj(r,e,t,o=1,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"huberLoss\"),a=v(e,\"predictions\",\"huberLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"huberLoss\")),xt(s.shape,a.shape,\"Error in huberLoss: \");let p=ke(o),u=Qt(Te(a,s)),c=Hu(u,p),l=Te(u,c),m=Ce(se(ke(.5),Zt(c)),se(p,l));return cr(m,i,n)}var PN=N({huberLoss_:Mj});function Lj(r,e,t,o=1e-7,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"logLoss\"),a=v(e,\"predictions\",\"logLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"logLoss\")),xt(s.shape,a.shape,\"Error in logLoss: \");let p=ke(1),u=ke(o),c=pr(se(s,pi(Ce(a,u)))),l=se(Te(p,s),pi(Ce(Te(p,a),u))),m=Te(c,l);return cr(m,i,n)}var ON=N({logLoss_:Lj});function Bj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"meanSquaredError\"),s=v(e,\"predictions\",\"meanSquaredError\"),a=null;t!=null&&(a=v(t,\"weights\",\"meanSquaredError\")),xt(n.shape,s.shape,\"Error in meanSquaredError: \");let i=Kd(n,s);return cr(i,a,o)}var MN=N({meanSquaredError_:Bj});function zj(r,e){let t=v(r,\"labels\",\"sigmoidCrossEntropyWithLogits\"),o=v(e,\"logits\",\"sigmoidCrossEntropyWithLogits\");xt(t.shape,o.shape,\"Error in sigmoidCrossEntropyWithLogits: \");let n=lu(o),s=se(o,t),a=kd(_o(pr(Qt(o))));return Ce(Te(n,s),a)}function Vj(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let 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Labels / logits was rank ${e.rank} and dim was ${t}`);return Ir((n,s,a)=>{let p=_d(s,[t],!0),u=Te(Ue(s,\"float32\"),p);a([n,u]);let c=pr(se(u,n));return{value:ot(c,[t]),gradFunc:(d,f)=>{let[h,g]=f,x=ii(d.shape,[t]);return[se(W(d,x),Te(Ue(h,\"float32\"),_o(g))),se(W(d,x),Te(_o(g),Ue(h,\"float32\")))]}}})(r,e)}function Uj(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"onehotLabels\",\"softmaxCrossEntropy\"),a=v(e,\"logits\",\"softmaxCrossEntropy\"),i=null;if(t!=null&&(i=v(t,\"weights\",\"softmaxCrossEntropy\")),xt(s.shape,a.shape,\"Error in softmaxCrossEntropy: \"),o>0){let u=ke(o),c=ke(1),l=ke(s.shape[1]);s=Ce(se(s,Te(c,u)),je(u,l))}let p=Wj(s,a);return cr(p,i,n)}var BN=N({softmaxCrossEntropy_:Uj});function Gj(r,e,t,o){let n=v(r,\"indices\",\"sparseFillEmptyRows\",\"int32\"),s=v(e,\"values\",\"sparseFillEmptyRows\"),a=v(t,\"denseShape\",\"sparseFillEmptyRows\",\"int32\"),i=v(o,\"defaultValue\",\"sparseFillEmptyRows\",s.dtype);if(n.rank!==2)throw new Error(`Indices should be Tensor2D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(a.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let p={indices:n,values:s,denseShape:a,defaultValue:i},u=T.runKernel(Ki,p);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var zN=N({sparseFillEmptyRows_:Gj});function Hj(r,e,t){let o=v(r,\"inputIndices\",\"sparseReshape\",\"int32\"),n=v(e,\"inputShape\",\"sparseReshape\",\"int32\"),s=v(t,\"newShape\",\"sparseReshape\",\"int32\");if(o.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape\n ${o.shape}`);if(n.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let a={inputIndices:o,inputShape:n,newShape:s},i=T.runKernel(ei,a);return{outputIndices:i[0],outputShape:i[1]}}var VN=N({sparseReshape_:Hj});function Kj(r,e,t){let o=v(r,\"data\",\"sparseSegmentMean\"),n=v(e,\"indices\",\"sparseSegmentMean\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentMean\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(ya,a)}var WN=N({sparseSegmentMean_:Kj});function qj(r,e,t){let o=v(r,\"data\",\"sparseSegmentSum\"),n=v(e,\"indices\",\"sparseSegmentSum\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(ba,a)}var UN=N({sparseSegmentSum_:qj});function jj(r,e,t,o,n,s,a,i){let p=v(r,\"data\",\"stringNGrams\",\"string\");if(p.dtype!==\"string\")throw new Error(\"Data must be of datatype string\");if(p.shape.length!==1)throw new Error(`Data must be a vector, saw: ${p.shape}`);let u=v(e,\"dataSplits\",\"stringNGrams\");if(u.dtype!==\"int32\")throw new Error(\"Data splits must be of datatype int32\");let c={separator:t,nGramWidths:o,leftPad:n,rightPad:s,padWidth:a,preserveShortSequences:i},l={data:p,dataSplits:u},m=T.runKernel(Ca,l,c);return{nGrams:m[0],nGramsSplits:m[1]}}var GN=N({stringNGrams_:jj});function Xj(r,e,t=!0){let o=v(r,\"input\",\"stringSplit\",\"string\"),n=v(e,\"delimiter\",\"stringSplit\",\"string\");if(o.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${o.shape}`);if(n.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);let s={skipEmpty:t},a={input:o,delimiter:n},i=T.runKernel(ji,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var HN=N({stringSplit_:Xj});function Yj(r,e){let t=v(r,\"input\",\"stringToHashBucketFast\",\"string\"),o={numBuckets:e};if(e<=0)throw new Error(\"Number of buckets must be at least 1\");let n={input:t};return T.runKernel(Xi,n,o)}var KN=N({stringToHashBucketFast_:Yj});function Qj(r,e,t,o=!0){let n=v(r,\"input\",\"staticRegexReplace\",\"string\"),s={pattern:e,rewrite:t,replaceGlobal:o};return T.runKernel(Ru,{x:n},s)}var qN=N({staticRegexReplace_:Qj});var Zj={fft:uc,ifft:ju,rfft:pc,irfft:Hd},Jj={hammingWindow:pN,hannWindow:Qd,frame:Zd,stft:cN},eX={flipLeftRight:mN,grayscaleToRGB:dN,resizeNearestNeighbor:kN,resizeBilinear:vN,rgbToGrayscale:fN,rotateWithOffset:hN,cropAndResize:lN,nonMaxSuppression:gN,nonMaxSuppressionAsync:bN,nonMaxSuppressionWithScore:CN,nonMaxSuppressionWithScoreAsync:wN,nonMaxSuppressionPadded:SN,nonMaxSuppressionPaddedAsync:IN,threshold:NN,transform:TN},tX={bandPart:_N,gramSchmidt:EN,qr:RN},rX={absoluteDifference:DN,computeWeightedLoss:cr,cosineDistance:AN,hingeLoss:FN,huberLoss:PN,logLoss:ON,meanSquaredError:MN,sigmoidCrossEntropy:LN,softmaxCrossEntropy:BN},oX={sparseFillEmptyRows:zN,sparseReshape:VN,sparseSegmentMean:WN,sparseSegmentSum:UN},nX={stringNGrams:GN,stringSplit:HN,stringToHashBucketFast:KN,staticRegexReplace:qN};var jN={};qe(jN,{Serializable:()=>Rl,SerializationMap:()=>rf,getRegisteredName:()=>aX,registerClass:()=>rS});var sX=new Map,tS=new Map,Rl=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},rf=class r{constructor(){this.classNameMap={}}static getMap(){return r.instance==null&&(r.instance=new r),r.instance}static register(e){r.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function rS(r,e,t){E(r.className!=null,()=>\"Class being registered does not have the static className property defined.\"),E(typeof r.className==\"string\",()=>\"className is required to be a string, but got type \"+typeof r.className),E(r.className.length>0,()=>\"Class being registered has an empty-string as its className, which is disallowed.\"),typeof e==\"undefined\"&&(e=\"Custom\"),typeof t==\"undefined\"&&(t=r.className);let o=t,n=e+\">\"+o;return rf.register(r),sX.set(n,r),tS.set(r,n),r}function aX(r){return tS.has(r)?tS.get(r):r.className}var kr=class extends Rl{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Ot(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Vw(e,t)}dispose(){this.iterations_!=null&&Ot(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:\"iter\",tensor:ke(this.iterations_,\"int32\")}}async getWeights(){throw new Error(\"getWeights() is not implemented for this optimizer yet.\")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(kr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var Ju=class extends kr{static get className(){return\"Adadelta\"}constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:De(()=>Gt(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;De(()=>{let c=Ce(se(p,this.rho),se(Zt(i),1-this.rho)),l=se(je(Rr(Ce(u,this.epsilon)),Rr(Ce(p,this.epsilon))),i),m=Ce(se(u,this.rho),se(Zt(l),1-this.rho));p.assign(c),u.assign(m);let d=Ce(se(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(e=>e.variable)),Ot(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};var ep=class extends kr{static get className(){return\"Adagrad\"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:De(()=>$a(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;De(()=>{let p=Ce(i,Zt(a));i.assign(p);let u=Ce(se(je(a,Rr(Ce(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};var tp=class extends kr{static get className(){return\"Adam\"}constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=Te(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:De(()=>Gt(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Gt(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=Ce(se(l,this.beta2),se(Zt(u),1-this.beta2)),f=je(m,o),h=je(d,n);c.assign(m),l.assign(d);let g=Ce(se(je(f,Ce(Rr(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(se(this.accBeta1,this.beta1)),this.accBeta2.assign(se(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ot(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),De(()=>{this.accBeta1.assign(ui(this.beta1,this.iterations_+1)),this.accBeta2.assign(ui(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};var rp=class extends kr{static get className(){return\"Adamax\"}constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=je(-this.learningRate,Ce(se(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Gt(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Gt(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=se(l,this.beta2),f=Qt(u),h=Ad(d,f);c.assign(m),l.assign(h);let g=Ce(se(je(n,o),je(m,Ce(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Ce(this.iteration,1)),this.accBeta1.assign(se(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(e){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};var mi=class extends kr{static get className(){return\"SGD\"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];De(()=>{let i=Ce(se(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=$r(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};var op=class extends mi{static get className(){return\"Momentum\"}constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:De(()=>Gt(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&De(()=>{let p,u=Ce(se(this.m,a),i);this.useNesterov?p=Ce(se(this.c,Ce(i,se(u,this.m))),s):p=Ce(se(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ot(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};var np=class extends kr{static get className(){return\"RMSProp\"}constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:De(()=>Gt(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;De(()=>{let c=Ce(se(p,this.decay),se(Zt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=Ce(se(l,this.decay),se(i,1-this.decay)),d=je(se(i,this.learningRate),Rr(Te(c,Ce(Zt(m),this.epsilon)))),f=Ce(se(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Te(s,f);s.assign(h)}else{let l=Ce(se(p,this.decay),se(Zt(i),1-this.decay)),m=Ce(se(u,this.momentum),je(se(i,this.learningRate),Rr(Ce(l,this.epsilon))));p.assign(l),u.assign(m);let d=Te(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ot(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ot(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ot(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};var iX=[Ju,ep,tp,rp,op,np,mi];function XN(){for(let r of iX)rS(r)}var 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o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=I(\"numElements\",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case\"TensorListFromTensor\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=$T(o,n,s);return t.addTensorList(a),[a.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id),s=I(\"dtype\",r,e,t),a=I(\"elementShape\",r,e,t);return[n.concat(s,a)]}case\"TensorListPushBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"tensor\",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case\"TensorListPopBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case\"TensorListSplit\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"lengths\",r,e,t),a=AT(o,s,n);return t.addTensorList(a),[a.idTensor]}case\"TensorListLength\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id);return[ke(n.size(),\"int32\")]}case\"TensorListResize\":{let o=I(\"tensorListId\",r,e,t),n=I(\"size\",r,e,t),a=t.getTensorList(o.id).resize(n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function PT(r,e,t){let[o,n]=I(\"fusedOps\",r,e,t),s=o===\"biasadd\",a=!s,i=n===\"prelu\",p=o===\"fusedbatchnorm\",u=I(\"numArgs\",r,e,t);if(s){if(i&&u!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&s&&u!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(p)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",r,e,t),l=Pl(r,e,t),m=I(\"dataFormat\",r,e,t).toUpperCase(),d=I(\"dilations\",r,e,t),[f,h]=I(\"args\",r,e,t);a&&(h=f,f=void 0);let g=I(\"leakyreluAlpha\",r,e,t);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var OT=(r,e,t,o=Je)=>{switch(r.op){case\"Conv1D\":{let n=I(\"stride\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilation\",r,e,t);return[o.conv1d(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,s,a,i)]}case\"Conv2D\":{let n=I(\"strides\",r,e,t),s=Pl(r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PT(r,e,t);return[o.fused.conv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"FusedDepthwiseConv2dNative\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PT(r,e,t);return[o.fused.depthwiseConv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let n=I(\"outputShape\",r,e,t),s=I(\"strides\",r,e,t),a=Pl(r,e,t);return[o.conv2dTranspose(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,[s[1],s[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let n=I(\"strides\",r,e,t),s=Pl(r,e,t),a=I(\"dilations\",r,e,t),i=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthwiseConv2d(I(\"input\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,i,[a[1],a[2]])]}case\"Conv3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv3d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPoolWithArgmax\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t),i=I(\"includeBatchInIndex\",r,e,t),{result:p,indexes:u}=o.maxPoolWithArgmax(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case\"AvgPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"MaxPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"Dilation2D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dilations\",r,e,t),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[i,p],s,[u,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MT=(r,e,t,o=Je)=>{switch(r.op){case\"Fill\":{let n=I(\"shape\",r,e,t),s=I(\"dtype\",r,e,t),a=I(\"value\",r,e,t);return[o.fill(n,a,s)]}case\"LinSpace\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"num\",r,e,t);return[o.linspace(n,s,a)]}case\"Multinomial\":{let n=I(\"logits\",r,e,t),s=I(\"numSamples\",r,e,t),a=I(\"seed\",r,e,t);return[o.multinomial(n,s,a)]}case\"OneHot\":{let n=I(\"indices\",r,e,t),s=I(\"depth\",r,e,t),a=I(\"onValue\",r,e,t),i=I(\"offValue\",r,e,t),p=I(\"dtype\",r,e,t);return[o.oneHot(n,s,a,i,p)]}case\"Ones\":return[o.ones(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"OnesLike\":return[o.onesLike(I(\"x\",r,e,t))];case\"RandomStandardNormal\":return[o.randomStandardNormal(I(\"shape\",r,e,t),I(\"dtype\",r,e,t),I(\"seed\",r,e,t))];case\"RandomUniform\":return[o.randomUniform(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"dtype\",r,e,t))];case\"RandomUniformInt\":return[o.randomUniformInt(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"seed\",r,e,t))];case\"Range\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"step\",r,e,t);return[o.range(n,s,a,I(\"dtype\",r,e,t))]}case\"TruncatedNormal\":{let n=I(\"shape\",r,e,t),s=I(\"mean\",r,e,t),a=I(\"stdDev\",r,e,t),i=I(\"seed\",r,e,t);return[o.truncatedNormal(n,s,a,I(\"dtype\",r,e,t),i)]}case\"Zeros\":return[o.zeros(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"ZerosLike\":return[o.zerosLike(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function OS(r,e,t){let o=I(\"boxes\",r,e,t),n=I(\"scores\",r,e,t),s=I(\"maxOutputSize\",r,e,t),a=I(\"iouThreshold\",r,e,t),i=I(\"scoreThreshold\",r,e,t),p=I(\"softNmsSigma\",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var LT=async(r,e,t,o,n=Je)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=OS(r,e,t),l=await 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n=I(\"x\",r,e,t),s=I(\"k\",r,e,t),a=I(\"sorted\",r,e,t),i=o.topk(n,s,a);return[i.values,i.indices]}case\"UpperBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.upperBound(n,s)]}case\"Unique\":{let n=I(\"x\",r,e,t),s=o.unique(n);return[s.values,s.indices]}case\"UniqueV2\":{let n=I(\"x\",r,e,t),s=I(\"axis\",r,e,t),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var zT=(r,e,t,o=Je)=>{switch(r.op){case\"Const\":return e[r.name];case\"PlaceholderWithDefault\":let n=I(\"default\",r,e,t);return[Bt(r.name,e,t)||n];case\"Placeholder\":return[Bt(r.name,e,t)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=I(\"x\",r,e,t);return[Bs(c)]}case\"IdentityN\":return I(\"x\",r,e,t).map(c=>Bs(c));case\"Snapshot\":let s=I(\"x\",r,e,t);return[Bs(s)];case\"Shape\":return[o.tensor1d(I(\"x\",r,e,t).shape,\"int32\")];case\"ShapeN\":return I(\"x\",r,e,t).map(c=>o.tensor1d(c.shape));case\"Size\":return[o.scalar(I(\"x\",r,e,t).size,\"int32\")];case\"Rank\":return[o.scalar(I(\"x\",r,e,t).rank,\"int32\")];case\"NoOp\":return[o.scalar(1)];case\"Print\":let a=I(\"x\",r,e,t),i=I(\"data\",r,e,t),p=I(\"message\",r,e,t),u=I(\"summarize\",r,e,t);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(p);for(let c=0;ce.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ke(this.size(),\"int32\")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),De(()=>{let n=fo(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I(\"keyDType\",r,e,t),a=I(\"valueDType\",r,e,t),i=new vf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"values\",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"defaultValue\",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let n=I(\"tableHandle\",r,e,t,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var WT=(r,e,t,o=Je)=>{switch(r.op){case\"ResizeBilinear\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case\"ResizeNearestNeighbor\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case\"CropAndResize\":{let n=I(\"image\",r,e,t),s=I(\"boxes\",r,e,t),a=I(\"boxInd\",r,e,t),i=I(\"cropSize\",r,e,t),p=I(\"method\",r,e,t),u=I(\"extrapolationValue\",r,e,t);return[o.image.cropAndResize(n,s,a,i,p,u)]}case\"ImageProjectiveTransformV3\":{let n=I(\"images\",r,e,t),s=I(\"transforms\",r,e,t),a=I(\"outputShape\",r,e,t),i=I(\"fillValue\",r,e,t),p=I(\"interpolation\",r,e,t),u=I(\"fillMode\",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UT=(r,e,t,o=Je)=>{switch(r.op){case\"Equal\":return[o.equal(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"NotEqual\":return[o.notEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Greater\":return[o.greater(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"GreaterEqual\":return[o.greaterEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Less\":return[o.less(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LessEqual\":return[o.lessEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalAnd\":return[o.logicalAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalNot\":return[o.logicalNot(I(\"a\",r,e,t))];case\"LogicalOr\":return[o.logicalOr(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Select\":case\"SelectV2\":return[o.where(I(\"condition\",r,e,t),I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"BitwiseAnd\":return[o.bitwiseAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GT=(r,e,t,o=Je)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[o.matMul(I(\"a\",r,e,t),I(\"b\",r,e,t),I(\"transposeA\",r,e,t),I(\"transposeB\",r,e,t))];case\"Einsum\":return[o.einsum(I(\"equation\",r,e,t),...I(\"tensors\",r,e,t))];case\"Transpose\":return[o.transpose(I(\"x\",r,e,t),I(\"perm\",r,e,t))];case\"_FusedMatMul\":let[n,s]=I(\"fusedOps\",r,e,t),a=n===\"biasadd\",i=s===\"prelu\",p=I(\"numArgs\",r,e,t),u=I(\"leakyreluAlpha\",r,e,t);if(a){if(i&&p!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&p!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,l]=I(\"args\",r,e,t);return[o.fused.matMul({a:I(\"a\",r,e,t),b:I(\"b\",r,e,t),transposeA:I(\"transposeA\",r,e,t),transposeB:I(\"transposeB\",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];case\"MatrixBandPart\":return[o.linalg.bandPart(I(\"a\",r,e,t),I(\"numLower\",r,e,t),I(\"numUpper\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HT=(r,e,t,o=Je)=>{switch(r.op){case\"EuclideanNorm\":return[o.euclideanNorm(I(\"x\",r,e,t),I(\"axis\",r,e,t),I(\"keepDims\",r,e,t))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"FusedBatchNormV3\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"LRN\":return[o.localResponseNormalization(I(\"x\",r,e,t),I(\"radius\",r,e,t),I(\"bias\",r,e,t),I(\"alpha\",r,e,t),I(\"beta\",r,e,t))];case\"Softmax\":return[o.softmax(I(\"x\",r,e,t))];case\"LogSoftmax\":return[o.logSoftmax(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KT=(r,e,t,o=Je)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I(\"paramsNestedSplits\",r,e,t),I(\"paramsDenseValues\",r,e,t),I(\"indices\",r,e,t),I(\"outputRaggedRank\",r,e,t));return n.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I(\"starts\",r,e,t),I(\"limits\",r,e,t),I(\"splits\",r,e,t));return[n,s]}case\"RaggedTensorToTensor\":return[o.raggedTensorToTensor(I(\"shape\",r,e,t),I(\"values\",r,e,t),I(\"defaultValue\",r,e,t),I(\"rowPartitionTensors\",r,e,t),I(\"rowPartitionTypes\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var qT=(r,e,t,o=Je)=>{switch(r.op){case\"Max\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.max(I(\"x\",r,e,t),i,p)]}case\"Mean\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.mean(I(\"x\",r,e,t),i,p)]}case\"Min\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.min(I(\"x\",r,e,t),i,p)]}case\"Sum\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.sum(I(\"x\",r,e,t),i,p)]}case\"All\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.all(I(\"x\",r,e,t),i,p)]}case\"Any\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.any(I(\"x\",r,e,t),i,p)]}case\"ArgMax\":{let i=I(\"axis\",r,e,t);return[o.argMax(I(\"x\",r,e,t),i)]}case\"ArgMin\":{let i=I(\"axis\",r,e,t);return[o.argMin(I(\"x\",r,e,t),i)]}case\"Prod\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.prod(I(\"x\",r,e,t),i,p)]}case\"Cumprod\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumprod(I(\"x\",r,e,t),i,p,u)]}case\"Cumsum\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumsum(I(\"x\",r,e,t),i,p,u)]}case\"Bincount\":let n=I(\"x\",r,e,t),s=I(\"weights\",r,e,t),a=I(\"size\",r,e,t);return[o.bincount(n,s,a)];case\"DenseBincount\":{let i=I(\"x\",r,e,t),p=I(\"weights\",r,e,t),u=I(\"size\",r,e,t),c=I(\"binaryOutput\",r,e,t);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var jT=(r,e,t,o=Je)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let n=I(\"n\",r,e,t),s=I(\"axis\",r,e,t),a=I(\"tensors\",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case\"Gather\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gather(n,o.cast(s,\"int32\"),0)]}case\"GatherV2\":{let n=I(\"axis\",r,e,t),s=I(\"batchDims\",r,e,t),a=I(\"x\",r,e,t),i=I(\"indices\",r,e,t);return[o.gather(a,o.cast(i,\"int32\"),n,s)]}case\"Reverse\":{let n=I(\"dims\",r,e,t),s=[];for(let i=0;i{let n=I(\"axis\",r,e,t),s=I(\"tensors\",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error(\"the input tensors shape does not match\");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case\"Unpack\":{let n=I(\"axis\",r,e,t),s=I(\"tensor\",r,e,t);return o.unstack(s,n)}case\"Tile\":{let n=I(\"reps\",r,e,t);return[o.tile(I(\"x\",r,e,t),n)]}case\"Split\":case\"SplitV\":{let n=I(\"axis\",r,e,t),s=I(\"numOrSizeSplits\",r,e,t),a=I(\"x\",r,e,t);return o.split(a,s,n)}case\"ScatterNd\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"shape\",r,e,t);return[o.scatterND(n,s,a)]}case\"GatherNd\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gatherND(n,s)]}case\"SparseToDense\":{let n=I(\"sparseIndices\",r,e,t),s=I(\"outputShape\",r,e,t),a=I(\"sparseValues\",r,e,t),i=I(\"defaultValue\",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}case\"TensorScatterUpdate\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"tensor\",r,e,t);return[o.tensorScatterUpdate(a,n,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var XT=(r,e,t,o=Je)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I(\"indices\",r,e,t),I(\"values\",r,e,t),I(\"denseShape\",r,e,t),I(\"defaultValue\",r,e,t));return[n,s,a,i]}case\"SparseReshape\":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I(\"inputIndices\",r,e,t),I(\"inputShape\",r,e,t),I(\"newShape\",r,e,t));return[n,s]}case\"SparseSegmentMean\":return[o.sparse.sparseSegmentMean(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];case\"SparseSegmentSum\":return[o.sparse.sparseSegmentSum(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YT=(r,e,t,o=Je)=>{switch(r.op){case\"FFT\":return[o.fft(I(\"x\",r,e,t))];case\"IFFT\":return[o.ifft(I(\"x\",r,e,t))];case\"RFFT\":return[o.rfft(I(\"x\",r,e,t))];case\"IRFFT\":return[o.irfft(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QT=(r,e,t,o=Je)=>{switch(r.op){case\"StaticRegexReplace\":return[o.string.staticRegexReplace(I(\"input\",r,e,t),I(\"pattern\",r,e,t),I(\"rewrite\",r,e,t),I(\"replaceGlobal\",r,e,t))];case\"StringNGrams\":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I(\"data\",r,e,t),I(\"dataSplits\",r,e,t),I(\"separator\",r,e,t),I(\"nGramWidths\",r,e,t),I(\"leftPad\",r,e,t),I(\"rightPad\",r,e,t),I(\"padWidth\",r,e,t),I(\"preserveShortSequences\",r,e,t));return[n,s]}case\"StringSplit\":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I(\"input\",r,e,t),I(\"delimiter\",r,e,t),I(\"skipEmpty\",r,e,t));return[n,s,a]}case\"StringToHashBucketFast\":return[o.string.stringToHashBucketFast(I(\"input\",r,e,t),I(\"numBuckets\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZT=(r,e,t,o=Je)=>{switch(r.op){case\"Cast\":return[o.cast(I(\"x\",r,e,t),I(\"dtype\",r,e,t))];case\"ExpandDims\":{let n=I(\"axis\",r,e,t);return[o.expandDims(I(\"x\",r,e,t),n)]}case\"Squeeze\":{let n=I(\"axis\",r,e,t);return[o.squeeze(I(\"x\",r,e,t),n)]}case\"Reshape\":return[o.reshape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"EnsureShape\":return[o.ensureShape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"MirrorPad\":return[o.mirrorPad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"mode\",r,e,t))];case\"PadV2\":case\"Pad\":return[o.pad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"constantValue\",r,e,t))];case\"SpaceToBatchND\":{let n=I(\"blockShape\",r,e,t),s=I(\"paddings\",r,e,t);return[o.spaceToBatchND(I(\"x\",r,e,t),n,s)]}case\"BatchToSpaceND\":{let n=I(\"blockShape\",r,e,t),s=I(\"crops\",r,e,t);return[o.batchToSpaceND(I(\"x\",r,e,t),n,s)]}case\"DepthToSpace\":{let n=I(\"blockSize\",r,e,t),s=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthToSpace(I(\"x\",r,e,t),n,s)]}case\"BroadcastTo\":return[o.broadcastTo(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"BroadcastArgs\":return[o.broadcastArgs(I(\"s0\",r,e,t),I(\"s1\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function MS(r,e,t,o,n=De){let s=((a,i,p)=>{switch(a.category){case\"arithmetic\":return n(()=>TT(a,i,p));case\"basic_math\":return n(()=>_T(a,i,p));case\"control\":return FT(a,i,p);case\"convolution\":return n(()=>OT(a,i,p));case\"creation\":return n(()=>MT(a,i,p));case\"dynamic\":return LT(a,i,p);case\"evaluation\":return n(()=>BT(a,i,p));case\"image\":return n(()=>WT(a,i,p));case\"graph\":return n(()=>zT(a,i,p));case\"logical\":return n(()=>UT(a,i,p));case\"matrices\":return n(()=>GT(a,i,p));case\"normalization\":return n(()=>HT(a,i,p));case\"ragged\":return n(()=>KT(a,i,p));case\"reduction\":return n(()=>qT(a,i,p));case\"slice_join\":return n(()=>jT(a,i,p));case\"sparse\":return n(()=>XT(a,i,p));case\"spectral\":return n(()=>YT(a,i,p));case\"string\":return n(()=>QT(a,i,p));case\"transformation\":return n(()=>ZT(a,i,p));case\"hash_table\":return VT(a,i,p,o);case\"custom\":let u=pf(a.op);if(u&&u.customExecutor)return u.customExecutor(new wf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var Ml=class{constructor(e={},t={},o={},n={},s){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function LS(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=new Set(Object.keys(r).map(m=>Nr(m)[0]));o=o||[];let c=new Set(o.map(m=>Nr(m.name)[0])),l=[...e];for(;l.length>0;){let m=l.pop();if((fu(m)||A8(m)||F8(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&!u.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function JT(r,e){let{usedNodes:t,inputs:o}=e,n=Object.keys(o).map(g=>Nr(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],a=g=>t.has(typeof g==\"string\"?g:g.name);function i(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let p=i([...n,...r.weights,...s]).filter(a),u=i([...p,...Object.values(r.nodes)]).filter(a),c=new Map(u.map(g=>[g.name,g])),l={};for(let g of u){l[g.name]=l[g.name]||0;for(let x of g.children)a(x)||(l[x.name]=Number.POSITIVE_INFINITY),l[x.name]=(l[x.name]||0)+1}let m=Object.entries(l).filter(([,g])=>g===0).map(([g])=>g),d=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(a))--l[b.name]===0&&(d.push(b.name),m.push(b.name))}let f=d.map(g=>c.get(g)),h=_8(f,p);return E8(h,p),h}function _8(r,e){let t=new Map(r.map(a=>[a.name,a])),o=e.map(a=>a.name),n=new Set(o);for(;o.length>0;){let a=o.pop(),i=t.get(a);for(let p of i.children)!t.has(p.name)||n.has(p.name)||(n.add(p.name),o.push(p.name))}return r.filter(a=>n.has(a.name))}var gc=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function E8(r,e){let t=new Map(r.map((i,p)=>[i.name,p])),o=new Set(e.map(i=>i.name)),n=i=>o.has(typeof i==\"string\"?i:i.name),s=new Set(r.map(i=>i.name)),a=i=>s.has(typeof i==\"string\"?i:i.name);for(let i of r){for(let p of i.children.filter(a)){if(!t.has(p.name))throw new gc(`Child ${p.name} of node ${i.name} is unreachable.`);if(t.get(i.name)>t.get(p.name))throw new gc(`Node ${i.name} is scheduled to run after its child ${p.name}.`)}if(!n(i))for(let p of i.inputs){if(!t.has(p.name))throw new gc(`Input ${p.name} of node ${i.name} is unreachable.`);if(t.get(p.name)>t.get(i.name))throw new gc(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function e_(r){let e=new Map(r.map((i,p)=>[i.name,p])),t=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>fu(i)?t:p),n=i=>{let p=o[e.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,c)=>Math.max(u,c),o[p])),a=new Map;for(let i=0;ie[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new r(e.functions[o],this)})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPARATOR)+\"--\"+n.join(this.SEPARATOR)}compile(e,t){let o=LS(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let u=t.map(l=>l.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`)}let i=JT(this.graph,o),p=e_(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return $r(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(m=>this.graph.nodes[Nr(m)[0]]),s=t.map(m=>Nr(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(e,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=A().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},l={};return De(()=>{let m=new Ml(this.weightMap,c,l,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(x=>{let[b,C]=Nr(x,m),S=[];S[C]=e[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=MS(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);d[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,d,m,f,a,g.get(x.name))}return this.parent==null&&m.dispose(f),t.map(x=>Bt(x,d,m))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){if(!(fu(t)||a.has(e))){for(let p of o[e])p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length);for(let p of t.inputs){if(fu(p))continue;let u=hS(p.name,o,n);if(u!=null)for(let c of u){if(!c||c.kept||s.has(c.id))continue;let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,o,n,s,a){function i(p){return fu(p)||s.has(p.name)}if(!(fu(e)||a==null))for(let p of a){if(i(p))continue;let u=hS(p.name,t,o);for(let c of u)!c||c.kept||n.has(c.id)||c.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=A().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new Ml(this.weightMap,n,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Bt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(S=>this.graph.nodes[Nr(S)[0]]),i=o.map(S=>Nr(S)[0]),p=new Set(i),u=i.map(S=>this.graph.nodes[S]);u.length===0&&(u=this._outputs);let{usedNodes:c,missingInputs:l,dynamicNode:m,syncInputs:d}=LS(e,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(S=>({node:S,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(S=>{let[k,_]=Nr(S),$=[];$[_]=e[S],h[k]=$});let g={},x=this.getFrozenTensorIds(h),b={};for(;f.length>0;){let S=this.processStack(a,f,t,h,b,x,p,g,c);await Promise.all(S)}m==null&&!n&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let C=u.filter(S=>!fu(S)&&!Bt(S.name,h,t)).map(S=>S.name);if(C.length>0){let S=\"\";throw m!=null&&(S=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${C}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`)}return h}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m=\"\";if(l.node.op===\"Enter\"&&I(\"isConstant\",l.node,n,o)&&([m]=Ls(l.node.name,o)),n[l.node.name]==null){let d=MS(l.node,n,o,this._resourceManager);m||([m]=Ls(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=Ls(i.name,o);s[p]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Nr(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Nr(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Nr(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var kf=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let o=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=n,this.version=`${o.versions.producer}.${o.versions.minConsumer}`,this.executor=new Ll(Ol.Instance.transformGraph(o,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=Ol.Instance.transformGraph(e.modelInitializer);this.initializer=new Ll(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e==\"string\"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[0]}if(e.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof mt?[e]:e,o={};return t.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return e}predict(e,t){let o=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(e,t){let o=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(e){var t;if(!(e instanceof mt)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let 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this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,o=Object.keys(t);for(let n=0;n1?o:o[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return 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YY=Ie(an,r=>Math.cosh(r)),NE={kernelName:an,backendName:\"cpu\",kernelFunc:YY};function QY(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=me([f,h,g,d],\"float32\"),b=t.data.get(s.dataId).values,C=t.data.get(a.dataId).values,S=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let $=0;$=c)continue;let B=h>1?(O-D)*(l-1)/(h-1):0,z=g>1?(M-P)*(m-1)/(g-1):0;for(let U=0;U1?D*(l-1)+U*B:.5*(D+O)*(l-1);if(j<0||j>l-1){for(let q=0;q1?P*(m-1)+re*z:.5*(P+M)*(m-1);if(ne<0||ne>m-1){for(let le=0;le1?P*(m-1)+q*z:.5*(P+M)*(m-1);if(Y<0||Y>m-1){for(let ne=0;nex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. 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c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=t.bufferSync(s),m=aE(l,c),d=c.strideDepth,f=c.strideHeight,h=c.strideWidth,g=c.dilationDepth,x=c.dilationHeight,b=c.dilationWidth,C=c.effectiveFilterDepth,S=c.effectiveFilterHeight,k=c.effectiveFilterWidth,_=C-1-c.padInfo.front,$=k-1-c.padInfo.left,R=S-1-c.padInfo.top,D=me(s.shape,\"float32\"),P=t.bufferSync(n);for(let O=0;O=c.outDepth||Math.floor(re)!==re))for(let ne=0;ne=c.outHeight||Math.floor(ee)!==ee))for(let oe=0;oe=c.outWidth||Math.floor(ie)!==ie)continue;let le=C*S*k-1-m.get(O,re,ee,ie,M),be=J*S*k+ne*k+oe,_e=le===be?1:0;if(_e===0)continue;let ve=P.get(O,re,ee,ie,M);Y+=ve*_e}}}D.set(Y,O,L,B,z,M)}return t.makeTensorInfo(D.shape,D.dtype,D.values)}var m$={kernelName:Gi,backendName:\"cpu\",kernelFunc:zQ};function 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h$={kernelName:ua,backendName:\"cpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;Q(o,\"MaxPoolWithArgmax\");let u=p.data.get(o.dataId).values,c=w.computePool2DInfo(o.shape,n,s,[1,1],a),[l,m]=f$(u,o.shape,o.dtype,i,c),d=p.write(l,c.outShape,o.dtype),f=p.write(m,c.outShape,o.dtype);return[{dataId:d,shape:c.outShape,dtype:o.dtype},{dataId:f,shape:c.outShape,dtype:\"int32\"}]}};function WQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=y.parseAxisParam(s,n.shape),u=w.computeOutAndReduceShapes(n.shape,i)[1],c=y.sizeFromShape(u),l=[],m=t.makeTensorInfo([],\"float32\",new Float32Array([c]));l.push(m);let d=Ro({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}});l.push(d);let f=Ul({inputs:{a:d,b:m},backend:t});l.push(f);let h=fi({inputs:{x:f},backend:t,attrs:{axis:s,keepDims:a}});return l.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}var g$={kernelName:Un,backendName:\"cpu\",kernelFunc:WQ};function 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Shape ${i} and ${p} must match`)})}function OR(r,e,t,o,n){e.program.enableShapeUniforms||(FR(e.inShapeInfos,t),FR([e.outShapeInfo],[o]));let s=o.texData.texture,a=o.texData.texShape;o.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,a[0],a[1]):r.setOutputMatrixTexture(s.texture,a[0],a[1]),r.setProgram(e.webGLProgram),r.bindVertexArray(e.webGLProgram.vao),A().getNumber(\"WEBGL_VERSION\")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,1/0),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN);for(let p=0;p{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!a.isUniform){let p=a.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:l}=Zf(r.packedInputs,a.shape,p),m=\"\",d=\"\",f=\"\";if(c.length===1&&r.packedInputs){let k=[Math.ceil(p[0]/2),Math.ceil(p[1]/2)];m=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!r.packedInputs)d=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let k=y.computeStrides(c);f=`${k[0]===p[1]}_${k[k.length-1]===p[1]}`}let h=a.shape.length,g=c.length===2&&y.arraysEqual(a.shape,p),x=y.sizeFromShape(a.shape)===1,b=w.getBroadcastDims(a.shape,t.shape),C=!r.packedInputs&&h===t.shape.length&&y.arraysEqual(p,t.texData.texShape),S=r.packedInputs||c.length>2?\"\":`${p[0]>1}_${p[1]>1}`;o+=`${h}_${C}_${u?l:\"\"}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${S}_${i}`}else{let p=a.isUniform?\"uniform\":a.texData.texShape;o+=`${a.shape}_${p}_${i}`}});let n=r.userCode,s=r.constructor.name;return s+=\"_\"+o+\"_\"+n+`${A().getNumber(\"WEBGL_VERSION\")}`,s}function ut(r){return A().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&r<=4}var Jf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=gu.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=It();this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n 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texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${t.output} = result;\n }\n `}};var th=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=mr.DOWNLOAD;let t=It();this.outputShape=e,this.userCode=`\n ${Qf}\n\n void main() {\n float x = getAAtOutCoords();\n ${t.output} = encode_float(x);\n }\n `}};var rh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=mr.DOWNLOAD;let t=It();this.outputShape=e,this.userCode=`\n ${Qf}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${t.output} = encode_float(x);\n }\n `}};var 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i=0;inv,createBufferFromOutputTexture:()=>iv,createFloat16MatrixTexture:()=>ev,createFloat16PackedMatrixTexture:()=>ov,createFloat32MatrixTexture:()=>JI,createIndexBuffer:()=>ZI,createPackedMatrixTexture:()=>rv,createUnsignedBytesMatrixTexture:()=>tv,createVertexBuffer:()=>QI,createVertexShader:()=>YI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>pv,downloadFloat32MatrixFromBuffer:()=>uv,downloadMatrixFromPackedOutputTexture:()=>lv,downloadPackedMatrixFromBuffer:()=>cv,getInternalFormatForFloat16MatrixTexture:()=>sh,getInternalFormatForFloat16PackedMatrixTexture:()=>uh,getInternalFormatForFloat32MatrixTexture:()=>nh,getInternalFormatForPackedMatrixTexture:()=>ih,getInternalFormatForUnsignedBytesMatrixTexture:()=>ah,uploadDenseMatrixToTexture:()=>sv,uploadPixelDataToTexture:()=>av});function YI(r){let e=It(),t=`${e.version}\n precision highp float;\n ${e.attribute} vec3 clipSpacePos;\n ${e.attribute} vec2 uv;\n ${e.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = 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o=\"WEBGL_color_buffer_float\",n=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),A().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=Nc(this.gl,s),qr(this.gl,a))this.textureHalfFloatExtension=Nc(this.gl,a);else if(A().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(o),qr(this.gl,n))this.colorBufferHalfFloatExtension=Nc(this.gl,n);else if(A().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(o=\"EXT_color_buffer_float\",qr(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(qr(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=QI(this.gl),this.indexBuffer=ZI(this.gl),this.framebuffer=LI(this.gl),this.textureConfig=Xl(this.gl,this.textureHalfFloatExtension)}get debug(){return A().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. 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t=this.gl;ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),nv(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Yl(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?BI(this.gl,e,t):zI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),VI(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Ma(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&Yl(this.gl,this.program),Tc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,\"VAO changed between setProgram and 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this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=JZ(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let o;\"setTimeoutCustom\"in A().platform&&(o=A().platform.setTimeoutCustom.bind(A().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Ql(this.gl,e,this.framebuffer),this.debug&&Tc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Ql(this.gl,this.outputTexture,this.framebuffer),this.debug&&Tc(this.gl)):Xf(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;Ql(n,e,this.framebuffer),this.debug&&Tc(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,o)),ce(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,o,n))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function JZ(r){let e=0;for(;e`${r}.${t}`)}function Rt(r,e){return e===1?[r]:dv(r,e)}function ED(r,e){if(r===1)return\"rc\";let t=\"\";for(let o=0;o ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let o=this.rank-2;o= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o= ${o};\n bool rEdge = rp1 >= ${n};\n `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n cEdge ? 0. : getA(${t[1]}),\n rEdge ? 0. : getA(${t[2]}),\n rEdge || cEdge ? 0. : getA(${t[3]})`}};var Mc=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let o=\"\";for(let n=0;n<4;n++){let s=\"thisRC = rc;\";n%2===1&&(s+=\"thisRC.z += 1;\"),n>1&&(s+=\"thisRC.y += 1;\"),o+=`\n ${s}\n ${n>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${n}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${n>0?\"}\":\"\"}\n `}this.userCode=`\n ${e9(t,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?Rc():$c(e)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n ${o}\n\n setOutput(result);\n }\n `}};function e9(r,e){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${e?ER([\"r\",\"c\",\"d\"],\"inputShape\"):Ws([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var dh=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,o){let n=RD(t,o),s=DD(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=$D(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].pop();return this.usedTextures[s].push(p),p}let i;return n===er.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===er.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===er.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===er.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===er.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=RD(o,n),a=DD(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=$D(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=A().getNumber(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u&&u.indexOf(e);if(c==null||c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u[c]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function t9(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function $D(r,e,t,o,n){let s=r9(e,o),a;if(n){let[p,u]=Ma(r[0],r[1]);a=p*u}else{let[p,u]=gp(r[0],r[1]);a=p*u}let i=t9(t,s);return a*i}function r9(r,e){switch(r){case er.PACKED_2X2_FLOAT32:return ih(e);case er.PACKED_2X2_FLOAT16:return uh(e);case er.UNPACKED_FLOAT32:return nh(e);case er.UNPACKED_FLOAT16:return sh(e);case er.PACKED_4X1_UNSIGNED_BYTE:return ah(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function o9(r){return A().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?er.PACKED_2X2_FLOAT32:er.UNPACKED_FLOAT32:r?er.PACKED_2X2_FLOAT16:er.UNPACKED_FLOAT16}function RD(r,e){if(r===mr.UPLOAD)return er.PACKED_2X2_FLOAT32;if(r===mr.RENDER||r==null)return o9(e);if(r===mr.DOWNLOAD||r===mr.PIXELS)return er.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function DD(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var tr=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${t}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},Wt=\"if (isnan(x)) return x;\",AD=\"return x;\",fv=\"return abs(x);\";var FD=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",PD=Wt+`\n return (x < 0.0) ? 0.0 : x;\n`,OD=Wt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,La=\"return x;\",MD=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var BD=\"return x;\",zD=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,VD=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,WD=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,UD=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Fr=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${t}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var fh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let t=e.length,o=Rt(\"rc\",t),n=Re(t),s=ED(t,o),a=o.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n void main() {\n ${n} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${i}));\n }\n `}};var s9=Vt.whereImpl,a9=1e-7,i9=1e-4,hh={};function u9(r){return r in hh||(hh[r]={}),hh[r]}var p9=A().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),c9=600;function l9(){return A().global.screen==null?1024:A().global.screen.height*A().global.screen.width*window.devicePixelRatio*c9/1024/1024}var Lc=class r extends ao{nextDataId(){return r.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!A().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof bp)t=e;else{let o=Kr(A().getNumber(\"WEBGL_VERSION\"),e);t=new bp(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Kr(A().getNumber(\"WEBGL_VERSION\"));t=new bp(o),this.binaryCache=u9(A().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new dh(this.gpgpu),this.numMBBeforeWarning=l9(),this.texData=new Bo(this,ur())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=_c(t),c=new Zl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((A().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||A().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:mr.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(A().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:o,dtype:n,values:t,usage:mr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Fr(i,La):m=new tr(i,La);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n===\"string\")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n===\"complex64\"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=w.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Fr(n,La):f=new tr(n,La);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(A().getBool(\"DEBUG\")&&!A().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&A().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,c;if(a!==\"complex64\"&&A().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...jl(n))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let l;if(a===\"complex64\"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=w.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ur().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;p?d=new Fr(s,La):d=new tr(s,La);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(e,t.customTexShape),l=ur().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=p9){return A().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return ur().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new fh(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new mh(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){let o=[gi(e.shape),...xi(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[gi(t),...xi(t)],a=new Mc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=_c(s),p;n?p=new eh(i):p=new Jf(i);let u=!0,c=[t!=null?t:jl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===gu.DENSE){let x=a!=null?a:jl(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=A().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!xu(b.shape,x.shape)){let C=x,S=x.shape;x.shape=b.shape,x=this.packedReshape(x,S),u.push(x),b=this.texData.get(x.dataId),C.shape=S}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=MR(e,c,l),d=this.getAndSaveBinary(m,()=>PR(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),A().get(\"ENGINE_COMPILE_ONLY\")||OR(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=A().getNumber(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!A().getBool(\"WEBGL_LAZILY_UNPACK\")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(A().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!A().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=A().getBool(\"DEBUG\");A().set(\"DEBUG\",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(A().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?a9:i9}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=WI(o,p),t.texShape=l),s!=null){let m=_c(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Ma(l[0],l[1])),p?d=new oh(m,g):d=new Zl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=mr.PIXELS:C.usage=mr.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let S=[[h,f]],_=this.runWebGLProgram(d,[b],n,S,!0),$=this.texData.get(_.dataId);t.texShape=$.texShape,t.isPacked=$.isPacked,t.usage=$.usage,A().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(_.dataId):(t.texture=$.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=m9(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await cS(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(qf(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);let{variablesLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,outShapeLocation:a,outShapeStridesLocation:i,outTexShapeLocation:p}=XI(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.outShapeLocation=a,e.outShapeStridesLocation=i,e.outTexShapeLocation=p}}createTensorFromGPUData(e,t,o){e.channels=e.channels||\"RGBA\";let{texture:n,height:s,width:a,channels:i}=e,p=ur().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let u=p.writeTexture(n,t,o,s,a,i);return ur().makeTensorFromDataId(u,t,o,p)}};Lc.nextDataId=0;function m9(r,e){if(e===\"float32\"||e===\"complex64\")return r;if(e===\"int32\"||e===\"bool\"){let t=e===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew Lc,2);var $at={forceHalfFloat:GD};var Bc=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var Pr=class{constructor(e,t,o){this.variableNames=[\"A\",\"B\"],this.outputShape=w.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${e}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Xr=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var jr=class{constructor(e,t,o,n=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=w.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=ut(s);let a=\"\";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(a=`\n ${Re(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?a+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:a+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let p=Rt(\"coords\",s);this.enableShapeUniforms?a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${e}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${a}\n\n setOutput(result);\n }\n `}};function Dt(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var HD={kernelName:Co,backendName:\"webgl\",kernelFunc:Dt};function Or(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.texData.get(s.dataId),i=Dt({inputs:{x:o},backend:t}),p=Dt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var KD={kernelName:Di,backendName:\"webgl\",kernelFunc:Or};var hv=\"return (a < 0.) ? b * a : a;\",gv=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function f9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),i=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(gv,n.shape,a.shape):new Pr(hv,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],\"float32\");return t.disposeIntermediateTensorInfo(a),p}var qD={kernelName:$n,backendName:\"webgl\",kernelFunc:f9};var xv=\"return (a < 0.) ? b * a : a;\",yv=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function h9(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(yv,o.shape,n.shape):new Pr(xv,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],\"float32\")}var jD={kernelName:rs,backendName:\"webgl\",kernelFunc:h9};var Fo=\"if (isnan(x)) return x;\";function xe({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=A().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&e!=null,c;return u?c=new Fr(a.shape,e):c=new tr(a.shape,r),i.runWebGLProgram(c,[a],p)}}function nt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype===\"complex64\"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[S,k]=C,_={dataId:S.dataId,dtype:S.dtype,shape:p.shape},$={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new Pr(r,p.shape,u.shape);return c.runWebGLProgram(R,[_,$],dt(S.dtype,k.dtype))}),b=Or({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype===\"string\"||u.dtype===\"string\"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype===\"string\"?w.fromUint8ToStringArray(f):f,x=p.dtype===\"string\"?w.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),S=c.makeTensorInfo(C,l),k=c.texData.get(S.dataId);return k.values=b,S}let m=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&e!=null,d;return m?d=new jr(e,p.shape,u.shape,t):d=new Pr(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function yi(r,e=!1){if(r===\"linear\")return e?BD:AD;if(r===\"relu\")return e?VD:PD;if(r===\"elu\")return e?zD:FD;if(r===\"relu6\")return e?WD:OD;if(r===\"prelu\")return e?yv:xv;if(r===\"leakyrelu\")return e?gv:hv;if(r===\"sigmoid\")return e?UD:MD;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var zc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=ut(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?\"i * 2, rc.y\":\"rc.y, i * 2\",d=s?\"rc.z, i * 2\":\"i * 2, rc.z\",f=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";i&&(p?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${i}\n }`:u?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${i}\n }`:g=`vec4 activation(vec4 x) {\n ${i}\n }`,x=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let C=\"rc.x\",S=\"rc.x\";e[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!xu(n.shape,p)&&!(c.texture!==null&&xu(c.shape,p))?QD(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var ZD={kernelName:da,backendName:\"webgl\",kernelFunc:te};var rm=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c=\"\";s%o>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${i}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${u}\n }\n\n int inIdx = inOffset + ${i};\n if (${p===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${u}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${u}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${u}\n }\n setOutput(sumValue);\n }\n `}};var gh=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=\"0.0\",p=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",p=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",p=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let c=Math.floor(o/4)*4,l=o%4,m=`\n if (${t===\"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${t===\"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${p}(values, minMaxValue);\n if (${t===\"min\"} || ${t===\"max\"}) {\n minMaxValue = ${p}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,d=\"vec4\";t===\"all\"?(i=\"1.0\",m=`\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",m=`\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n `,d=\"bvec4\");let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${i};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n vec4 minMaxValue = vec4(${i});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===2}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===3}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function x9(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=w.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Yr(r,e,t,o){let n=x9(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Re(this.rank),s=dv(\"rc\",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes 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ne=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));re.push(ne),D.push(ne)}j=n.runWebGLProgram(J,re,Y)}let q=te({inputs:{x:j},backend:n,attrs:{shape:S}});D.push(j);for(let Y of D)n.disposeIntermediateTensorInfo(Y);return q}function b9(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Sp({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var rA={kernelName:So,backendName:\"webgl\",kernelFunc:b9};var oA=\"return abs(x);\";function C9(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!==\"complex64\"){let s=t.texData.get(o.dataId),a=ch(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return A().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?n=new Fr(o.shape,oA):n=new tr(o.shape,oA),t.runWebGLProgram(n,[o],o.dtype)}var nA={kernelName:\"Abs\",backendName:\"webgl\",kernelFunc:C9};var w9=Wt+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,S9=xe({opSnippet:w9}),sA={kernelName:Vo,backendName:\"webgl\",kernelFunc:S9};var I9=Wt+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,v9=xe({opSnippet:I9}),aA={kernelName:Wo,backendName:\"webgl\",kernelFunc:v9};var iA=\"return a + b;\",k9=nt({opSnippet:iA,packedOpSnippet:iA,supportsComplex:!0,cpuKernelImpl:LR}),uA={kernelName:\"Add\",backendName:\"webgl\",kernelFunc:k9};var bh=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n float result = ${n};\n setOutput(result);\n }\n `}};var Ch=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n vec4 result = ${n};\n setOutput(result);\n }\n `}};function wh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Dt({inputs:{x:o[0]},backend:t});if(o.length>A().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let p=Math.floor(o.length/2),u=wh({inputs:o.slice(0,p),backend:t}),c=wh({inputs:o.slice(p),backend:t});return wh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=A().getBool(\"WEBGL_PACK\")?new Ch(o[0].shape,s):new bh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var pA={kernelName:Uo,backendName:\"webgl\",kernelFunc:wh};function N9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims(\"all\",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,\"all\",t),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var cA={kernelName:\"All\",backendName:\"webgl\",kernelFunc:N9};function 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n=[t];if(w.assertAxesAreInnerMostDims(\"arg\"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!A().getBool(\"WEBGL_PACK_REDUCE\")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,c]=w.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=mA(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return dA(r,e,o)}function _9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=vh(t,p,a[0],\"max\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var fA={kernelName:Ys,backendName:\"webgl\",kernelFunc:_9};function E9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=vh(t,p,a[0],\"min\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var hA={kernelName:Qs,backendName:\"webgl\",kernelFunc:E9};var $9=Wt+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,R9=xe({opSnippet:$9}),gA={kernelName:Ko,backendName:\"webgl\",kernelFunc:R9};var D9=Wt+\"return log(x + sqrt(x * x + 1.0));\",A9=xe({opSnippet:D9}),xA={kernelName:qo,backendName:\"webgl\",kernelFunc:A9};var F9=Wt+`\n return atan(x);\n`,P9=xe({opSnippet:F9}),yA={kernelName:jo,backendName:\"webgl\",kernelFunc:P9};var O9=Bc+`\n return atan(a, b);\n`,M9=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+Xr+`\n return result;\n`,L9=nt({opSnippet:O9,packedOpSnippet:M9}),bA={kernelName:Yo,backendName:\"webgl\",kernelFunc:L9};var B9=Wt+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelName:Xo,backendName:\"webgl\",kernelFunc:z9};var Us=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t===\"avg\",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),o){this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value >= currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let C=\"max\",S=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(S=\"avgValue / max(count, 1.0)\");let k=Math.floor(a/4)*4,_=a%4,$=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${C}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${k}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${$}\n }\n\n int xC = xCCorner + ${k};\n if (${_===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${$}\n } else if (${_===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${$}\n } else if (${_===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${$}\n }\n }\n setOutput(${S});\n }\n `}},bu=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t===\"avg\",S=\"0.0\";if(C||(S=\"-1.0 / 1e-20\"),o){this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value >= currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${f} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let k=\"max\",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(_=\"avgValue / max(count, 1.0)\");let $=Math.floor(a/4)*4,R=a%4,D=`\n if (${C}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${k}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${S};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${S});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${$}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${D}\n }\n\n int xC = xCCorner + ${$};\n if (${R===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${D}\n }\n }\n }\n setOutput(${_});\n }\n `}};function V9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Vs(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Us(c,\"avg\",!1);return t.runWebGLProgram(l,[n],\"float32\")}var wA={kernelName:Qo,backendName:\"webgl\",kernelFunc:V9};function W9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new bu(l,\"avg\",!1);return t.runWebGLProgram(m,[n],\"float32\")}var SA={kernelName:Zs,backendName:\"webgl\",kernelFunc:W9};var kh=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${l});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${p};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC+= ${i}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},Nh=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=l-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`\n const ivec3 pads = ivec3(${f}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${l};\n wD += ${p}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${u}) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${d};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function U9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Nh(m);return t.runWebGLProgram(d,[n],a.dtype)}var IA={kernelName:Ri,backendName:\"webgl\",kernelFunc:U9};function G9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Vs([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new kh(c);return t.runWebGLProgram(l,[n],a.dtype)}var vA={kernelName:$i,backendName:\"webgl\",kernelFunc:G9};function H9(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Sp({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var kA={kernelName:Zo,backendName:\"webgl\",kernelFunc:H9};var Th=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],w.assertAndGetBroadcastShape(e,t),w.assertAndGetBroadcastShape(e,o);let i=\"0.0\";n!=null&&(w.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"1.0\";s!=null&&(w.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${i};\n float scale = ${p};\n float inv = scale * inversesqrt(variance + float(${a}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var _h=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],w.assertAndGetBroadcastShape(e,t),w.assertAndGetBroadcastShape(e,o);let i=\"vec4(0.0)\";n!=null&&(w.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"vec4(1.0)\";s!=null&&(w.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n vec4 offset = ${i};\n vec4 scale = ${p};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${a}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var K9=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||n.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(i==null||n.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=A().getBool(\"WEBGL_PACK_NORMALIZATION\")?new _h(o.shape,n.shape,s.shape,c,l,p):new Th(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},NA={kernelName:In,backendName:\"webgl\",kernelFunc:K9};var Eh=class{constructor(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;let t=Re(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let o=q9(this.rank),n,s=e.map((a,i)=>`sourceLoc.${wv[i]} = start[${i}] + coords.${wv[i]};`);n=`\n ${t} sourceLoc;\n ${t} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${n}\n setOutput(getSource(${o}));\n }\n `}},wv=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function q9(r){if(r===1)return\"sourceLoc\";if(r<=6)return wv.slice(0,r).map(e=>\"sourceLoc.\"+e).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var $h=class{constructor(e){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let t=Re(this.rank),o=Rt(\"coords\",this.rank),n=Rt(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`\n result.x = ${a};\n if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.y = ${a};\n --${n[this.rank-1]};\n }\n `,p=this.rank===1?\"\":`\n --${o[this.rank-1]};\n if (++${o[this.rank-2]} < ${e[this.rank-2]}) {\n ++${n[this.rank-2]};\n result.z = ${a};\n if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.w = ${a};\n }\n }\n `,u=this.rank<=4?`sourceLoc = coords +\n ${t}(${e.map((c,l)=>`start[${l}]`).join()});`:e.map((c,l)=>`${n[l]} = ${o[l]} + start[${l}];`).join(`\n`);this.userCode=`\n void main() {\n ${t} coords = getOutputCoords();\n ${t} sourceLoc;\n ${u}\n vec4 result = vec4(0.);\n ${i}\n ${p}\n setOutput(result);\n }\n `}};function j9(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=pt.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function Gs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=pt.parseSliceParams(n,s,a);if(pt.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.texData.get(n.dataId),m=gD(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=pt.isSliceContinous(n.shape,i,p);if(u||!c){let l=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new $h(p):new Eh(p),m=[i];return t.runWebGLProgram(l,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),j9(n,i,p,t)}var TA={kernelName:ha,backendName:\"webgl\",kernelFunc:Gs};var X9=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:t,attrs:{shape:p}}),h=bt({inputs:{x:f},backend:t,attrs:{perm:u}}),g=te({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Gs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},_A={kernelName:Js,backendName:\"webgl\",kernelFunc:X9};function Y9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),p=t.readSync(s.dataId),u=ph(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var EA={kernelName:Jo,backendName:\"webgl\",kernelFunc:Y9};var Q9=`\n int r = int(a.r) & int(b.r);\n int g = int(a.g) & int(b.g);\n int rb = int(a.b) & int(b.b);\n int ra = int(a.a) & int(b.a);\n return vec4(r, g, rb, ra);\n`,Z9=`\n return float(int(a.r) & int(b.r));\n`;function J9(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"),a=A().getNumber(\"WEBGL_VERSION\");if(t.shouldExecuteOnCPU([o,n])||a===1){let p=t.texData.get(o.dataId).values,u=t.texData.get(n.dataId).values,[c,l]=zR(o.shape,n.shape,p,u,o.dtype),m=t.makeTensorInfo(l,o.dtype),d=t.texData.get(m.dataId);return d.values=c,m}let i;return s?i=new jr(Q9,o.shape,n.shape,!1):i=new Pr(Z9,o.shape,n.shape),t.runWebGLProgram(i,[o,n],o.dtype)}var $A={kernelName:qa,backendName:\"webgl\",kernelFunc:J9};function eJ(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.readSync(o.dataId),a=t.readSync(n.dataId),i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],\"int32\",Int32Array.from(i))}var RA={kernelName:ea,backendName:\"webgl\",kernelFunc:eJ};var tJ=\"return float(a != b);\",Sv=nt({opSnippet:tJ,cpuKernelImpl:iD,dtype:\"bool\"}),DA={kernelName:Yn,backendName:\"webgl\",kernelFunc:Sv};function bi(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.real},backend:t})}var AA={kernelName:Hi,backendName:\"webgl\",kernelFunc:bi};var rJ=\"return float(int(x));\";function FA(r,e){let t=new tr(r.shape,rJ),o=e.runWebGLProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Iv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return Dt({inputs:{x:n},backend:t});let a=Gr(n.shape),i=Iv({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=Or({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),p}if(n.dtype===\"complex64\"){let a=bi({inputs:{input:n},backend:t}),i=Iv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Dt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.texData.get(n.dataId).values,[i,p,u]=VR(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return FA(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=Sv({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var PA={kernelName:yo,backendName:\"webgl\",kernelFunc:Iv};var OA=\"return ceil(x);\",oJ=xe({opSnippet:OA,packedOpSnippet:OA,cpuKernelImpl:WR}),MA={kernelName:en,backendName:\"webgl\",kernelFunc:oJ};var Rh=class{constructor(e){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var Dh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function nJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;A().getBool(\"WEBGL_PACK_CLIP\")?i=new Dh(n.shape):i=new Rh(n.shape);let p=[[s],[a]];return t.runWebGLProgram(i,[n],n.dtype,p)}var LA={kernelName:bo,backendName:\"webgl\",kernelFunc:nJ};var Ah=class{constructor(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). 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pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${l};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${f}) *\n getW(wR, wC, ${f}, d2);\n } else {\n dotProd +=\n getX(batch, ${f}, xR, xC) *\n getW(wR, wC, ${f}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2),\n getW(wR, wC, ${f} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1),\n getX(batch, xR, xC, ${f} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC),\n getX(batch, ${f} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${k}\n setOutput(result);\n }\n `}},Mh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${a}, ${i});\n const ivec3 pads = ivec3(${t}, ${o}, ${n});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${l}; wF++) {\n int xF = xFCorner + wF * ${p};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${f}) *\n getW(wF, wR, wC, ${f}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1),\n getX(batch, xF, xR, xC, ${f} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2),\n getW(wF, wR, wC, ${f} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var Uc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*p};\n `,i===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,p===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,p>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${i}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+l}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+l}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${u}\n\n ${n.output} = result;\n }\n `}};function Bh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function zh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat===\"channelsLast\",f=!1,h=!1,g,x=[];if(s!=null){let S=Bh(s.shape,d);S!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:S}}),x.push(s))}if(n!=null){let S=Bh(n.shape,d);S!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:S}}),x.push(n))}if(!((l===1||m===1)&&c>Cv)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let S=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,S,t.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(xu(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let $=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push($);let R=Sp({a:k,b:$,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),D=o.texData.get(R.dataId);y.assert(D.isPacked,()=>\"batchMatMul result is expected to be packed\"),u.shape=_,D.shape=t.outShape,g=Dt({inputs:{x:R},backend:o}),g.shape=t.outShape,x.push(R)}else{let S=t.outHeight*t.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,S,t.inChannels]:[t.batchSize,t.inChannels,S]}}),_=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),$=Sp({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:$},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(_),x.push($)}for(let S of x)o.disposeIntermediateTensorInfo(S);return g}function Vh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=t,f=d===\"channelsLast\",h=p*u*c,g=m*l,x=[t.batchSize,h,g],b=!0,C=!1,S=[];if(s!=null){let q=Bh(s.shape,f);q!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:q}}),S.push(s))}if(n!=null){let q=Bh(n.shape,f);q!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:q}}),S.push(n))}let k=te({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});S.push(k);let _=new Lh(x,t),$=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],R=o.runWebGLProgram(_,[r],\"float32\",$),D=te({inputs:{x:R},backend:o,attrs:{shape:x}});S.push(R),S.push(D);let P=n!=null,O=s!=null,M=i===\"leakyrelu\",L=i?yi(i,!0):null,B=new zc(f?D.shape:k.shape,f?k.shape:D.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,C,P,L,O,M),z=f?[D,k]:[k,D];if(n&&z.push(n),O&&z.push(s),M){let q=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));z.push(q),S.push(q)}let U=o.runWebGLProgram(B,z,\"float32\"),j=te({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});S.push(U);for(let q of S)o.disposeIntermediateTensorInfo(q);return j}function iJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))d=zh({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l===\"channelsLast\"&&A().getBool(\"WEBGL_EXP_CONV\")){let h=new Uc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],\"float32\",g)}else if(A().getBool(\"WEBGL_CONV_IM2COL\"))d=Vh({x:n,filter:s,convInfo:m,backend:t});else{let h=new Wc(m);d=t.runWebGLProgram(h,[n,s],\"float32\")}let f=te({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var UA={kernelName:tn,backendName:\"webgl\",kernelFunc:iJ};var Wh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n ${a?`float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);`}\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Uh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,p=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${l}];\n\n ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n if (${a}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Gh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yF = 0; yF < ${e.outDepth}; yF++) {\n int xF = wF + yF * ${t} - ${s};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${o} - ${a};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${i};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Hh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${u}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${t}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${t} - 1 - wF;\n\n for (int wR = 0; wR < ${o}; wR++) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${o} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function uJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Wh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var GA={kernelName:Fi,backendName:\"webgl\",kernelFunc:uJ};var Kh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"strides\",type:\"vec2\"}],this.outputShape=e.inShape,this.enableShapeUniforms=ut(this.outputShape.length);let t=e.filterHeight,o=e.filterWidth,n=t-1-e.padInfo.top,s=o-1-e.padInfo.left;this.userCode=`\n const ivec2 pads = ivec2(${n}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n vec4 result = vec4(0.);\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / strides[0];\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n int wCPerm = ${o} - 1 - wC;\n\n float dyC = float(dyCCorner + wC) / strides[1];\n bool idyCVal = (dyC >= 0.0) && (dyC < ${e.outWidth}.0)\n && (fract(dyC) == 0.0);\n int idyC = int(dyC);\n\n float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${e.outWidth}.0)\n && (fract(dyC2) == 0.0);\n int idyC2 = int(dyC2);\n\n if (idyCVal && idyCVal2) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n dySample : getDy(batch, idyR, idyC2, d2);\n\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n\n dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample2.xy : dySample2.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal2) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC2, d2);\n vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n }\n }\n }\n setOutput(result);\n }\n `}};function pJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l);if(A().getBool(\"WEBGL_PACK_CONV2DTRANSPOSE\")&&l===\"channelsLast\"){let d=[[m.strideHeight,m.strideWidth]],f=new Kh(m);return t.runWebGLProgram(f,[n,s],\"float32\",d)}else{let d=new Uh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}}var HA={kernelName:rn,backendName:\"webgl\",kernelFunc:pJ};function cJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Mh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var KA={kernelName:on,backendName:\"webgl\",kernelFunc:cJ};function lJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Gh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var qA={kernelName:ja,backendName:\"webgl\",kernelFunc:lJ};function mJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,i,1,a),c=new Hh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var jA={kernelName:nn,backendName:\"webgl\",kernelFunc:mJ};var dJ=Fo+`\n return cos(x);\n`,fJ=`\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${Xr}\n return result;\n`,hJ=xe({opSnippet:dJ,packedOpSnippet:fJ}),XA={kernelName:\"Cos\",backendName:\"webgl\",kernelFunc:hJ};var gJ=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,xJ=xe({opSnippet:gJ}),YA={kernelName:an,backendName:\"webgl\",kernelFunc:xJ};var qh=class{constructor(e,t,o,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n===\"bilinear\"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,\"(y2-y1) * height_ratio\",`y1*${f} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${f}`],[C,S,k]=m>1?[`${(p-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${C});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${a}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${S};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${f} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${k};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${d} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var yJ=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new qh(n.shape,s.shape,i,p,u);return t.runWebGLProgram(c,[n,s,a],\"float32\")},QA={kernelName:cn,backendName:\"webgl\",kernelFunc:yJ};var vp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(vp||(vp={}));var om=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,a=this.op===vp.Prod?\"1.0\":\"0.0\",i=o?a:`getX(${ZA(s,\"coords\",this.op)})`,p=this.outputShape[this.outputShape.length-1],u=\"\",c=\"\";o?(u=n?`end != ${p-1}`:\"end != 0\",c=n?\"end + 1\":\"end - 1\"):(u=n?`end + pow2 < ${p}`:\"end >= pow2\",c=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${Re(s)} coords = getOutputCoords();\n int end = ${JA(s,\"coords\",this.op)};\n float val = ${i};\n int pow2 = int(pow(2.0, index));\n if (${u}) {\n int idx = ${c};\n ${JA(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${ZA(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function ZA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function JA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function jh(r,e,t,o,n,s){let a=e.shape.length,i=w.getAxesPermutation([o],a),p=e;i!=null&&(p=bt({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Dt({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new om(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new om(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=bt({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function bJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return jh(vp.Prod,n,t,s,a,i)}var eF={kernelName:un,backendName:\"webgl\",kernelFunc:bJ};function CJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return jh(vp.Sum,n,t,s,a,i)}var tF={kernelName:pn,backendName:\"webgl\",kernelFunc:CJ};function wJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=ph(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=BR(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var rF={kernelName:ra,backendName:\"webgl\",kernelFunc:wJ};var Xh=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${t};\n int offset_h = imod(h, ${t});\n int in_w = w / ${t};\n int offset_w = imod(w, ${t});\n int offset_d = (offset_h * ${t} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function SJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=new Xh(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var oF={kernelName:ln,backendName:\"webgl\",kernelFunc:SJ};var Gc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u=\"\",c=\"\";o&&(n?u=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?u=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:u=`\n float activation(float x) {\n ${o}\n }\n `,c=\"result = activation(result);\");let l=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${u}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${p};\n int q = d2 - d1 * ${p};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${a}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${i}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${l}\n ${c}\n setOutput(result);\n }\n `}};var Hc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=`\n xC = xCCorner + ${b*u};\n `,p===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,u===1&&b>0?d+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:d+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):d+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,u>1?d+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:d+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):C===1?d+=`\n xC${b+1} = xTexelC${b};\n `:d+=`\n xCOffset = xC + ${C};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(d+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=w.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;A().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new Hc(l):m=new Gc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],\"float32\",d)}var nF={kernelName:mn,backendName:\"webgl\",kernelFunc:IJ};var Yh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${a} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Qh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${p}; dm++) {\n int d2 = d1 * ${p} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function vJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Yh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var sF={kernelName:Pi,backendName:\"webgl\",kernelFunc:vJ};function kJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Qh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var aF={kernelName:Oi,backendName:\"webgl\",kernelFunc:kJ};var Zh=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function NJ(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Zh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var iF={kernelName:oa,backendName:\"webgl\",kernelFunc:NJ};var Jh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${a});\n const ivec2 pads = ivec2(${l}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${i}; h++) {\n int hIn = hBeg + h * ${u};\n\n if (hIn >= 0 && hIn < ${t}) {\n for (int w = 0; w < ${p}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${o}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function TJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c,l=new Jh(u);c=t.runWebGLProgram(l,[n,s],\"float32\");let m=te({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var uF={kernelName:dn,backendName:\"webgl\",kernelFunc:TJ};function _J(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=wp({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var pF={kernelName:Bi,backendName:\"webgl\",kernelFunc:_J};var EJ=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",$J=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,RJ=xe({opSnippet:EJ,packedOpSnippet:$J}),cF={kernelName:\"Elu\",backendName:\"webgl\",kernelFunc:RJ};var DJ=\"return (b >= 0.0) ? a : a * (b + 1.0);\",AJ=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,FJ=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(AJ,o.shape,n.shape):new Pr(DJ,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},lF={kernelName:Xa,backendName:\"webgl\",kernelFunc:FJ};var PJ=`\n return vec4(equal(a, b));\n`,OJ=\"return float(a == b);\",MJ=nt({opSnippet:OJ,packedOpSnippet:PJ,dtype:\"bool\",cpuKernelImpl:GR}),mF={kernelName:xn,backendName:\"webgl\",kernelFunc:MJ};var LJ=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${w.ERF_P};\n float a1 = ${w.ERF_A1};\n float a2 = ${w.ERF_A2};\n float a3 = ${w.ERF_A3};\n float a4 = ${w.ERF_A4};\n float a5 = ${w.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,BJ=xe({opSnippet:LJ}),dF={kernelName:\"Erf\",backendName:\"webgl\",kernelFunc:BJ};var zJ=Fo+`\n return exp(x);\n`,VJ=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,kv=xe({opSnippet:zJ,packedOpSnippet:VJ,cpuKernelImpl:HR,dtype:\"float32\"}),fF={kernelName:\"Exp\",backendName:\"webgl\",kernelFunc:kv};function eg(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var hF={kernelName:na,backendName:\"webgl\",kernelFunc:eg};var gF=\"return exp(x) - 1.0;\",WJ=xe({opSnippet:gF,packedOpSnippet:gF,cpuKernelImpl:KR}),xF={kernelName:bn,backendName:\"webgl\",kernelFunc:WJ};var nm=class{constructor(e,t,o){this.variableNames=[\"real\",\"imag\"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:\"1.0\",i;if(e===\"real\")i=\"return real * expR - imag * expI;\";else if(e===\"imag\")i=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${e}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${i}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${n});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${n}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${a};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function tg(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new nm(\"real\",p,e),c=new nm(\"imag\",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,\"float32\"),d=t.runWebGLProgram(c,l,\"float32\"),f=Or({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function UJ(r){let{inputs:e,backend:t}=r,{input:o}=e;return tg(o,!1,t)}var yF={kernelName:\"FFT\",backendName:\"webgl\",kernelFunc:UJ};var rg=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=e,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Ci(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new rg(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var bF={kernelName:sa,backendName:\"webgl\",kernelFunc:Ci};var og=class{constructor(e){this.variableNames=[\"Image\"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${t} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${t}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var CF={kernelName:Cn,backendName:\"webgl\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new og(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var wF=\"return floor(x);\",GJ=xe({opSnippet:wF,packedOpSnippet:wF,cpuKernelImpl:qR}),SF={kernelName:wn,backendName:\"webgl\",kernelFunc:GJ};var HJ=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,KJ=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,qJ=nt({opSnippet:HJ,packedOpSnippet:KJ,dtype:\"int32\"}),IF={kernelName:Sn,backendName:\"webgl\",kernelFunc:qJ};var ng=class{constructor(e){this.variableNames=[\"A\"];let t=It(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);\n\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var sg=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let t=It(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${n}.0, ${o}.0);\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${t.output} = result;\n }\n `}};var vF={kernelName:Du,backendName:\"webgl\",kernelFunc:jJ},Kc,Nv=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function jJ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Kc==null||h!==Nv)&&(Nv=h,Kc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:Nv})),Kc.canvas.width=p,Kc.canvas.height=u,Kc.drawImage(n,0,0,p,u),n=Kc.canvas}let m=t.makeTensorInfo(c,\"int32\");t.texData.get(m.dataId).usage=mr.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let d=A().getBool(\"WEBGL_PACK\")?new sg(l):new ng(l),f=t.runWebGLProgram(d,[m],\"int32\");return t.disposeData(m.dataId),f}function XJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],C=a!=null,S=i!=null,k=d===\"leakyrelu\",_=()=>{let R=[n,s],D=(P,O)=>{if(O===\"NCHW\"&&P.shape.length===1&&P.shape[0]!==1){let M=te({inputs:{x:P},backend:t,attrs:{shape:[P.shape[0],1,1]}});return b.push(M),M}return P};if(C&&R.push(D(a,c)),S&&R.push(D(i,c)),k){let P=t.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));R.push(P),b.push(P)}return R};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=zh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h===\"channelsLast\"&&A().getBool(\"WEBGL_EXP_CONV\")){let R=d?yi(d,!0):null,D=new Uc(g,C,R,S,k),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],O=_();x=t.runWebGLProgram(D,O,\"float32\",P)}else if(A().getBool(\"WEBGL_CONV_IM2COL\"))x=Vh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let R=d?yi(d,!1):null,D=new Wc(g,C,R,S,k),P=_();x=t.runWebGLProgram(D,P,\"float32\")}let $=te({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(R=>t.disposeIntermediateTensorInfo(R)),$}var kF={kernelName:Io,backendName:\"webgl\",kernelFunc:XJ};function YJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${a}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function QJ(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=te({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=te({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let x=t.readSync(n.dataId),b=t.bufferSync(o),C=jR(x,b,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,C.values)}let f=new ag(a,l,[u,c],o.shape),h=t.runWebGLProgram(f,[d,m],d.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:p}});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var TF={kernelName:vn,backendName:\"webgl\",kernelFunc:QJ};var ig=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let o=Re(this.rank),n=ZJ(e,2);this.userCode=`\n void main() {\n ${o} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${n}));\n }\n `}};function ZJ(r,e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=0,()=>`GatherV2: the index value ${k} is not in [0, ${C-1}]`)}}let u=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=te({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=te({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype===\"string\"){let b=t.bufferSync(d),C=t.bufferSync(m),S=XR(C,b,f);return l.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,S.dtype,S.values)}let h=new ig(m.shape,f),g=t.runWebGLProgram(h,[m,d],m.dtype);l.push(g);let x=te({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var _F={kernelName:aa,backendName:\"webgl\",kernelFunc:Tv};var JJ=\"return float(a > b);\",eee=`\n return vec4(greaterThan(a, b));\n`,tee=nt({opSnippet:JJ,packedOpSnippet:eee,cpuKernelImpl:YR,dtype:\"bool\"}),EF={kernelName:kn,backendName:\"webgl\",kernelFunc:tee};var ree=\"return float(a >= b);\",oee=`\n return vec4(greaterThanEqual(a, b));\n`,nee=nt({opSnippet:ree,packedOpSnippet:oee,dtype:\"bool\",cpuKernelImpl:QR}),$F={kernelName:Nn,backendName:\"webgl\",kernelFunc:nee};function see(r){let{inputs:e,backend:t}=r,{input:o}=e;return tg(o,!0,t)}var RF={kernelName:Vi,backendName:\"webgl\",kernelFunc:see};var aee=\"return float(!isnan(x) && !isinf(x));\",iee=xe({opSnippet:aee,dtype:\"bool\"}),DF={kernelName:Tn,backendName:\"webgl\",kernelFunc:iee};var uee=\"return float(isinf(x));\",pee=xe({opSnippet:uee,dtype:\"bool\"}),AF={kernelName:_n,backendName:\"webgl\",kernelFunc:pee};var cee=\"return float(isnan(x));\",lee=xe({opSnippet:cee,dtype:\"bool\"}),FF={kernelName:En,backendName:\"webgl\",kernelFunc:lee};var mee=\"return float(a < b);\",dee=`\n return vec4(lessThan(a, b));\n`,fee=nt({opSnippet:mee,packedOpSnippet:dee,cpuKernelImpl:ZR,dtype:\"bool\"}),PF={kernelName:Rn,backendName:\"webgl\",kernelFunc:fee};var hee=\"return float(a <= b);\",gee=`\n return vec4(lessThanEqual(a, b));\n`,xee=nt({opSnippet:hee,packedOpSnippet:gee,cpuKernelImpl:JR,dtype:\"bool\"}),OF={kernelName:Dn,backendName:\"webgl\",kernelFunc:xee};function yee(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=eD(o,n,s);return e.makeTensorInfo([a.length],\"float32\",a)}var MF={kernelName:An,backendName:\"webgl\",kernelFunc:yee};var bee=Fo+`\n return x < 0.0 ? 0./0. : log(x);\n`,Cee=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,wee=xe({opSnippet:bee,packedOpSnippet:Cee,cpuKernelImpl:tD}),LF={kernelName:\"Log\",backendName:\"webgl\",kernelFunc:wee};var See=Fo+`\n return log(1.0 + x);\n`,Iee=xe({opSnippet:See}),BF={kernelName:Pn,backendName:\"webgl\",kernelFunc:Iee};var vee=\"return float(a >= 1.0 && b >= 1.0);\",kee=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,Nee=nt({opSnippet:vee,packedOpSnippet:kee,dtype:\"bool\"}),zF={kernelName:On,backendName:\"webgl\",kernelFunc:Nee};var Tee=\"return float(!(x >= 1.0));\",_ee=xe({opSnippet:Tee}),VF={kernelName:Mn,backendName:\"webgl\",kernelFunc:_ee};var Eee=\"return float(a >= 1.0 || b >= 1.0);\",$ee=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,Ree=nt({opSnippet:Eee,packedOpSnippet:$ee,dtype:\"bool\"}),WF={kernelName:Ln,backendName:\"webgl\",kernelFunc:Ree};var ug=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${a}; j <= ${a}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${i}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${p};\n setOutput(val);\n }\n `}};var pg=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${a};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${a}; j <= ${a}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${p};\n setOutput(result);\n }\n `}};var Dee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=A().getBool(\"WEBGL_PACK_NORMALIZATION\")?new pg(n.shape,s,a,i,p):new ug(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},UF={kernelName:\"LRN\",backendName:\"webgl\",kernelFunc:Dee};var cg=class{constructor(e,t,o,n,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${t})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${t} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${n}) * norm + float(${o});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${n})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var Aee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new cg(n.shape,i,p,u,c);return t.runWebGLProgram(l,[n,s,a],n.dtype)},GF={kernelName:Ya,backendName:\"webgl\",kernelFunc:Aee};function HF(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Yr(i,r.dtype,\"max\",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function _v(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=t.texData.get(d.dataId).values,S=new Array(i);for(let $=0;$`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Us(c,\"max\",!1);return t.runWebGLProgram(l,[n],n.dtype)}var jF={kernelName:Wn,backendName:\"webgl\",kernelFunc:Mee};function Lee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new bu(l,\"max\",!1);return t.runWebGLProgram(m,[n],n.dtype)}var XF={kernelName:ia,backendName:\"webgl\",kernelFunc:Lee};var lg=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,p=a-1-e.padInfo.left,u=s*a-1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${n}) {\n float dyR = float(dyRCorner + wR) / ${t}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${a}; wC++) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${a} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},mg=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=c-1-e.padInfo.left,f=p*u*c-1;this.userCode=`\n const ivec3 pads = ivec3(${l}, ${m}, ${d});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${t}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${u};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${i}) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${f} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${u} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function Bee(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new bu(m,\"max\",!0),f=t.runWebGLProgram(d,[a],a.dtype),h=new mg(m),g=t.runWebGLProgram(h,[n,f],a.dtype);return t.disposeIntermediateTensorInfo(f),g}var YF={kernelName:Gi,backendName:\"webgl\",kernelFunc:Bee};function zee(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Vs([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new Us(m,\"max\",d),h=t.runWebGLProgram(f,[i],i.dtype),g=new lg(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var QF={kernelName:Ui,backendName:\"webgl\",kernelFunc:zee};function ZF(r,e,t,o){let n=new Us(t,\"max\",!1),s=o.runWebGLProgram(n,[r],\"float32\");n=new Us(t,\"max\",!0,!0,e);let a=o.runWebGLProgram(n,[r],\"float32\");return[s,a]}var JF={kernelName:ua,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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gg(u,a,i,p),l=te({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=t.runWebGLProgram(c,[l],s);t.disposeIntermediateTensorInfo(l);let d=[...n.shape,a],f=te({inputs:{x:m},backend:t,attrs:{shape:d}});return t.disposeIntermediateTensorInfo(m),f},h3={kernelName:Jn,backendName:\"webgl\",kernelFunc:ite};function sm(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"complex64\"){let n=bi({inputs:{input:o},backend:t}),s=sm({inputs:{x:n},backend:t}),a=Ip({inputs:{input:o},backend:t}),i=sm({inputs:{x:a},backend:t}),p=Or({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),p}else return Ci({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:t})}var g3={kernelName:Sa,backendName:\"webgl\",kernelFunc:sm};function x3(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string 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yg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Re(n),a=t.map(h=>h[0]).join(\",\"),i=t.map((h,g)=>h[0]+e[g]).join(\",\"),p=Rt(\"rc\",n),u=Rt(\"source\",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?\"source\":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1;\n if(${c}) {\n `,n===1?\"\":`}\n rc = outputLoc;\n ${p[n-2]} += 1;\n if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?\"\":` ${p[n-1]} += 1;\n if(${c}) {`],d=n===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",f=\"\";for(let h=0,g=n===1?2:4;h{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Ci({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new yg(n.shape,s,a):new xg(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},C3={kernelName:es,backendName:\"webgl\",kernelFunc:Dv};var pte=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,cte=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = 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f=w.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var S3={kernelName:os,backendName:\"webgl\",kernelFunc:mte};function dte(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=pD(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],\"int32\",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var I3={kernelName:Hp,backendName:\"webgl\",kernelFunc:dte};function fte(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=cD(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],\"int32\",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var v3={kernelName:Kp,backendName:\"webgl\",kernelFunc:fte};function hte(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=lD(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var k3={kernelName:qp,backendName:\"webgl\",kernelFunc:hte};var Av=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=mD(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},N3={kernelName:ma,backendName:\"webgl\",kernelFunc:Av};var gte=\"return 1.0 / x;\",xte=xe({opSnippet:gte}),T3={kernelName:ns,backendName:\"webgl\",kernelFunc:xte};var yte=Wt+`\n return (x < 0.0) ? 0.0 : x;\n`,bte=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Cte=xe({opSnippet:yte,packedOpSnippet:bte}),_3={kernelName:ss,backendName:\"webgl\",kernelFunc:Cte};var wte=Wt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Ste=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Ite=xe({opSnippet:wte,packedOpSnippet:Ste}),E3={kernelName:us,backendName:\"webgl\",kernelFunc:Ite};var bg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var Cg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function vte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new Cg(n.shape,p,u,s,a):new bg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],\"float32\")}var $3={kernelName:is,backendName:\"webgl\",kernelFunc:vte};var wg=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function kte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new wg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var R3={kernelName:Ja,backendName:\"webgl\",kernelFunc:kte};var Sg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var Ig=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function Nte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new Ig(n.shape,p,u,s,a):new Sg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var D3={kernelName:as,backendName:\"webgl\",kernelFunc:Nte};var vg=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float sourceFracRow =\n float(${p[0]}) *\n (float(dyR) / float(${u[0]}));\n\n float sourceFracCol =\n float(${p[1]}) *\n (float(dyC) / float(${u[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${n}) - 1),\n ${o} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${o} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function Tte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new vg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var A3={kernelName:Za,backendName:\"webgl\",kernelFunc:Tte};var kg=class{constructor(e,t){this.variableNames=[\"x\"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${e[0]} - coord - 1));\n }\n `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,p)=>n(p)).join(\",\"),a=Re(o);this.userCode=`\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var Ng=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Rt(\"rc\",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Re(o);o===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${e[0]} - rc - 1),\n ${e[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),\n ${e[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${i} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${p(n.slice())};\n if(${s}){\n result.g = ${u(n.slice())};\n }\n if(${a}) {\n result.b = ${c(n.slice())};\n if(${s}) {\n result.a = ${l(n.slice())};\n }\n }\n setOutput(result);\n }\n `;function p(f){return m(f)}function u(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",m(f)}function c(f){return f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function l(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function m(f){let h=e.map((b,C)=>d(C,f)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function _te(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return Dt({inputs:{x:n},backend:t});let p=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new Ng(n.shape,i):new kg(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var F3={kernelName:ps,backendName:\"webgl\",kernelFunc:_te};var Tg=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let o=e[1],n=e[2];this.outputShape=e;let s=\"\";typeof t==\"number\"?s=`float outputValue = ${t.toFixed(2)};`:s=`\n vec3 fill = vec3(${t.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var P3={kernelName:Ds,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new Tg(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var Ete=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n`,$te=xe({opSnippet:Ete}),O3={kernelName:cs,backendName:\"webgl\",kernelFunc:$te};var Rte=\"return inversesqrt(x);\",Dte=xe({opSnippet:Rte,cpuKernelImpl:dD}),M3={kernelName:ls,backendName:\"webgl\",kernelFunc:Dte};var Cu=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=t>1?\"strides[j]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${e}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${t}; j++) {\n int index = round(${m});\n flattenedIndex += index * ${x};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${f};\n found = true;\n }\n }\n setOutput(mix(${g}, sum, float(found)));\n }\n `}};var _g=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=t>1?\"strides[j]\":\"strides\",b=t>1?\"strides[j + 1]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n vec4 sum = vec4(0.);\n vec4 found = vec4(0.);\n for (int i = 0; i < ${e}; i+=2) {\n ivec2 flattenedIndex = ivec2(0);\n for (int j = 0; j < ${t}; j+=2) {\n ivec4 index = round(${m});\n flattenedIndex += index.xz * ${x};\n if (j + 1 < ${t}) {\n flattenedIndex += index.yw * ${b};\n }\n }\n if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n vec4 updVals = ${f};\n if (flattenedIndex[0] == coords[0]) {\n sum.xy += updVals.xy;\n found.xy = vec2(1.);\n } else if (flattenedIndex[0] == coords[0] + 1) {\n sum.zw += updVals.xy;\n found.zw = vec2(1.);\n }\n if (flattenedIndex[1] == coords[0]) {\n sum.xy += updVals.zw;\n found.xy = vec2(1.);\n } else if (flattenedIndex[1] == coords[0] + 1) {\n sum.zw += updVals.zw;\n found.zw = vec2(1.);\n }\n }\n }\n setOutput(mix(${g}, sum, found));\n }\n `}};function Ate(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],\"float32\",new Float32Array([0])),g;A().getBool(\"WEBGL_PACK\")?g=new _g(p,i,d.shape.length,f.shape.length,c,m):g=new Cu(p,i,d.shape.length,f.shape.length,c,m);let x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var L3={kernelName:ms,backendName:\"webgl\",kernelFunc:Ate};var Eg=class{constructor(e,t,o,n){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[e,o];let s=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=A().getNumber(\"WEBGL_VERSION\")===2?s:a,p=n===\"left\"?\"<\":\"<=\";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${i}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${p} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}};function Fte(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new Eg(n.shape[0],n.shape[1],s.shape[1],a),p=[[n.shape[1]]];return t.runWebGLProgram(i,[n,s],\"int32\",p)}var B3={kernelName:fs,backendName:\"webgl\",kernelFunc:Fte};var $g=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s=\"resRC\",n=\"resRC\";else{let i=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],p=[],u=[];for(let c=0;c= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}};function Pte(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new $g(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var z3={kernelName:fa,backendName:\"webgl\",kernelFunc:Pte};var Ote=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${w.SELU_SCALEALPHA};\n float scale = ${w.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,Mte=xe({opSnippet:Ote}),V3={kernelName:hs,backendName:\"webgl\",kernelFunc:Mte};var Lte=Fo+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,Bte=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,zte=xe({opSnippet:Lte,packedOpSnippet:Bte,cpuKernelImpl:hD}),W3={kernelName:bs,backendName:\"webgl\",kernelFunc:zte};var Vte=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,Wte=xe({opSnippet:Vte}),U3={kernelName:ys,backendName:\"webgl\",kernelFunc:Wte};var Ute=Fo+`\n return sin(x);\n`,Gte=`\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${Xr}\n return result;\n`,Hte=xe({opSnippet:Ute,packedOpSnippet:Gte}),G3={kernelName:\"Sin\",backendName:\"webgl\",kernelFunc:Hte};var Kte=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,qte=xe({opSnippet:Kte}),H3={kernelName:xs,backendName:\"webgl\",kernelFunc:qte};var jte=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`,Xte=xe({opSnippet:jte}),K3={kernelName:Cs,backendName:\"webgl\",kernelFunc:Xte};var Yte=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeIntermediateTensorInfo(x)),g},q3={kernelName:ga,backendName:\"webgl\",kernelFunc:Yte};function Qte(r){let{inputs:e,backend:t}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=e;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(o.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n ${o.shape}`);if(n.shape.length!==1)throw new Error(`Values must be a vector, saw:\n ${n.shape}`);if(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n ${a.shape}`);let i=t.readSync(o.dataId),p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[l,m,d,f,h]=xD(i,o.shape,o.dtype,p,n.dtype,u,c);return[t.makeTensorInfo(m,o.dtype,l),t.makeTensorInfo([m[0]],n.dtype,d),t.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var j3={kernelName:Ki,backendName:\"webgl\",kernelFunc:Qte};function Zte(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,c,l]=yD(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var X3={kernelName:ei,backendName:\"webgl\",kernelFunc:Zte};function Jte(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=lh(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var Y3={kernelName:ya,backendName:\"webgl\",kernelFunc:Jte};function ere(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=lh(a,o.shape,o.dtype,i,p);return t.makeTensorInfo(c,o.dtype,u)}var Q3={kernelName:ba,backendName:\"webgl\",kernelFunc:ere};function tre(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let x=t.bufferSync(n),b=t.bufferSync(s),C=y.decodeString(t.readSync(a.dataId)[0]),S=fD(x,b,i,m,c,u,p,l,C,d);return t.makeTensorInfo(i,S.dtype,S.values)}let f=new Cu(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=t.runWebGLProgram(f,[s,n,a],s.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(h),g}var Z3={kernelName:vs,backendName:\"webgl\",kernelFunc:tre};function rre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Gs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var J3={kernelName:xa,backendName:\"webgl\",kernelFunc:rre};var eP=\"return sqrt(x);\",ore=xe({opSnippet:eP,packedOpSnippet:eP,cpuKernelImpl:bD}),tP={kernelName:ws,backendName:\"webgl\",kernelFunc:ore};var nre=\"return x * x;\",sre=xe({opSnippet:nre}),rP={kernelName:qi,backendName:\"webgl\",kernelFunc:sre};var oP=\"return (a - b) * (a - b);\",are=nt({opSnippet:oP,packedOpSnippet:oP}),nP={kernelName:ks,backendName:\"webgl\",kernelFunc:are};function ire(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;if(n.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");let s=t.readSync(n.dataId),a=w.fromUint8ToStringArray(s),i=CD(a,\"string\",o);return t.makeTensorInfo(n.shape,\"string\",i)}var sP={kernelName:Ru,backendName:\"webgl\",kernelFunc:ire};function ure({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=Wt+`\n return x > 0.0 ? 1.0 : float(${e.alpha});\n `,s=new tr(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var aP={kernelName:wo,backendName:\"webgl\",kernelFunc:ure};var Rg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=o;let n=o.length,s=Re(o.length),a=Re(o.length),i=\"\";if(n===1)i=\"coords * strides + begin\";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${e});\n ${s} strides = ${s}(${t});\n\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${i}));\n }\n `}};function pre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=pt.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=te({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let $=pt.computeOutShape(b,C,S),R=Gs({inputs:{x:n},backend:t,attrs:{begin:b,size:$}});k=te({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeIntermediateTensorInfo(R)}else if(t.shouldExecuteOnCPU([n])){let R=t.readSync(n.dataId),D=me(n.shape,n.dtype,R),P=wD(d,D,S,b);k=t.makeTensorInfo(f,n.dtype,P.values)}else{let R=new Rg(b,S,d);k=t.runWebGLProgram(R,[n],n.dtype)}let _=te({inputs:{x:k},backend:t,attrs:{shape:f}});return t.disposeIntermediateTensorInfo(k),_}var iP={kernelName:Ns,backendName:\"webgl\",kernelFunc:pre};function cre(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=SD(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var uP={kernelName:Ca,backendName:\"webgl\",kernelFunc:cre};function lre(r){let{inputs:e,backend:t,attrs:o}=r,{skipEmpty:n}=o,{input:s,delimiter:a}=e;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=t.readSync(s.dataId),p=t.readSync(a.dataId)[0],[u,c,l]=ID(i,p,n),m=c.length;return[t.makeTensorInfo([m,2],\"int32\",u),t.makeTensorInfo([m],\"string\",c),t.makeTensorInfo([2],\"int32\",new Int32Array(l))]}var pP={kernelName:ji,backendName:\"webgl\",kernelFunc:lre};function mre(r){let{inputs:e,backend:t,attrs:o}=r,{numBuckets:n}=o,{input:s}=e;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(n<=0)throw new Error(\"Number of buckets must be at least 1\");let a=t.readSync(s.dataId),i=vD(a,n);return t.makeTensorInfo(s.shape,\"int32\",i)}var cP={kernelName:Xi,backendName:\"webgl\",kernelFunc:mre};var dre=\"return tan(x);\",fre=xe({opSnippet:dre}),lP={kernelName:\"Tan\",backendName:\"webgl\",kernelFunc:fre};var hre=`\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,gre=xe({opSnippet:hre}),mP={kernelName:Es,backendName:\"webgl\",kernelFunc:gre};function xre(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return t.makeTensorInfo(n.shape,s.dtype);let d=te({inputs:{x:s},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:a},backend:t,attrs:{shape:[p,u]}}),h=te({inputs:{x:n},backend:t,attrs:{shape:m}}),g=new Cu(p,i,d.shape.length,f.shape.length,c,m,!1,!0),x=t.runWebGLProgram(g,[f,d,h],h.dtype),b=te({inputs:{x},backend:t,attrs:{shape:n.shape}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(x),b}var dP={kernelName:ds,backendName:\"webgl\",kernelFunc:xre};var Dg=class{constructor(e,t){this.variableNames=[\"A\"];let o=new Array(e.length);for(let a=0;a5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],o=[];for(let n=0;n5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=ND(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new Dg(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var fP={kernelName:po,backendName:\"webgl\",kernelFunc:Fv};var Ag=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n `}},Fg=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function kp(r,e){e!==null&&r.disposeIntermediateTensorInfo(e)}function hP(r){let e=1;for(;ep){let P=t.readSync(n.dataId),[O,M]=TD(P,u,n.dtype,s,a);return[t.makeTensorInfo(O.shape,O.dtype,O.values),t.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[t.makeTensorInfo(u,n.dtype,[]),t.makeTensorInfo(u,\"int32\",[])];if(c===1)return[n,Ci({attrs:{shape:u,dtype:\"int32\",value:0},backend:t})];let l=t.texData.get(n.dataId),m=l!==null&&l.isPacked,d=m?t.unpackTensor(n):n,h=y.sizeFromShape(u)/c,g=te({inputs:{x:d},attrs:{shape:[h,c]},backend:t});m&&kp(t,d);let x=hP(s),b=hP(c),C=null,S=()=>C===null?[g,g]:[g,C],k=(P,O,M)=>{let L=S(),B=new Ag(M),U=[[c],[C===null?1:0],[Number.NEGATIVE_INFINITY],[P],[O]],j=C;C=t.runWebGLProgram(B,L,\"int32\",U),kp(t,j)};for(let P=1;P=1;M/=2)k(O,M,[h,b])}for(let P=b;P>x;P/=2){let O=S(),M=new Fg([h,P/2]),B=[[c],[C===null?1:0],[x]],z=C;C=t.runWebGLProgram(M,O,\"int32\",B),kp(t,z);let U=x/2,j=U*2;for(let q=U;q>=1;q/=2)k(j,q,C.shape)}let _=C;C=Gs({inputs:{x:C},backend:t,attrs:{begin:0,size:[h,s]}}),kp(t,_);let $=Tv({inputs:{x:g,indices:C},backend:t,attrs:{axis:1,batchDims:1}});kp(t,g);let R=u.slice(0,-1);R.push(s),_=C,C=te({inputs:{x:C},attrs:{shape:R},backend:t}),kp(t,_);let D=$;return $=te({inputs:{x:$},attrs:{shape:R},backend:t}),kp(t,D),[$,C]}var gP={kernelName:$s,backendName:\"webgl\",kernelFunc:bre};var Pg=class{constructor(e,t,o,n,s,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=o===\"nearest\"?1:2,p;switch(n){case\"constant\":p=1;break;case\"reflect\":p=2;break;case\"wrap\":p=3;break;case\"nearest\":p=4;break;default:p=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${p} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = 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valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function Cre(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Pg(l,m,a,i,p,g);return t.runWebGLProgram(x,[n,s],\"float32\")}var xP={kernelName:Rs,backendName:\"webgl\",kernelFunc:Cre};function wre(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;Vs(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let a=o.readSync(s.dataId),{outputValues:i,outputShape:p,indices:u}=_D(a,n,s.shape,s.dtype);return[o.makeTensorInfo(p,s.dtype,i),o.makeTensorInfo([u.length],\"int32\",u)]}var yP={kernelName:Yi,backendName:\"webgl\",kernelFunc:wre};function Sre(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeIntermediateTensorInfo(h)),f}var bP={kernelName:wa,backendName:\"webgl\",kernelFunc:Sre};var Og=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p=\"0.0\",u=\"sumValue\",c=Math.floor(o/4)*4,l=o%4,m=`\n sumValue += dot(values, segFilter);\n `,d=\"\";s%o>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${p};\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${f}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${a})) * float(${o}));\n int currentSeg = int(mod(float(outIdx), float(${a})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function Ire(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=te({inputs:{x:l},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=oi(n.dtype),g=(S,k,_,$,R)=>{let D=S.shape[0],P=S.shape[1],O=w.segment_util.segOpComputeOptimalWindowSize(P,R),M={windowSize:O,inSize:P,batchSize:D,numSegments:R},L=new Og(M,k),B=t.compileAndRun(L,[S,_],$);if(p.push(B),B.shape[1]===R)return B;let z=Av({backend:t,attrs:{start:0,stop:R,step:1,dtype:\"float32\"}}),U=Fv({inputs:{x:z},backend:t,attrs:{reps:[P/O]}});return p.push(z),p.push(U),g(B,k,U,$,R)},x=g(f,\"unsortedSegmentSum\",s,h,a),b=te({inputs:{x},backend:t,attrs:{shape:m}}),C=b;if(c!=null){p.push(b);let S=w.getUndoAxesPermutation(c);C=bt({inputs:{x:C},backend:t,attrs:{perm:S}})}return 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wu;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(wu||(wu={}));var wP;function kre(r){wP=r.wasm.cwrap(So,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Nre(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e;if(n.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o,m=t.dataIdMap.get(n.dataId).id,d=t.dataIdMap.get(s.dataId).id,f=0;if(a!=null){let R=t.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);f=R.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=wu[c];if(g==null)throw new Error(`${c} 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TP(s,n.length,we[o.dtype],a),o}var _P={kernelName:Uo,backendName:\"wasm\",setupFunc:_re,kernelFunc:Ere};function Np(r){let{inputs:{x:e},backend:t}=r;if(e.dtype===\"string\")return ar(t.readSync(e.dataId),e.shape,e.dtype);let o=t.makeOutput(e.shape,e.dtype),n=t.typedArrayFromHeap(e);return t.typedArrayFromHeap(o).set(n),o}var EP={kernelName:Co,backendName:\"wasm\",kernelFunc:Np};var $P;function $re(r){$P=r.wasm.cwrap(co,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function ho(r){let{inputs:e,backend:t,attrs:o}=r,[n,s]=Dre(e.x.shape,o.perm),a=!0;for(let f=0;f=n&&(s===-1||o[s]>o[a])&&(s=a);o[s]=n}return[t,o]}var RP={kernelName:co,backendName:\"wasm\",kernelFunc:ho,setupFunc:$re};function Tr(r,e,t){let o=r.shape,n=r.shape.length,s=y.parseAxisParam(e,o),a=s,i=w.getAxesPermutation(a,n),p=null,u=!1;if(i!=null){let c=new Array(n);for(let d=0;d`new shape: ${a}, old shape: ${o.shape}. 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voe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:c,dimRoundingMode:l}=t,m=u==null?[1,1]:u,d=w.computeConv2DInfo(n.shape,s.shape,p,m,c,l,!0),f=d.filterHeight,h=d.filterWidth,g=d.padInfo.top,x=d.padInfo.right,b=d.padInfo.bottom,C=d.padInfo.left,S=d.dilationHeight,k=d.dilationWidth,_=d.strideHeight,$=d.strideWidth,R=d.inChannels,D=d.outChannels,P=d.padInfo.type===\"SAME\"?1:0;if(d.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. 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Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=t.makeOutput(s.shape,s.dtype);return BO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var zO={kernelName:Li,backendName:\"wasm\",setupFunc:Eoe,kernelFunc:$oe};var VO;function Roe(r){VO=r.wasm.cwrap(Mi,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Doe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o;if(n.dtype!==s.dtype||n.dtype!==a.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=t.makeOutput(n.shape,n.dtype);return VO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var WO={kernelName:Mi,backendName:\"wasm\",setupFunc:Roe,kernelFunc:Doe};var UO=he(\"Elu\");var GO;function Aoe(r){GO=r.wasm.cwrap(Xa,null,[\"number\",\"number\",\"number\"])}function Foe(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=t.makeOutput(n.shape,\"float32\"),a=i=>t.dataIdMap.get(i.dataId).id;return GO(a(n),a(o),a(s)),s}var HO={kernelName:Xa,backendName:\"wasm\",setupFunc:Aoe,kernelFunc:Foe};var Poe=!1,KO=Ge(xn,Poe,\"bool\");var qO=he(\"Erf\");var jO=he(\"Exp\",\"float32\");function Lg(r){let{inputs:e,attrs:t,backend:o}=r,{input:n}=e,{dim:s}=t,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),zt({inputs:{x:n},backend:o,attrs:{shape:i}})}var XO={kernelName:na,backendName:\"wasm\",kernelFunc:Lg};var YO=he(bn,\"float32\");function Mv(r){let{attrs:{shape:e,value:t},backend:o}=r,{attrs:{dtype:n}}=r;n=n||y.inferDtype(t);let s=o.makeOutput(e,n);return o.typedArrayFromHeap(s).fill(t),s}var QO={kernelName:sa,backendName:\"wasm\",kernelFunc:Mv};var ZO;function Ooe(r){ZO=r.wasm.cwrap(Cn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Moe(r){let{inputs:e,backend:t}=r,{image:o}=e,n=t.makeOutput(o.shape,o.dtype),s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return ZO(s,i,p,u,c,a),n}var JO={kernelName:Cn,backendName:\"wasm\",kernelFunc:Moe,setupFunc:Ooe};var eM=he(wn);var Loe=!1,tM=Ge(Sn,Loe);var rM;function Boe(r){rM=r.wasm.cwrap(In,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function zoe(r){let{backend:e,inputs:t,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=t,c=e.dataIdMap.get(s.dataId).id,l=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,d=p!=null?e.dataIdMap.get(p.dataId).id:0,f=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return rM(c,l,m,d,f,n,g),h}var oM={kernelName:In,backendName:\"wasm\",setupFunc:Boe,kernelFunc:zoe};var nM;function Voe(r){nM=r.wasm.cwrap(Io,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Woe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=wu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,$=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return nM(x,j,q,Y,b,k,_,S,$,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var sM={kernelName:Io,backendName:\"wasm\",setupFunc:Voe,kernelFunc:Woe};var aM;function Uoe(r){aM=r.wasm.cwrap(vo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Goe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m,!0),g=wu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,$=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return aM(x,j,q,Y,b,k,_,S,$,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var iM={kernelName:vo,backendName:\"wasm\",setupFunc:Uoe,kernelFunc:Goe};var uM;function Hoe(r){uM=r.wasm.cwrap(vn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Koe(r){let{backend:e,inputs:t}=r,{params:o,indices:n}=t,[s,a,i,p]=af.prepareAndValidate(o,n),u=e.makeOutput(s,o.dtype);if(a===0)return u;let c=n.shape,l=c[c.length-1],d=e.dataIdMap.get(o.dataId).id,h=e.dataIdMap.get(n.dataId).id,g=new Uint8Array(new Int32Array(p).buffer),x=e.dataIdMap.get(u.dataId).id;return uM(d,we[o.dtype],h,a,l,i,g,x),u}var pM={kernelName:vn,backendName:\"wasm\",setupFunc:Hoe,kernelFunc:Koe};var cM;function qoe(r){cM=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function joe(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,indices:s}=t,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=e.readSync(s.dataId),c=n.shape[p];for(let D=0;D=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let l=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=zt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:e}),d=y.sizeFromShape(s.shape),f=zt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:e}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=e.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,C=e.dataIdMap.get(m.dataId).id,k=e.dataIdMap.get(f.dataId).id,_=e.dataIdMap.get(g.dataId).id,$=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return cM(C,we[n.dtype],$,x,k,l.batchSize,R,_),e.disposeData(m.dataId),e.disposeData(f.dataId),g.shape=l.outputShape,g}var lM={kernelName:aa,backendName:\"wasm\",setupFunc:qoe,kernelFunc:joe};var Xoe=!1,mM=Ge(kn,Xoe,\"bool\");var Yoe=!1,dM=Ge(Nn,Yoe,\"bool\");var fM=he(Tn,\"bool\");var hM=he(_n,\"bool\");var gM=he(En,\"bool\");var xM;function Qoe(r){xM=r.wasm.cwrap($n,null,[\"number\",\"number\",\"number\",\"number\"])}function Zoe(r){let{inputs:{x:e},attrs:{alpha:t},backend:o}=r,n=o.dataIdMap.get(e.dataId).id,s=o.makeOutput(e.shape,\"float32\");if(y.sizeFromShape(e.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;xM(n,we[e.dtype],t,a)}return s}var yM={kernelName:$n,backendName:\"wasm\",setupFunc:Qoe,kernelFunc:Zoe};var Joe=!1,bM=Ge(Rn,Joe,\"bool\");var ene=!1,CM=Ge(Dn,ene,\"bool\");var wM;function tne(r){wM=r.wasm.cwrap(An,null,[\"number\",\"number\",\"number\",\"number\"])}function rne(r){let{attrs:e,backend:t}=r,{start:o,stop:n,num:s}=e,a=Math.floor(s),i=t.makeOutput([a],\"float32\");return wM(t.dataIdMap.get(i.dataId).id,o,n,a),i}var SM={kernelName:An,backendName:\"wasm\",setupFunc:tne,kernelFunc:rne};var IM=he(\"Log\");var vM=he(Pn);var one=!1,kM=Ge(On,one,\"bool\");var NM=he(Mn);var nne=!1,TM=Ge(Ln,nne,\"bool\");var sne=!1,_M=Ge(R0,sne,\"bool\");var EM;function ane(r){EM=r.wasm.cwrap(\"LRN\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ine(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o;if(n.dtype!==\"float32\")throw new Error(\"LRN error: x must have dtype float32\");let u=t.makeOutput(n.shape,n.dtype);return EM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var $M={kernelName:\"LRN\",backendName:\"wasm\",setupFunc:ane,kernelFunc:ine};var RM;function une(r){RM=r.wasm.cwrap(Ya,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function pne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o;if(n.dtype!==\"float32\"||s.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"LRNGrad error: x, y, and dy must have dtype float32\");let l=t.makeOutput(n.shape,n.dtype);return RM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,a.shape[3],i,p,u,c),l}var DM={kernelName:Ya,backendName:\"wasm\",setupFunc:une,kernelFunc:pne};var AM;function cne(r){AM=r.wasm.cwrap(\"Max\",null,[\"number\",\"number\",\"number\",\"number\"])}function lne(r){let{backend:e,inputs:t,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=t,p=e.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e);if(d){let C=e.dataIdMap.get(c.dataId).id;u=c,p=C}let f=u.shape.length;w.assertAxesAreInnerMostDims(\"max\",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;AM(p,we[a.dtype],x,C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var FM={kernelName:\"Max\",backendName:\"wasm\",setupFunc:cne,kernelFunc:lne};var mne=!1,PM=Ge(Vn,mne);var OM;function dne(r){OM=r.wasm.cwrap(Wn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function fne(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=t,c=w.computePool2DInfo(n.shape,a,i,1,p,u),l=c.filterHeight,m=c.filterWidth,d=c.padInfo.top,f=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,C=c.strideHeight,S=c.strideWidth,k=c.inChannels,_=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let $=o.makeOutput(c.outShape,\"float32\"),R=o.dataIdMap.get($.dataId).id;return OM(s,n.shape[0],n.shape[1],n.shape[2],l,m,d,f,h,g,x,b,C,S,k,_,R),$}var MM={kernelName:Wn,backendName:\"wasm\",setupFunc:dne,kernelFunc:fne};var LM;function hne(r){LM=r.wasm.cwrap(\"MaxPool3D\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function gne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=w.computePool3DInfo(n.shape,s,a,1,i,p,u),l=t.makeOutput(c.outShape,n.dtype);return LM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var BM={kernelName:ia,backendName:\"wasm\",setupFunc:hne,kernelFunc:gne};var zM;function xne(r){zM=r.wasm.cwrap(\"MaxPool3DGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function yne(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=t.makeOutput(s.shape,s.dtype);return zM(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var VM={kernelName:Gi,backendName:\"wasm\",setupFunc:xne,kernelFunc:yne};var WM;function bne(r){WM=r.wasm.cwrap(\"MaxPoolGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Cne(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool2DInfo(s.shape,a,i,1,p,u),l=t.makeOutput(s.shape,s.dtype);return WM(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),l}var UM={kernelName:Ui,backendName:\"wasm\",setupFunc:bne,kernelFunc:Cne};var GM;function wne(r){GM=r.wasm.cwrap(\"MaxPoolWithArgmax\",null,[\"number\",\"number\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Sne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,includeBatchInIndex:p}=o;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,[1,1],i),l=t.makeOutput(c.outShape,n.dtype),m=t.makeOutput(c.outShape,\"int32\");return GM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,t.dataIdMap.get(m.dataId).id,we[n.dtype],p,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[l,m]}var HM={kernelName:ua,backendName:\"wasm\",setupFunc:wne,kernelFunc:Sne};var KM;function Ine(r){KM=r.wasm.cwrap(Un,null,[\"number, number, number\"])}function vne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e),f=l;if(d){let S=e.dataIdMap.get(c.dataId).id;S!==i&&(u=c,p=S,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims(\"mean\",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!==\"float32\"&&(b=Mr({backend:e,inputs:{x:u},attrs:{dtype:\"float32\"}}),p=e.dataIdMap.get(b.dataId).id);let C=e.makeOutput(h,\"float32\");if(y.sizeFromShape(u.shape)!==0){let S=e.dataIdMap.get(C.dataId).id;KM(p,x,S)}if(d&&e.disposeData(c.dataId),s){let S=w.expandShapeToKeepDim(C.shape,m);C.shape=S}return u.dtype!==\"float32\"&&e.disposeData(b.dataId),C}var qM={kernelName:Un,backendName:\"wasm\",setupFunc:Ine,kernelFunc:vne};var jM;function kne(r){jM=r.wasm.cwrap(\"Min\",null,[\"number\",\"number\",\"number\",\"number\"])}function Nne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e);if(d){let 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CL={kernelName:rs,backendName:\"wasm\",setupFunc:jne,kernelFunc:Xne};var wL;function Yne(r){wL=r.wasm.cwrap(os,null,[\"number\",\"number\",\"number\",\"number\"])}function Qne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e),f=l;if(d){let C=e.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims(\"prod\",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;wL(p,x,we[b.dtype],C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var SL={kernelName:os,backendName:\"wasm\",setupFunc:Yne,kernelFunc:Qne};var Zne=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=up(o,n,s,a),p=e.makeOutput([i.length],a);return 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PL={kernelName:Za,backendName:\"wasm\",setupFunc:ase,kernelFunc:ise};var OL;function use(r){OL=r.wasm.cwrap(ps,null,[\"number\",\"array\",\"number\",\"array\",\"number\",\"number\"])}function pse(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=y.parseAxisParam(s,n.shape);if(n.shape.length===0)return Np({inputs:{x:n},backend:t});let i=t.makeOutput(n.shape,n.dtype),p=t.dataIdMap.get(n.dataId).id,u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),l=new Uint8Array(new Int32Array(n.shape).buffer);OL(p,c,a.length,l,n.shape.length,u);let m=zt({inputs:{x:i},attrs:{shape:n.shape},backend:t});return t.disposeData(i.dataId),m}var ML={kernelName:ps,backendName:\"wasm\",kernelFunc:pse,setupFunc:use};var LL;function cse(r){LL=r.wasm.cwrap(Ds,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\"])}function 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find the buffer in buffer manager\");a[i]=a[a.length-1],a.pop(),this.numUsedBuffers--,this.numBytesUsed-=o,t?(this.freeBuffers.get(s).push(e),this.numFreeBuffers++):(e.destroy(),this.numBytesAllocated-=o)}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function YB(r,e){return`${r}_${e}`}var qg=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,o,n){let s=ZB(o),a=e*t*s,i=QB(e,t,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[e,t],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(e){if(this.freeTextures.size===0)return;let t=e.width,o=e.height,n=e.format,s=e.usage,a=QB(t,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(e);if(p<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");i.splice(p,1);let u=ZB(n),c=t*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function QB(r,e,t,o){return`${r}_${e}_${t}_${o}`}function ZB(r){if(r===\"rgba8unorm\")return 16;throw new Error(`${r} is not supported!`)}function JB(r,e){if(Math.max(...r)>5)throw new Error(\"Cannot symbolically compute strides for rank > 6 tensor.\");let t=r.length,o=\"xyzwuv\",n=r.map(a=>`${e}.${o[a]}`),s=new Array(t-1);s[t-2]=n[t-1];for(let a=t-3;a>=0;--a)s[a]=`(${s[a+1]} * ${n[a+1]})`;return s}var Qr=(r,e,t)=>t===\"int32\"?`atomicAdd(${r}, bitcast(${e}));`:`\n {\n var oldValue = 0;\n loop {\n let newValueF32 = bitcast(oldValue) + (${e});\n let newValue = bitcast(newValueF32);\n let res = atomicCompareExchangeWeak(${r}, oldValue, newValue);\n if res.exchanged {\n break;\n }\n oldValue = res.old_value;\n }\n }`;var wi;(function(r){r[r.FROM_PIXELS=0]=\"FROM_PIXELS\",r[r.DRAW=1]=\"DRAW\"})(wi||(wi={}));var oz=(r,e,t,o,n)=>{let s={dtype:o.dtype,shape:o.shape},a=cae(t,s,e),i=r.createShaderModule({code:a,label:e.constructor.name}),p=A().get(\"WEBGPU_PRINT_SHADER\");if(p!==\"\"){p=p.toLowerCase();let u=p.split(\",\");(p===\"all\"||u.some(c=>e.shaderKey.toLowerCase().includes(c)))&&(console.group(e.shaderKey),console.debug(a),console.groupEnd())}return n?r.createComputePipelineAsync({compute:{module:i,entryPoint:\"_start\"},label:e.constructor.name,layout:\"auto\"}):r.createComputePipeline({compute:{module:i,entryPoint:\"_start\"},label:e.constructor.name,layout:\"auto\"})},Ae=(r,e=\"f32\")=>{switch(r){case 1:return`${e}`;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${r}-component ${e} is not supported.`)}};function ft(r){if(r<=1)return\"i32\";if(r===2)return\"vec2\";if(r===3)return\"vec3\";if(r===4)return\"vec4\";if(r===5)return\"vec5\";if(r===6)return\"vec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Oo(r){if(r===0)return\"x\";if(r===1)return\"y\";if(r===2)return\"z\";if(r===3)return\"w\";if(r===4)return\"u\";if(r===5)return\"v\";throw Error(`Index ${r} is not yet supported`)}function G(...r){let e;switch(r.length){case 0:e=`\n fn main()\n `;break;case 1:e=`\n fn main(${r[0]} : i32)\n `;break;default:throw Error(\"Unreachable\")}return e}function ez(r,e){let t;return t=`\n ${pae(e)}\n fn _start(@builtin(local_invocation_id) LocalId : vec3,\n @builtin(global_invocation_id) GlobalId : vec3,\n @builtin(local_invocation_index) LocalIndex: u32,\n @builtin(workgroup_id) WorkgroupId : vec3,\n @builtin(num_workgroups) NumWorkgroups : vec3) {\n localId = LocalId;\n localIndex = LocalIndex;\n globalId = GlobalId;\n numWorkgroups = NumWorkgroups;\n workgroupId = WorkgroupId;\n ${r?\"main(getGlobalIndex());\":\"main();\"};\n }\n `,t}function pae(r){return`\n @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]})\n`}function cae(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(t.outputComponent=t.outputComponent?t.outputComponent:1,o.push(`\n\n var localId: vec3;\n var localIndex: u32;\n var globalId: vec3;\n var numWorkgroups: vec3;\n var workgroupId: vec3;\n\n // Only used when the y/z dimension of workgroup size is 1.\n fn getGlobalIndex() -> i32 {\n ${sz(t)?\" return i32(globalId.x);\":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +\n workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u +\n localIndex);\n `}\n }\n `),t.pixelsOpType!=null){let f=t.pixelsOpType===wi.FROM_PIXELS?`@group(0) @binding(0) var result: array<${Su(e.dtype,t.outputComponent)}>;`:`@group(0) @binding(1) var inBuf : array<${Su(r[0].dtype,t.outputComponent)}>;`,h=e.shape.length===3?\"vec2\":\"i32\";o.push(`\n struct Uniform {\n outShapeStrides : ${h},\n size : i32,\n numChannels : i32,\n alpha : f32,\n };\n\n ${f}\n @group(0) @binding(2) var uniforms: Uniform;\n `);let g=rz(t);return[tz,o.join(`\n`),cm(e.shape),t.getUserCode(),ez(g,t)].join(`\n`)}let s,a,i=\"struct Uniforms { NAN : f32, INFINITY : f32, \";t.variableNames.forEach((f,h)=>{let g=ft(r[h].shape.length);i+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `,s=r[h].shape.length-1,a=ft(s),i+=`${f.charAt(0).toLowerCase()+f.slice(1)}ShapeStrides: ${a}, `});let p=ft(e.shape.length);i+=`outShape : ${p}, `,s=e.shape.length-1,a=ft(s),i+=`\n outShapeStrides: ${a}, `,t.size&&(i+=\"size : i32, \"),t.uniforms&&(i+=t.uniforms),i+=\"};\",i=yae(i),o.push(i),t.atomic?o.push(`\n @group(0) @binding(0) var result: array>;\n `):o.push(`\n @group(0) @binding(0) var result: array<${Su(e.dtype,t.outputComponent)}>;\n `),t.variableNames.forEach((f,h)=>{o.push(`\n @group(0) @binding(${1+h}) var ${f}: array<${t.variableComponents?Su(r[h].dtype,t.variableComponents[h]):Su(r[h].dtype,t.outputComponent)}>;\n `)}),i!==\"\"&&o.push(`\n @group(0) @binding(${1+t.variableNames.length}) var uniforms: Uniforms;\n `);let u=hae(e.shape,t.dispatchLayout),c=[tz,o.join(`\n`)+lae,cm(e.shape),u,gae(e.shape.length)];t.atomic||c.push(xae(e.shape,e.dtype,t.outputComponent)),t.variableNames.forEach((f,h)=>{c.push(`${cm(r[h].shape,f)}`)});let l=r.map((f,h)=>fae(f,e.shape,t.variableComponents?t.variableComponents[h]:t.outputComponent,t.dispatchLayout.x.length===e.shape.length)).join(`\n`);c.push(l),c.push(t.getUserCode());let m=rz(t);return c.push(ez(m,t)),c.join(`\n`)}function nz(r,e,t){let o=r.shaderKey;if(r.pixelsOpType!=null)return o;let n=[],s=[];e.forEach(c=>{n.push(c.shape),s.push(c.dtype)}),n.push(t.shape),s.push(t.dtype);let a=e.map(c=>w.getBroadcastDims(c.shape,t.shape)),i=e.map(c=>y.arraysEqual(c.shape,t.shape)).join(\"_\"),p=a.map(c=>c.join(\"_\")).join(\";\"),u=sz(r)?\"flatDispatch\":\"\";return o+=\"_\"+(r.workgroupSize?r.workgroupSize.join(\",\"):\"\")+n.map(c=>c.length).join(\",\")+s.join(\",\")+r.variableNames.join(\",\")+p+i+u,o}var tz=`\n struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\n // Checks whether coordinates lie within the bounds of the shape.\n fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool {\n return all(coord >= vec2(0)) && all(coord < shape);\n }\n fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool {\n return all(coord >= vec3(0)) && all(coord < shape);\n }\n fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool {\n return all(coord >= vec4(0)) && all(coord < shape);\n }\n\n fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n return coord;\n }\n fn getIndexFromCoords2D(coords : vec2, shape : vec2) -> i32 {\n return dot(coords, vec2(shape.y, 1));\n }\n fn getIndexFromCoords3D(coords : vec3, shape : vec3) -> i32 {\n return dot(coords, vec3(shape.y * shape.z, shape.z, 1));\n }\n fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n }\n fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n }\n fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n }\n\n // NaN defination in IEEE 754-1985 is :\n // - sign = either 0 or 1.\n // - biased exponent = all 1 bits.\n // - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n fn isnan(val: f32) -> bool {\n let floatToUint: u32 = bitcast(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n fn isnanVec4(val : vec4) -> vec4 {\n let floatToUint: vec4 = bitcast>(val);\n return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u);\n }\n`,lae=`\n fn isinf(val: f32) -> bool {\n return abs(val) == uniforms.INFINITY;\n }\n`;function cm(r,e=\"\"){let t=r.length,o=e!==\"\"?`get${e.charAt(0).toUpperCase()+e.slice(1)}CoordsFromIndex`:\"getCoordsFromIndex\",n=e!==\"\"?`${e.charAt(0).toLowerCase()+e.slice(1)}ShapeStrides`:\"outShapeStrides\";if(t<=1)return`fn ${o}(index : i32) -> i32 { return index; }`;let s=y.computeStrides(r),a=ft(t),i=[];for(let u=0;u vec2 {\n let d0 = index / uniforms.${n}; let d1 = index - d0 * uniforms.${n};\n return vec2(d0, d1);\n }`;let p;return p=\"var index2 = index;\"+s.map((u,c)=>{let l=`let ${i[c]} = index2 / uniforms.${n}.${Oo(c)}`,m=c===s.length-1?`let ${i[c+1]} = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`:`index2 = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`;return`${l}; ${m};`}).join(\"\"),`\n fn ${o}(index : i32) -> ${a} {\n ${p}\n return ${a}(${i.join(\",\")});\n }\n `}function mae(r,e){let t=r.name,o=r.shape.length,n=ft(o),s=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=a.map(c=>`${c} : i32`).join(\", \");if(o<1)return`\n fn ${s}() -> ${Ae(e)} {\n return ${Ae(e)}(${t}[0]);\n }\n `;let p=`uniforms.${t.charAt(0).toLowerCase()+t.slice(1)}Shape`,u=`${o}D`;return o===0&&(u=\"1D\"),`\n fn ${s}(${i}) -> ${Ae(e)} {\n return ${Ae(e)}(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p})${e===1?\"\":` / ${e}`}]);\n }\n `}function dae(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a=\"get\"+s+\"ByOutput\",i=r.shape.length,p=e.length,u=ft(p);if(y.arraysEqual(r.shape,e)&&o)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)} {\n return ${Ae(t)}(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(t)} {\n return ${Ae(t)}(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}${t===1?\"\":` / ${t}`}]);\n }\n `;let c=w.getBroadcastDims(r.shape,e),l=p-i,m=\"\";if(i===0)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)}{\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(t)}{\n return get${s}();\n }\n `;p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(g=>`coords.${Oo(g+l)} = 0;`).join(`\n`);let d=\"\";if(p<2&&i>0)d=\"coords\";else if(p>1){let g=ft(i),x=r.shape.map((b,C)=>`coords.${Oo(C+l)}`).join(\", \");d=`${g}(${x})`}else d=\"coords\";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)} {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return ${Ae(t)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t===1?\"\":` / ${t}`}]);\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> ${Ae(t)} {\n var coords = coordsIn;\n ${m}\n return ${Ae(t)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t===1?\"\":` / ${t}`}]);\n }\n`}function fae(r,e,t,o){let n=mae(r,t);return r.shape.length<=e.length&&(n+=dae(r,e,t,o)),n}function hae(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return\"\";if(t.length===s)return`fn getOutputCoords() -> ${ft(s)}{\n let globalIndex = getGlobalIndex();\n return getCoordsFromIndex(globalIndex);\n }\n `;let i=\"\",p=[t,o,n];for(let m=0;m ${c} {\n ${i}\n`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(\",\")}); }`,l}function gae(r){let e=\"\";switch(r){case 0:case 1:e+=`\n fn getOutputIndexFromCoords(coords : i32) -> i32 {\n return coords;\n }\n `;break;case 2:e+=`\n fn getOutputIndexFromCoords(coords : vec2) -> i32 {\n return dot(coords, vec2(uniforms.outShapeStrides, 1));\n }\n `;break;case 3:e+=`\n fn getOutputIndexFromCoords(coords : vec3) -> i32 {\n return dot(coords, vec3(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n }\n `;break;case 4:e+=`\n fn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n }\n `;break;case 5:e+=`\n fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u;\n }\n `;break;case 6:e+=`\n fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u * uniforms.outShapeStrides.u +\n coords.v;\n }\n `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function sz(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function Su(r,e=1){if(r===\"float32\")return Ae(e,\"f32\");if(r===\"int32\"||r===\"bool\")return Ae(e,\"i32\");throw new Error(`type ${r} is not supported.`)}function xae(r,e,t){let o=r.length,n=Su(e,t),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Ae(t)}) {\n result[flatIndex] = ${n}(value);\n }\n\n fn setOutputAtIndexI32(flatIndex : i32, value : ${Ae(t,\"i32\")}) {\n result[flatIndex] = ${n}(value);\n }\n `;if(o>=2){let a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=ft(o);s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(t)}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex${t===1?\"\":` / ${t}`}, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(t,\"i32\")}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex${t===1?\"\":` / ${t}`}, value);\n }\n `}return s}function yae(r){let e=/(\\w+)\\s*:\\s*vec(5|6)/g;r=r.replace(e,o=>\"@align(16) \"+o);let t=/vec(5|6)\\s*,\\s*(\\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function rz(r){return!(r.dispatchLayout.hasOwnProperty(\"y\")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty(\"z\")&&r.dispatchLayout.z.length!==0)}var Zv={};qe(Zv,{GPUBytesPerElement:()=>jg,MatMulProgramType:()=>Mo,assertNotComplex:()=>fm,computeDispatch:()=>H,computeWorkPerThreadForConv2d:()=>mm,computeWorkgroupInfoForMatMul:()=>Qv,computeWorkgroupSizeForConv2d:()=>lm,flatDispatchLayout:()=>X,isWebGPUSupported:()=>dm,tilesFitEvenlyIntoShape:()=>Cae});var Tp=r=>{let e=1;for(let t=0;tt%r[o]===0)}function H(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(Tp(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(Tp(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(Tp(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function Qv(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function lm(r,e,t=!1){if(t)return[8,8,1];let o=Tp(r.x.map(s=>e[s])),n=Tp(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function mm(r,e,t=!1){if(t)return[4,4,1];let o=Tp(r.x.map(s=>e[s])),n=Tp(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function X(r){return{x:r.map((e,t)=>t)}}function jg(r){if(r===\"float32\"||r===\"int32\"||r===\"bool\"||r===\"string\")return 4;if(r===\"complex64\")return 8;throw new Error(`Unknown dtype ${r}`)}function dm(){return!!(typeof globalThis!=\"undefined\"&&globalThis.navigator&&globalThis.navigator.gpu)}function fm(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!==\"complex64\",()=>`${e} does not support complex64 tensors in the WebGPU backend.`)})}var Mo;(function(r){r[r.MatMulReduceProgram=0]=\"MatMulReduceProgram\",r[r.MatMulSplitKProgram=1]=\"MatMulSplitKProgram\",r[r.MatMulSmallOutputSizeProgram=2]=\"MatMulSmallOutputSizeProgram\",r[r.MatMulPackedProgram=3]=\"MatMulPackedProgram\",r[r.MatMulMax=4]=\"MatMulMax\"})(Mo||(Mo={}));var wae=A().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),Sae=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[s,s,s]):[s,s,1]},jc=class r extends ao{nextDataId(){return r.nextDataId++}constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchCountInPass=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.queryResolveBuffer=null,this.querySet=null,this.querySetCount=2,this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,this.hasReadSyncWarned=!1,this.hasTimestampQueryWarned=!1,!dm())throw new Error(\"WebGPU is not supported on this device\");this.pipelineCache={},this.device=e,this.queue=e.queue,this.commandEncoder=null,this.computePassEncoder=null,this.adapterInfo=new Hg(t),this.supportTimestampQuery=this.device.features.has(\"timestamp-query\"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Kg(this.device),this.textureManager=new qg(this.device),this.tensorMap=new Bo(this,ur()),A().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}disposeData(e,t=!1){if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);return t?o.refCount=0:o.refCount--,o.refCount>0?!1:(o.complexTensorInfos!=null&&(this.disposeData(o.complexTensorInfos.real.dataId),this.disposeData(o.complexTensorInfos.imag.dataId)),this.commandQueueOwnedIds.has(e)?(this.tensorDataPendingDisposal.push(e),!0):(this.releaseResource(e),this.tensorMap.delete(e),!0))}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resource)){if(t.external){t.resource=null;return}t.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(t.resource):t.resource instanceof GPUTexture&&this.textureManager.releaseTexture(t.resource),t.resource=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let e;try{e=await Promise.all(Object.values(this.pipelineCache))}catch(t){throw new Error(t.message)}Object.keys(this.pipelineCache).map((t,o)=>{this.pipelineCache[t]=e[o]})}async getBufferData(e){if(A().getBool(\"WEBGPU_ENGINE_COMPILE_ONLY\"))return console.warn(\"The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false\"),null;let t=e.size,o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o),A().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(y.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o,complexTensorInfos:n}=t;if(o!=null||t.dtype===\"string\")return o;if(t.dtype===\"complex64\"){let h=this.readSync(n.real.dataId),g=this.readSync(n.imag.dataId),x=y.convertBackendValuesAndArrayBuffer(w.mergeRealAndImagArrays(h,g).buffer,\"float32\");return this.convertAndCacheOnCPU(e,x),x}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn(\"The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead.\"));let s=[\"opaque\",\"premultiplied\"],a=t.resource,i=a.size;y.assert(i%4===0,()=>\"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.\");let p=i/4,u=new ArrayBuffer(i),c=256,l=256,m=s.map(h=>new OffscreenCanvas(c,l)),d=new OffscreenCanvas(c,l);this.endComputePassEncoder(),m.map((h,g)=>{let x=h.getContext(\"webgpu\");return x.configure({device:this.device,format:\"bgra8unorm\",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),x.getCurrentTexture()}).map((h,g)=>{let x=c*4,b=(R,D,P)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:a,bytesPerRow:x,offset:P},{texture:h},{width:R,height:D}),this.submitQueue();let O=d.getContext(\"2d\",{willReadFrequently:!0});O.clearRect(0,0,R,D),O.drawImage(m[g],0,0);let M=O.getImageData(0,0,R,D).data,L=s[g],B=new Uint8ClampedArray(u,P,R*D*4);for(let z=0;z0&&(b(S,k,_),_+=k*(c*4)),S=$%c,S>0&&b(S,1,_)});let f=y.convertBackendValuesAndArrayBuffer(u,t.dtype);return this.convertAndCacheOnCPU(e,f),f}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return o;let n;if(t.dtype===\"complex64\"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=w.mergeRealAndImagArrays(a,i)}else{let s=await this.getBufferData(t.resource);n=y.convertBackendValuesAndArrayBuffer(s,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e){let t=e.size,o=e.usage,n=this.bufferManager.acquireBuffer(t,o);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,o){let n=e.buffer;if(o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. \");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:t,values:null,refCount:1,external:e.zeroCopy});let a=this.tensorMap.get(s),i=jg(a.dtype)*y.sizeFromShape(a.shape);if(e.buffer.sizey.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(e.shape,e.dtype,t)}async time(e){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn(\"This device doesn't support timestamp-query extension. Start Chrome browser with flag --enable-dawn-features=allow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled.\"),this.hasTimestampQueryWarned=!0);let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let o=this.tensorMap.get(e.dataId).resource;return o instanceof GPUBuffer?{buffer:o}:o instanceof GPUTexture?o.createView():o}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resource!=null)return;let o=jg(t.dtype)*y.sizeFromShape(t.shape),n,s=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(t.values){if(n=this.bufferManager.acquireBuffer(o,s,!0),n.mapState===\"unmapped\"){let a=this.bufferManager.acquireBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),i=a.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(i).set(t.values):new Float32Array(i).set(t.values),a.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(a,0,n,0,o),this.stagingPendingDisposal.push(a)}else{let a=n.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),n.unmap()}t.values=null}else n=this.bufferManager.acquireBuffer(o,s);t.resource=n}makeUniforms(e){let t=0,o=0,n=[],s=1;e.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),u>s&&(s=u),t=Math.ceil(t/u)*u,o=p.data.length,n.push(t),t+=p.data.length*4}),t=Math.ceil(t/s)*s;let a=new ArrayBuffer(t);e.forEach((p,u)=>{let c=n[u];p.type===\"int32\"?new Int32Array(a,c,p.data.length).set(p.data):p.type===\"uint32\"?new Uint32Array(a,c,p.data.length).set(p.data):new Float32Array(a,c,p.data.length).set(p.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(i,0,a,0,t),this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=Sae(this.device,e);let a=t.map((p,u)=>{if(p.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(p.dataId),{dtype:this.tensorMap.get(p.dataId).dtype,shape:p.shape,name:e.variableNames[u]}});e.shaderKey=nz(e,a,s);let i=A().getBool(\"WEBGPU_ENGINE_COMPILE_ONLY\");return e.shaderKey in this.pipelineCache||(this.pipelineCache[e.shaderKey]=oz(this.device,e,a,s,i)),e.pipeline=this.pipelineCache[e.shaderKey],i||this.recordAndSubmit(e,s,t,n),s}recordAndSubmit(e,t,o,n){if(e.pipeline instanceof Promise)throw new Error(\"Please call checkCompileCompletionAsync to ensure parallel compilation is done!\");let s=[],a=[],i=\"int32\";if(e.pixelsOpType==null){s.push({type:\"float32\",data:[NaN]},{type:\"float32\",data:[1/0]}),a=o.concat(t).map(d=>d.shape);let m=\"int32\";a.map(d=>{s.push({type:m,data:d});let f=y.computeStrides(d);s.push({type:m,data:f})})}else{let m=y.computeStrides(t.shape);s.push({type:i,data:m})}if(e.size){let m=y.sizeFromShape(e.outputShape);s.push({type:i,data:[e.outputComponent?m/e.outputComponent:m]})}n&&(s=[...s,...n]);let p=[this.tensorToBinding(t),...o.map(m=>this.tensorToBinding(m)),this.makeUniforms(s)];o.forEach(m=>{this.commandQueueOwnedIds.add(m.dataId)}),this.commandQueueOwnedIds.add(t.dataId);let u=this.device.createBindGroup({layout:e.pipeline.getBindGroupLayout(0),entries:p.map((m,d)=>({binding:d,resource:m}))}),c=this.activeTimers!=null;this.ensureCommandEncoderReady();let l={};c&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:this.querySetCount})),l.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:0,endOfPassWriteIndex:1},this.computePassEncoder=this.commandEncoder.beginComputePass(l)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(l)),this.computePassEncoder.setPipeline(e.pipeline),this.computePassEncoder.setBindGroup(0,u),this.computePassEncoder.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),this.dispatchCountInPass++,(c||A().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchCountInPass||e.pixelsOpType===wi.DRAW)&&(this.endComputePassEncoder(),c?this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let e=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.querySetCount*8),this.submitQueue(),await e.mapAsync(GPUMapMode.READ);let t=new BigUint64Array(e.getMappedRange()),o=Number(t[1]-t[0])/1e6;return e.unmap(),this.bufferManager.releaseBuffer(e),o}shouldExecuteOnCPU(e,t=wae){return A().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(o=>this.tensorMap.get(o.dataId).resource==null&&y.sizeFromShape(o.shape){let r={powerPreference:A().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},e=await navigator.gpu.requestAdapter(r),t={},o=[];e.features.has(\"timestamp-query\")&&o.push(\"timestamp-query\"),e.features.has(\"bgra8unorm-storage\")&&o.push([\"bgra8unorm-storage\"]),t.requiredFeatures=o;let n=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize,maxBufferSize:n.maxBufferSize,maxComputeWorkgroupSizeX:n.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:n.maxComputeInvocationsPerWorkgroup};let s=await e.requestDevice(t),a=\"info\"in e?e.info:\"requestAdapterInfo\"in e?await e.requestAdapterInfo():void 0;return new jc(s,a)},3);var fe;(function(r){r[r.ADD=0]=\"ADD\",r[r.ATAN2=1]=\"ATAN2\",r[r.COMPLEX_MULTIPLY_IMAG=2]=\"COMPLEX_MULTIPLY_IMAG\",r[r.COMPLEX_MULTIPLY_REAL=3]=\"COMPLEX_MULTIPLY_REAL\",r[r.DIV=4]=\"DIV\",r[r.ELU_DER=5]=\"ELU_DER\",r[r.EQUAL=6]=\"EQUAL\",r[r.FLOOR_DIV=7]=\"FLOOR_DIV\",r[r.GREATER=8]=\"GREATER\",r[r.GREATER_EQUAL=9]=\"GREATER_EQUAL\",r[r.LESS=10]=\"LESS\",r[r.LESS_EQUAL=11]=\"LESS_EQUAL\",r[r.LOGICAL_AND=12]=\"LOGICAL_AND\",r[r.LOGICAL_OR=13]=\"LOGICAL_OR\",r[r.MAX=14]=\"MAX\",r[r.MIN=15]=\"MIN\",r[r.MOD=16]=\"MOD\",r[r.MUL=17]=\"MUL\",r[r.NOT_EQUAL=18]=\"NOT_EQUAL\",r[r.POW=19]=\"POW\",r[r.PRELU=20]=\"PRELU\",r[r.SQUARED_DIFFERENCE=21]=\"SQUARED_DIFFERENCE\",r[r.SUB=22]=\"SUB\"})(fe||(fe={}));var Iae=\"let resultTemp = a + b;\",vae=\"let resultTemp = atan2(a, b);\",kae=\"let resultTemp = areal * breal - aimag * bimag;\",Nae=\"let resultTemp = areal * bimag + aimag * breal;\",Tae=\"let resultTemp = a / b;\",_ae=\"let resultTemp = select(a * (b + 1.0), a, b >= b - b);\",Eae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a == b);\n`,$ae=`\n let remainder =\n select(a % b, round(a % b), (round(a) == a) & (round(b) == b));\n let quotient = (a - remainder) / b;\n let resultTemp =\n round(select(quotient, quotient - 1, sign(remainder) == -sign(b)));\n`,Rae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a > b);\n`,Dae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a >= b);\n`,Aae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a < b);\n`,Fae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a <= b);\n`,Pae=\"return f32(a >= 1.0 && b >= 1.0);\",Oae=`return (vec4(a >= vec4(1.0)) *\n vec4(b >= vec4(1.0)));`,Mae=\"return f32(a >= 1.0 || b >= 1.0);\",Lae=`return min(vec4(a >= vec4(1.0)) +\n vec4(b >= vec4(1.0)), vec4(1.0));`,Bae=\"let resultTemp = max(a, b);\",zae=\"let resultTemp = min(a, b);\",Vae=`\n let isNaN = b == 0.;\n var resultTemp = a % b;\n resultTemp = select((resultTemp + b) % b, resultTemp,\n (a < 0. && b < 0.) || (a >= 0. && b > 0.));\n`,Wae=`\n let isNaN = !vec4(b);\n var resultTemp = vec4(a % b);\n if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {\n resultTemp[0] = (resultTemp[0] + b[0]) % b[0];\n }\n if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {\n resultTemp[1] = (resultTemp[1] + b[1]) % b[1];\n }\n if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {\n resultTemp[2] = (resultTemp[2] + b[2]) % b[2];\n }\n if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {\n resultTemp[3] = (resultTemp[3] + b[3]) % b[3];\n }\n`,Uae=\"let resultTemp = a * b;\",Gae=`\n var resultTemp = f32(a != b);\n let valueForNaN = 1.0;\n`,Hae=`\n var resultTemp = vec4(a != b);\n let valueForNaN = 1.0;\n`,Kae=`\n let isNaN = a < 0.0 && floor(b) < b;\n if (b == 0.0) {\n return 1.0;\n }\n var resultTemp = select(sign(a) * pow(abs(a), b), pow(abs(a), b),\n round(abs(b) % 2.0) != 1.0);\n`,qae=`\n let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1);\n let isModRound1 = vec4(isModRound1Bool);\n let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n var resultTemp = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n let isExpZero = b == vec4(0.0);\n if (isExpZero.r) {\n resultTemp.r = 1.0;\n }\n if (isExpZero.g) {\n resultTemp.g = 1.0;\n }\n if (isExpZero.b) {\n resultTemp.b = 1.0;\n }\n if (isExpZero.a) {\n resultTemp.a = 1.0;\n }\n let isNaN = (a < vec4(0.0)) & (floor(b) < b);\n`,jae=\"if (a < 0.0) { return b * a; } return a;\",Xae=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,Yae=\"let resultTemp = (a - b) * (a - b);\",Qae=\"let resultTemp = a - b;\";function Xc(r,e){let t;do{switch(r){case fe.ATAN2:t=vae;break;case fe.MAX:t=Bae;break;case fe.MIN:t=zae;break;case fe.MOD:t=e?Wae:Vae;break;case fe.NOT_EQUAL:t=e?Hae:Gae;break;case fe.POW:t=e?qae:Kae;break;default:continue}let o,n,s;return e?(o=\"isnanVec4\",n=\"vec4\",s=\"vec4\"):(o=\"isnan\",n=\"f32\",s=\"bool\"),`\n let aIsNaN = ${o}(a);\n let aPostLegalization = select(a, ${n}(42), aIsNaN);\n let bIsNaN = ${o}(b);\n let bPostLegalization = select(b, ${n}(42), bIsNaN);\n let isNaN = false;\n let valueForNaN = uniforms.NAN;\n {\n let a = aPostLegalization;\n let b = bPostLegalization;\n ${t}\n return select(\n resultTemp, ${n}(valueForNaN),\n ${s}(isNaN) | aIsNaN | bIsNaN);\n }\n `}while(!1);switch(r){case fe.ADD:t=Iae;break;case fe.COMPLEX_MULTIPLY_IMAG:t=Nae;break;case fe.COMPLEX_MULTIPLY_REAL:t=kae;break;case fe.DIV:t=Tae;break;case fe.ELU_DER:t=_ae;break;case fe.EQUAL:t=Eae;break;case fe.FLOOR_DIV:t=$ae;break;case fe.GREATER:t=Rae;break;case fe.GREATER_EQUAL:t=Dae;break;case fe.LESS:t=Aae;break;case fe.LESS_EQUAL:t=Fae;break;case fe.LOGICAL_AND:return e?Oae:Pae;case fe.LOGICAL_OR:return e?Lae:Mae;case fe.MUL:t=Uae;break;case fe.PRELU:return e?Xae:jae;case fe.SQUARED_DIFFERENCE:t=Yae;break;case fe.SUB:t=Qae;break;default:}return`\n ${t}\n return resultTemp;\n `}var Z;(function(r){r[r.ABS=0]=\"ABS\",r[r.ACOS=1]=\"ACOS\",r[r.ACOSH=2]=\"ACOSH\",r[r.ASIN=3]=\"ASIN\",r[r.ASINH=4]=\"ASINH\",r[r.ATAN=5]=\"ATAN\",r[r.ATANH=6]=\"ATANH\",r[r.CEIL=7]=\"CEIL\",r[r.COS=8]=\"COS\",r[r.COSH=9]=\"COSH\",r[r.ELU=10]=\"ELU\",r[r.ERF=11]=\"ERF\",r[r.EXP=12]=\"EXP\",r[r.EXPM1=13]=\"EXPM1\",r[r.FLOOR=14]=\"FLOOR\",r[r.IS_FINITE=15]=\"IS_FINITE\",r[r.IS_INF=16]=\"IS_INF\",r[r.IS_NAN=17]=\"IS_NAN\",r[r.LINEAR=18]=\"LINEAR\",r[r.LOG=19]=\"LOG\",r[r.LOG1P=20]=\"LOG1P\",r[r.LOGICAL_NOT=21]=\"LOGICAL_NOT\",r[r.NEG=22]=\"NEG\",r[r.RELU=23]=\"RELU\",r[r.RELU6=24]=\"RELU6\",r[r.LEAKYRELU=25]=\"LEAKYRELU\",r[r.RECIPROCAL=26]=\"RECIPROCAL\",r[r.ROUND=27]=\"ROUND\",r[r.RSQRT=28]=\"RSQRT\",r[r.SELU=29]=\"SELU\",r[r.SIGMOID=30]=\"SIGMOID\",r[r.SIGN=31]=\"SIGN\",r[r.SIN=32]=\"SIN\",r[r.SINH=33]=\"SINH\",r[r.SOFTPLUS=34]=\"SOFTPLUS\",r[r.SQRT=35]=\"SQRT\",r[r.SQUARE=36]=\"SQUARE\",r[r.STEP=37]=\"STEP\",r[r.TAN=38]=\"TAN\",r[r.TANH=39]=\"TANH\",r[r.TO_INT=40]=\"TO_INT\"})(Z||(Z={}));var Zae=\"return abs(a);\",Jae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return acos(a);\n`,eie=`\n if (a < 1.) {\n return uniforms.NAN;\n }\n return acosh(a);\n`,tie=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return asin(a);\n`,rie=\"return asinh(a);\",oie=`\n if (isnan(a)) {\n return uniforms.NAN;\n }\n return atan(a);\n`,nie=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n if (a == 1.) {\n return uniforms.INFINITY;\n }\n if (a == -1.) {\n return -uniforms.INFINITY;\n }\n return atanh(a);\n`,sie=\"return ceil(a);\",aie=\"return cos(a);\",iie=`\n let e2x = exp(-a);\n return (e2x + 1.0 / e2x) / 2.0;\n`,uie=\"return exp(a) - 1.0;\",pie=\"if (a >= 0.0) { return a; } return (exp(a) - 1.0);\",cie=`\n var resFloat = exp(a) - vec4(1.0);\n if (a.r >= 0.0) {\n resFloat.r = a.r;\n }\n if (a.g >= 0.0) {\n resFloat.g = a.g;\n }\n if (a.b >= 0.0) {\n resFloat.b = a.b;\n }\n if (a.a >= 0.0) {\n resFloat.a = a.a;\n }\n return resFloat;\n`,lie=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n let p = ${w.ERF_P};\n let a1 = ${w.ERF_A1};\n let a2 = ${w.ERF_A2};\n let a3 = ${w.ERF_A3};\n let a4 = ${w.ERF_A4};\n let a5 = ${w.ERF_A5};\n\n let sign = sign(a);\n let absA = abs(a);\n let t = 1.0 / (1.0 + p * absA);\n return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));\n`,mie=\"return exp(a);\",die=\"return floor(a);\",fie=\"return f32(!isnan(a) && !isinf(a));\",hie=\"return f32(isinf(a));\",gie=\"return f32(isnan(a));\",xie=\"return a;\",yie=`if (a < 0.0) { return uniforms.NAN; }\n return log(a);`,bie=`\n if (isnan(a)) { return a; }\n return log(1.0 + a);\n`,Cie=\"return f32(!(a >= 1.0));\",wie=\"return -a;\",Sie=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",Iie=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,vie=\"return 1.0 / a;\",kie=\"return select(a, 0.0, a < 0.0);\",Nie=\"return clamp(a, 0.0, 6.0);\",Tie=\"return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));\",_ie=`\n return select(a, vec4(0.0), a < vec4(0.0));\n`,Eie=\"return round(a);\",$ie=\"return inverseSqrt(a);\",Rie=`\n if (a >= 0.0) {\n return ${w.SELU_SCALE} * a;\n } else {\n return ${w.SELU_SCALEALPHA} * (exp(a) - 1.0);\n }\n`,Die=\"return 1.0 / (1.0 + exp(-1.0 * a));\",Aie=\"return sign(a);\",Fie=\"return sin(a);\",Pie=`\n let e2x = exp(a);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Oie=`\n let epsilon = 1.1920928955078125e-7;\n let threshold = log(epsilon) + 2.0;\n\n let too_large = a > -threshold;\n let too_small = a < threshold;\n let exp_a = exp(a);\n\n if (too_large) {\n return a;\n } else if (too_small) {\n return exp_a;\n } else {\n return log(exp_a + 1.0);\n }\n`,Mie=\"return sqrt(a);\",Lie=\"return a * a;\",Bie=`\n if (isnan(a)) {\n return a;\n }\n\n return select(uniforms.stepAlpha, 1.0, a > 0.0);\n`,zie=\"return tan(a);\",Vie=`\n let e2x = exp(-2.0 * abs(a));\n return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,Wie=\"return f32(i32((a)));\";function Si(r,e){switch(r){case Z.ABS:return Zae;case Z.ACOS:return Jae;case Z.ACOSH:return eie;case Z.ASIN:return tie;case Z.ASINH:return rie;case Z.ATAN:return oie;case Z.ATANH:return nie;case Z.COS:return aie;case Z.COSH:return iie;case Z.CEIL:return sie;case Z.ELU:return e?cie:pie;case Z.ERF:return lie;case Z.EXP:return mie;case Z.EXPM1:return uie;case Z.FLOOR:return die;case Z.IS_FINITE:return fie;case Z.IS_INF:return hie;case Z.IS_NAN:return gie;case Z.LINEAR:return xie;case Z.LOG:return yie;case Z.LOG1P:return bie;case Z.LOGICAL_NOT:return Cie;case Z.NEG:return wie;case Z.LEAKYRELU:return e?Iie:Sie;case Z.RECIPROCAL:return vie;case Z.RELU:return e?_ie:kie;case Z.RELU6:return e?Tie:Nie;case Z.ROUND:return Eie;case Z.RSQRT:return $ie;case Z.SELU:return Rie;case Z.SIGMOID:return Die;case Z.SIGN:return Aie;case Z.SIN:return Fie;case Z.SINH:return Pie;case Z.SOFTPLUS:return Oie;case Z.SQRT:return Mie;case Z.SQUARE:return Lie;case Z.STEP:return Bie;case Z.TAN:return zie;case Z.TANH:return Vie;case Z.TO_INT:return Wie;default:throw new Error(`BinaryType ${r} is not implemented!`)}}function dr(r,e=!1,t=!1,o=3){if(r===null)return\"\";let n=\"\";if(r===\"linear\")n=Si(Z.LINEAR);else if(r===\"relu\")n=Si(Z.RELU,t);else if(r===\"elu\")n=Si(Z.ELU,t);else if(r===\"relu6\")n=Si(Z.RELU6,t);else if(r===\"prelu\")n=Xc(fe.PRELU,t);else if(r===\"sigmoid\")n=Si(Z.SIGMOID,t);else if(r===\"leakyrelu\")n=Si(Z.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=Ae(t?4:1),i=\"\";return e?i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n let b = getPreluActivationWeightsByOutputCoords(coords);\n ${n}\n }`:i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n ${n}\n }`,i}function Zr(r,e){return`\n ${r?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\n ${e?\"value = activation(value, coords);\":\"\"}\n `}function Jv(r,e,t=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=`\n ${r?\"value = getA(batch, col, row);\":\"value = getA(batch, row, col);\"}\n\n `,i=e?\"value = getB(batch, col, row);\":\"value = getB(batch, row, col);\";return`\n fn mm_readA(batch: i32, row: i32, col: i32) -> ${Ae(s)} {\n var value = ${Ae(s)}(0.0);\n ${t&&n?a:`\n ${r?\"if(row < uniforms.dimAOuter && col < uniforms.dimInner)\":\"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])\"}\n {\n ${a}\n }\n `}\n return value;\n }\n\n fn mm_readB(batch: i32, row: i32, col: i32) -> ${Ae(s)} {\n var value = ${Ae(s)}(0.0);\n ${i}\n return value;\n }\n `}function hm(r,e,t,o,n=!1,s=!1,a=!1,i=1){return`\n ${Jv(t,o,n,s,a,i)}\n fn mm_write(batch: i32, row: i32, col: i32, valueIn: ${Ae(i)}) {\n ${n&&s?\"\":\"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n {\n var value = valueIn;\n let coords = vec3(batch, row, col);\n ${Zr(r,e)}\n setOutputAtCoords(coords[0], coords[1], coords[2], value);\n }\n }\n `}var Uie=(r,e)=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart + inputCol * ${e});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRow + innerRow,\n kStart + inputCol * ${e});\n `,Gie=(r,e,t,o)=>{if(r)return`\n for (var k = 0; k < ${o}; k++) {\n let BCached0 = mm_Bsub[k][tileCol];\n let ACached0 = mm_Asub[k][localRow];\n for (var i = 0; i < ${t}; i++) {\n acc[i] = fma(BCached0, vec4(ACached0[i]), acc[i]);\n }\n }`;{let n=\"\",s=\"\";for(let a=0;a(ACached[${a}]), acc[i]);`;return`\n for (var k = 0; k < ${o/e}; k++) {\n ${n}\n for (var i = 0; i < ${t}; i++) {\n let ACached = mm_Asub[tileRow + i][k];\n ${s}\n }\n }`}};function _p(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=e[1]*r[1],p=e[0]*r[0],u=t?i:o,c=t?o:i,l=u/e[0],m=o/e[1],d=r[1],f=r[0];return y.assert((t&&l===4&&r[1]===4||!t&&(l===3||l===4))&&u%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4.\n Otherwise, innerElementSize ${l} must be 3 or 4.\n tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),`\n var mm_Asub : array, ${u/l}>, ${c}>;\n var mm_Bsub : array, ${p/r[0]}>, ${o}>;\n\n ${G()} {\n let localRow = i32(localId.y);\n let tileRow = localRow * ${d};\n let tileCol = i32(localId.x);\n\n let globalRow = i32(globalId.y) * ${d};\n let globalCol = i32(globalId.x) * ${f};\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!a?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!a?\"batch\":\"batch % uniforms.bShape[0]\"};\n let globalRowStart = i32(workgroupId.y) * ${i};\n\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc: array, ${d}>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${m};\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${Uie(t,l)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol);\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n ${Gie(t,l,d,o)}\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n }`}var az=r=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRowStart + inputRow,\n kStart + inputCol);\n `,Hie=r=>r?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function Ep(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*e[1],u=r[0]*e[0],c=t?p:o,l=t?o:p;y.assert(l%e[1]===0&&c%e[0]===0&&o%e[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let m=l/e[1],d=c/e[0],f=o/e[1],h=r[1],g=r[0],x=a?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${p};\n let globalColStart = i32(workgroupId.x) * ${u};\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) {\n ${az(t)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalColStart + inputCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}];\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let ACached = ${t?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let gRow = globalRowStart + localRow + innerRow * ${e[1]};\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let gCol = globalColStart + localCol + innerCol * ${e[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\n let tileRow = i32(localId.y) * ${h};\n let tileCol = i32(localId.x) * ${g};\n\n let globalRow = i32(globalId.y) * ${h};\n let globalCol = i32(globalId.x) * ${g};\n let globalRowStart = i32(workgroupId.y) * ${p};\n\n let tileRowA = i32(localId.y) * ${m};\n let tileColA = i32(localId.x) * ${d};\n let tileRowB = i32(localId.y) * ${f};\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n for (var innerCol = 0; innerCol < ${d}; innerCol++) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${az(t)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalCol + innerCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n ${Hie(t)}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n }\n `;return`\n var mm_Asub : array, ${l}>;\n var mm_Bsub : array, ${o}>;\n\n ${G()} {\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!i?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!i?\"batch\":\"batch % uniforms.bShape[0]\"};\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc : array, ${h}>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${x}\n }\n `}var Kie=r=>r?`\n mm_readA(batchA, colA, globalRow),\n mm_readA(batchA, colA + 1, globalRow),\n mm_readA(batchA, colA + 2, globalRow),\n mm_readA(batchA, colA + 3, globalRow)\n `:`\n mm_readA(batchA, globalRow, colA),\n mm_readA(batchA, globalRow, colA + 1),\n mm_readA(batchA, globalRow, colA + 2),\n mm_readA(batchA, globalRow, colA + 3)\n `;function qie(r,e=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let t=r[0]*4;return`\n var mm_Asub : array, ${r[0]}>;\n\n ${G()} {\n let tileCol = i32(localId.x);\n let globalCol = i32(globalId.x);\n let globalRow = i32(globalId.y);\n\n let numTiles = (uniforms.dimInner - 1) / ${t} + 1;\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n // Without this initialization strange values show up in acc.\n var acc = 0.0;\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n let colA = t * ${t} + tileCol * 4;\n mm_Asub[tileCol] = vec4(${Kie(e)});\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < ${t/4}; k++) {\n let rowB = t * ${t} + k * 4;\n let BCached = vec4(mm_readB(batchB, rowB, globalCol),\n mm_readB(batchB, rowB + 1, globalCol),\n mm_readB(batchB, rowB + 2, globalCol),\n mm_readB(batchB, rowB + 3, globalCol));\n\n let ACached = mm_Asub[k];\n acc = acc + dot(ACached, BCached);\n }\n\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var Xg=class{constructor(e,t,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?e[1]:e[2];if(this.isVec4=(u%4===0&&!o||t[1]%4===0&&o)&&t[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=t[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=Qv(t[1],u,t[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push(\"bias\"),l&&this.variableNames.push(\"preluActivationWeights\"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights,this.isVec4)}\n ${hm(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}\n ${this.isVec4?_p(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?qie(this.workgroupSize,this.transposeA):Ep(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)}\n `}};function jie(r){return`\n var sumValues : array;\n ${G()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n let row = coords[1];\n let col = coords[2];\n var sum = 0.0;\n let Length = uniforms.dimInner;\n for (var k = i32(localId.x); k < Length; k = k + ${r}) {\n let dataA = mm_readA(batchA, row, k);\n let dataB = mm_readB(batchB, k, col);\n sum = sum + dataA * dataB;\n }\n sumValues[localId.x] = sum;\n workgroupBarrier();\n\n for(var currentSize = ${r/2}u; currentSize > 1u;\n currentSize = currentSize / 2u) {\n if (localId.x < currentSize)\n {\n sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n }\n workgroupBarrier();\n }\n\n if (localId.x == 0u) {\n sum = sumValues[0] + sumValues[1];\n mm_write(batch, row, col, sum);\n }\n }\n `}var Yg=class{constructor(e,t=!1,o=!1,n=null,s=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=t,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${t}_${o}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${hm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${jie(this.workgroupSize[0])}\n `}};function Xie(r){let e=r[1],t=r[0],o=e>t?e:t;return`\n var mm_Asub : array, ${e}>;\n var mm_Bsub : array, ${o}>;\n\n // If the output size is small for matrix multiplication, avoid to use vec4\n // and handle some elements per thread to optimally utilize the ALU.\n // Read data from global memory to registers firstly, then store them into\n // shared memory, so it is instruction-Level parallelism for arithmetic\n // operations and others handle IO operations between barrier api, makes ALU\n // and load/store units work simultaneously, could improves the performance.\n ${G()} {\n let tileRow = i32(localId.y);\n let tileCol = i32(localId.x);\n let globalRow = i32(globalId.y);\n let globalCol = i32(globalId.x);\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n\n // uniforms.dimInner should be greater than 0.\n let numTiles = (uniforms.dimInner - 1) / ${o} + 1;\n var acc = 0.0;\n\n var globalColA = tileCol;\n var globalRowB = 0;\n var regA = mm_readA(batchA, globalRow, globalColA);\n var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var t = 0; t < numTiles; t = t + 1) {\n mm_Asub[tileRow][tileCol] = regA;\n mm_Bsub[2 * tileRow][tileCol] = regB0;\n mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n workgroupBarrier();\n\n regA = mm_readA(batchA, globalRow, globalColA);\n regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var k = 0; k < ${o}; k = k + 1) {\n acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n }\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var Qg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push(\"bias\");let c=p!=null;c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${hm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${Xie(this.workgroupSize)}\n `}};var Zg=class{constructor(e,t,o=!1,n=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,y.assert(e[0]===1,()=>\"MatMulSplitKProgram only supports batch = 1.\"),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let s=(o&&this.outputShape[1]%4===0||!o&&t%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=s?4:1,s||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=H(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let e=this.outputComponent;return`\n ${Jv(!1,this.transposeB,!1,!1,!1,e)}\n fn mm_write(batch: i32, row : i32, col : i32, value : ${Ae(e)}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n let coords = vec3(batch, row, col);\n let flatIndex = getOutputIndexFromCoords(coords);\n // The problem is that we should initialize output to zero before using.\n // Otherwise, the original value will be added to the result.\n for (var i = 0; i < ${e}; i = i + 1) {\n ${Qr(\"&result[flatIndex + i]\",`${e>1?\"value[i]\":\"value\"}`,\"float32\")}\n }\n }\n }\n ${e===4?_p(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Ep(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}\n `}},Jg=class{constructor(e,t=null,o=null,n=null){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var value = getXByOutputIndex(index);\n ${Zr(this.addBias,this.activation)}\n setOutputAtIndex(index, value);\n }\n }\n `}};var ex=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.value);\n }\n }\n `}};function vt(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new ex(o),i=[{type:\"float32\",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var iz={kernelName:sa,backendName:\"webgpu\",kernelFunc:vt};function pe(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var uz={kernelName:da,backendName:\"webgpu\",kernelFunc:pe};function $p({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),S=Sr.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=pe({inputs:{x:r},backend:n,attrs:{shape:k}}),R=pe({inputs:{x:e},backend:n,attrs:{shape:_}}),D=[$,R],P=Math.max(x,b),O=[$,R],M=[{type:\"int32\",data:[d]},{type:\"int32\",data:[f]},{type:\"int32\",data:[l]}],L,B,z=[P,d,f],U=A().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");if(U<0){let q=A().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),Y=q>0?q:n.thresholdToIncreaseWorkgroups,J=P*Math.ceil(d/32)*Math.ceil(f/32);J<=Y||d<=8&&J<=Y*2?P*d*f<=128?U=Mo.MatMulReduceProgram:P===1&&m>=2e3?U=Mo.MatMulSplitKProgram:U=Mo.MatMulSmallOutputSizeProgram:U=Mo.MatMulPackedProgram}switch(U){case Mo.MatMulReduceProgram:L=new Yg(z,t,o,s,p,a);break;case Mo.MatMulSplitKProgram:{if(B=vt({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Zg(z,m,t,o),s||p){B=n.runWebGPUProgram(L,O,r.dtype,M,B);let Y=new Jg(B.shape,s,p,a),J=null,re=[B];s&&re.push(s),a&&re.push(a),p===\"leakyrelu\"&&(J=[{type:\"float32\",data:[i]}],Y.uniforms+=\" alpha : f32,\");let ne=n.runWebGPUProgram(Y,re,B.dtype,J);D.push(B);let ee=pe({inputs:{x:ne},backend:n,attrs:{shape:S}});D.push(ne);for(let oe of D)n.disposeData(oe.dataId);return ee}break}case Mo.MatMulSmallOutputSizeProgram:L=new Qg(k,_,z,t,o,s,p,a);break;case Mo.MatMulPackedProgram:let q=n.adapterInfo.isIntel();L=new Xg(k,z,t,o,s,p,a,q);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&O.push(s),a&&O.push(a),p===\"leakyrelu\"&&(M.push({type:\"float32\",data:[i]}),L.uniforms+=\" alpha : f32,\"),B=n.runWebGPUProgram(L,O,r.dtype,M,B);let j=pe({inputs:{x:B},backend:n,attrs:{shape:S}});D.push(B);for(let q of D)n.disposeData(q.dataId);return j}function Yie(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return $p({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var pz={kernelName:So,backendName:\"webgpu\",kernelFunc:Yie};var gm=class{constructor(e,t,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=w.assertAndGetBroadcastShape(t,o),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n fn binaryOpComplex(\n areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n ${Xc(this.op,!1)}\n }\n\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let areal = getARealByOutputIndex(index);\n let aimag = getAImagByOutputIndex(index);\n let breal = getBRealByOutputIndex(index);\n let bimag = getBImagByOutputIndex(index);\n setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n }\n }\n `}};var Ii=class{constructor(e,t,o){if(this.size=!0,this.variableNames=[\"A\",\"B\"],this.outputShape=w.assertAndGetBroadcastShape(t,o),this.dispatchLayout=X(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${e}_${this.lastDimensionSize}`,this.type=\"shared\",this.workgroupSize=[256,1,1];else{let n=t.length>0&&t[t.length-1]%4===0,s=o.length>0&&o[o.length-1]%4===0;n&&s?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(y.isScalarShape(o)||o[o.length-1]===1)||s&&(y.isScalarShape(t)||t[t.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type=\"nonshared\",this.shaderKey=`binary_${e}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let e,t=this.outputComponent===4?\"vec4\":\"f32\",o=`\n fn binaryOperation(a : ${t}, b : ${t}) -> ${t} {\n ${Xc(this.op,this.outputComponent===4)}\n };\n `;if(this.type===\"shared\"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index);\n let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}];\n let b = getBByOutputIndex(index);`;e=`\n ${o}\n var sharedBuf : array;\n ${G(\"index\")} {\n // Fill in the shared memory buffer.\n let localIndex = i32(localId.x);\n if(localIndex < ${this.lastDimensionSize}) {\n sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n }\n workgroupBarrier();\n\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n ${s}\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `}else e=`\n ${o}\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index * ${this.outputComponent});\n let a = ${t}(getAByOutputCoords(coords));\n let b = ${t}(getBByOutputCoords(coords));\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `;return e}};function At(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var cz={kernelName:Co,backendName:\"webgpu\",kernelFunc:At};function xo(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.tensorMap.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var lz={kernelName:Di,backendName:\"webgpu\",kernelFunc:xo};var Jr=class{constructor(e,t,o=\"\"){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,o!==\"\"&&(this.uniforms=o),this.shaderKey=`unary_${t}`}getUserCode(){return`\n fn unaryOperation(a : f32) -> f32 {\n ${Si(this.op,!1)}\n }\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n setOutputAtIndex(index, unaryOperation(a));\n }\n }\n `}};function ye({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Jr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function et({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype===\"complex64\"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==fe.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},S={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Ii(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,S],dt(x.dtype,b.dtype))});else{let g=new gm(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new gm(fe.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,\"float32\"),f=p.runWebGPUProgram(x,b,\"float32\")}let h=xo({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype===\"string\"||i.dtype===\"string\"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype===\"string\"?w.fromUint8ToStringArray(l):l,f=a.dtype===\"string\"?w.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Ii(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:mz,castImpl:dz,ceilImpl:fz,concatImpl:hz,equalImpl:gz,expImpl:xz,expm1Impl:yz,floorImpl:bz,floorDivImpl:Cz,gatherNdImpl:wz,gatherV2Impl:Sz,greaterEqualImpl:Iz,greaterImpl:vz,lessEqualImpl:kz,lessImpl:Nz,logImpl:Tz,maxImpl:_z,maximumImpl:Ez,minimumImpl:$z,multiplyImpl:Rz,negImpl:Dz,notEqualImpl:Az,prodImpl:Fz,rangeImpl:Pz,rsqrtImpl:Oz,scatterImpl:Mz,simpleAbsImpl:Lz,sliceImpl:Bz,stridedSliceImpl:zz,stringNGramsImpl:Vz,subImpl:Wz,tileImpl:Uz,topKImpl:Gz,transposeImpl:Hz,uniqueImpl:rOt}=Ic;var Qie=ye({opType:Z.ABS,cpuKernelImpl:Lz}),Kz={kernelName:\"Abs\",backendName:\"webgpu\",kernelFunc:Qie};var Zie=ye({opType:Z.ACOS}),qz={kernelName:Vo,backendName:\"webgpu\",kernelFunc:Zie};var Jie=ye({opType:Z.ACOSH}),jz={kernelName:Wo,backendName:\"webgpu\",kernelFunc:Jie};var eue=et({opType:fe.ADD,cpuKernelImpl:mz,supportsComplex:!0}),Xz={kernelName:\"Add\",backendName:\"webgpu\",kernelFunc:eue};var tx=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(\" + \");return`\n ${G(\"index\")} {\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let flatIndex = index * ${this.workPerThread} + i;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n ${e.join(`\n `)}\n setOutputAtIndex(flatIndex, ${t});\n }\n }\n }\n `}};function tue(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new tx(s);return t.runWebGPUProgram(a,o,n)}var Yz={kernelName:Uo,backendName:\"webgpu\",kernelFunc:tue};var rx=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return`\n var tile : array, ${this.workgroupSize[0]}>;\n ${G()} {\n var x = i32(workgroupId.x) * ${e} + i32(localId.x);\n var y = i32(workgroupId.y) * ${e} + i32(localId.y);\n let width = uniforms.outShape[0];\n let height = uniforms.outShape[1];\n if (x < width && y < height) {\n tile[localId.y][localId.x] = f32(A[y * width + x]);\n }\n workgroupBarrier();\n\n x = i32(workgroupId.y) * ${e} + i32(localId.x);\n y = i32(workgroupId.x) * ${e} + i32(localId.y);\n if (x < height && y < width) {\n setOutputAtIndex((y * height + x), tile[localId.x]\n [localId.y]);\n }\n }\n `}};var ox=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o=32768&&o>=512?this.workgroupSize=[512,1,1]:e.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\",o=this.workgroupSize[0];this.reduceType===\"min\"||this.reduceType===\"max\"?(e=`\n if (isnan(candidate)) {\n bestValue = uniforms.NAN;\n } else if (!isnan(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n { bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"?(e=\" bestValue = bestValue * candidate; \",t=\"1.0\"):this.reduceType===\"all\"?(e=\" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); \",t=\"1.0\"):this.reduceType===\"any\"&&(e=\" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); \",t=\"0.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n fn DIV_CEIL(a : u32, b : u32) -> u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestValues : array;\n `}\n fn getOffset(outputIndex : i32) -> i32 {\n let outputCoords = getCoordsFromIndex(outputIndex);\n let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n return offset;\n }\n ${G(\"index\")} {\n let outputIndex = index / ${o};\n let offset = getOffset(outputIndex);\n var bestValue = ${t};\n let Length = uniforms.reduceSize;\n let WorkPerThread = DIV_CEIL(u32(Length), ${o}u);\n for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n k = k + ${o}) {\n let candidate = f32(x[offset + k]);\n ${e}\n }\n xBestValues[localId.x] = bestValue;\n workgroupBarrier();\n\n var reduceSize = min(u32(Length), ${o}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n ${e}\n xBestValues[localId.x] = bestValue;\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n ${n}\n }\n }\n `}};var rue={mean:\"float32\",all:\"bool\",any:\"bool\"};function eo(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=w.getAxesPermutation(p,s),c=r;u!=null&&(c=xr({inputs:{x:r},attrs:{perm:u},backend:n}),p=w.getInnerMostAxes(p.length,s),a.push(c)),w.assertAxesAreInnerMostDims(o,p,s);let[l,m]=w.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=w.expandShapeToKeepDim(l,i));let f;if((o===\"max\"||o===\"prod\")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case\"max\":let g=_z(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case\"prod\":let{outVals:x,outShape:b,outDtype:C}=Fz(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=rue[o]||oi(r.dtype),S=[{type:\"int32\",data:[h]}],k=new nx(b,o,n.device.limits.maxComputeWorkgroupSizeX),_=n.runWebGPUProgram(k,[c],C,S);a.push(_),f=pe({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function oue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,\"all\",t)}var Zz={kernelName:\"All\",backendName:\"webgpu\",kernelFunc:oue};function nue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,\"any\",t)}var Jz={kernelName:\"Any\",backendName:\"webgpu\",kernelFunc:nue};var Yc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let n=[t];this.op=o===\"min\"?\"<\":\">\";let[s,a]=w.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=X(this.outputShape),y.sizeFromShape(a)<32?(this.type=\"plain\",this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type=\"shared\",this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${Oo(this.inputShape.length-1)}`,o=()=>{let n=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+=\"outputCoords,\");else for(let s=0;s u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestIndices : array;\n var xBestValues : array;\n `}\n\n ${G(\"index\")} {\n let outputIndex = index / ${e};\n let reduceLength = ${t()};\n\n var bestIndex = i32(localId.x);\n var bestValue = uniforms.infinityValue;\n let outputCoords = getCoordsFromIndex(outputIndex);\n for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n k = k + ${e}) {\n let candidate = getX(${o()} k);\n if (!isnan(candidate) && candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = k;\n }\n }\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = bestIndex;\n workgroupBarrier();\n\n var reduceSize = min(u32(reduceLength), ${e}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n }\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n }\n }\n `:`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outputCoords = getCoordsFromIndex(index);\n var bestIndex = 0;\n var bestValue = getX(${o()} 0);\n let reduceLength = ${t()};\n for (var i = 1; i < reduceLength; i++) {\n let candidate = getX(${o()} i);\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = i;\n }\n }\n setOutputAtIndexI32(index, bestIndex);\n }\n }\n `}};function sue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=new Yc(p.shape,a[0],\"max\"),l=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var eV={kernelName:Ys,backendName:\"webgpu\",kernelFunc:sue};function aue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=new Yc(p.shape,a[0],\"min\"),l=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var tV={kernelName:Qs,backendName:\"webgpu\",kernelFunc:aue};var iue=ye({opType:Z.ASIN}),rV={kernelName:Ko,backendName:\"webgpu\",kernelFunc:iue};var uue=ye({opType:Z.ASINH}),oV={kernelName:qo,backendName:\"webgpu\",kernelFunc:uue};var pue=ye({opType:Z.ATAN}),nV={kernelName:jo,backendName:\"webgpu\",kernelFunc:pue};var cue=et({opType:fe.ATAN2}),sV={kernelName:Yo,backendName:\"webgpu\",kernelFunc:cue};var lue=ye({opType:Z.ATANH}),aV={kernelName:Xo,backendName:\"webgpu\",kernelFunc:lue};var sx=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"strides : vec2,\",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let xRCCorner = coords.yz * uniforms.strides;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n let value = getX(batch, xRCorner, xCCorner, d);\n setOutputAtIndex(index, value);\n }\n }\n `}};var Ba=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,\",this.workgroupSize=[128,1,1],this.size=!0,t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool2D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType===\"avg\"?e=\"resultValue = resultValue + value; count = count + 1.0;\":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"((batch * uniforms.xShape[1] + xR) * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"(xR * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"wR * uniforms.filterDims.y + wC\"};\n }`:e=\"resultValue = max(value, resultValue);\";let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / max(count, 1.0)\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n let xRCCorner = vec2(coords.yz) * uniforms.strides - uniforms.pads;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilations.x) {\n let xR = xRCorner + wR;\n\n if (xR < 0 || xR >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilations.y) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.y) {\n continue;\n }\n\n let value = getX(batch, xR, xC, d);\n ${e}\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${t});`}\n }\n }\n `}},Iu=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,\",this.workgroupSize=[128,1,1],this.size=!0,t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool3D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType===\"avg\"?e=\"resultValue += value; count += 1.0;\":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"(((batch * uniforms.xShape.y + xD) * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"((xD * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"wD * uniforms.filterDims.y * uniforms.filterDims.y + wR * uniforms.filterDims.z + wC\"};\n }`:e=\"resultValue = max(value, resultValue);\";let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / max(count, 1.0)\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let xCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xDCorner = xCorner.x;\n let xRCorner = xCorner.y;\n let xCCorner = xCorner.z;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wD = 0; wD < uniforms.filterDims.x; wD++) {\n let xD = xDCorner + wD;\n if (xD < 0 || xD >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims.y; wR++) {\n let xR = xRCorner + wR;\n if (xR < 0 || xR >= uniforms.convDims.y) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.z; wC++) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.z) {\n continue;\n }\n\n let value = getX(batch, xD, xR, xC, ch);\n ${e}\n }\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${t});`}\n }\n }\n `}};function t0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return eo(n,s,a,\"max\",t)}var iV={kernelName:\"Max\",backendName:\"webgpu\",kernelFunc:t0};function r0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,\"mean\",t)}var uV={kernelName:Un,backendName:\"webgpu\",kernelFunc:r0};function ax(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return At({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type===\"VALID\"){let a=r.shape.length,i=pe({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t===\"avg\"?p=r0({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),p=t0({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=pe({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:\"int32\",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new sx(e):(t===\"avg\"?n=new Ba(e,\"avg\"):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),n=new Ba(e,\"max\")),s.push({type:\"int32\",data:[e.padInfo.top,e.padInfo.left]},{type:\"int32\",data:[e.dilationHeight,e.dilationWidth]},{type:\"int32\",data:[e.inHeight,e.inWidth]},{type:\"int32\",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function mue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,c=w.computePool2DInfo(n.shape,s,a,1,i,p);return ax(n,c,\"avg\",t)}var pV={kernelName:Qo,backendName:\"webgpu\",kernelFunc:mue};function due(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Iu(l,\"avg\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var cV={kernelName:Zs,backendName:\"webgpu\",kernelFunc:due};var ix=class{constructor(e){this.variableNames=[\"dy\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool2DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n\n dotProd = dotProd + dyValue * uniforms.avgMultiplier;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},ux=class{constructor(e){this.variableNames=[\"dy\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool3DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n dotProd += dyValue * uniforms.avgMultiplier;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function fue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=w.computePool3DInfo(a.shape,i,p,1,u,c),m=new ux(l),d=1/(l.filterDepth*l.filterHeight*l.filterWidth),f=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.effectiveFilterDepth-1-l.padInfo.front,l.effectiveFilterHeight-1-l.padInfo.top,l.effectiveFilterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]},{type:\"int32\",data:[l.outDepth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"float32\",data:[d]}];return t.runWebGPUProgram(m,[n],a.dtype,f)}var lV={kernelName:Ri,backendName:\"webgpu\",kernelFunc:fue};function hue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;fm([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new ix(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:\"int32\",data:[c.outHeight]},{type:\"int32\",data:[c.outWidth]},{type:\"float32\",data:[m]}];return t.runWebGPUProgram(l,[n],a.dtype,d)}var mV={kernelName:$i,backendName:\"webgpu\",kernelFunc:hue};function gue(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return $p({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var dV={kernelName:Zo,backendName:\"webgpu\",kernelFunc:gue};var px=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${ft(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=ft(this.rank),t=xue(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>\"sourceLoc = uniforms.start + coords;\"):o=this.outputShape.map((s,a)=>`sourceLoc.${o0[a]} = uniforms.start.${Oo(a)} + coords.${o0[a]};`),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var sourceLoc : ${e};\n let coords = getCoordsFromIndex(index);\n ${o.join(`\n`)}\n setOutputAtIndex(index, getSource(${t}));\n }\n }\n `}},o0=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function xue(r){if(r===1)return\"sourceLoc\";if(r<=6)return o0.slice(0,r).map(e=>`sourceLoc.${e}`).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}function Hs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=pt.parseSliceParams(n,s,a);if(pt.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.tensorMap.get(n.dataId),m=Bz(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new px(i,p),c=[{type:\"int32\",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var fV={kernelName:ha,backendName:\"webgpu\",kernelFunc:Hs};var yue=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),h=xr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Hs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},hV={kernelName:Js,backendName:\"webgpu\",kernelFunc:yue};var bue=`\n fn bincount_write(index: i32, value: f32) {\n ${Qr(\"&result[index]\",\"value\",\"float32\")}\n }\n`,Cue=`\n fn bincount_write(index: i32, value: f32) {\n atomicStore(&result[index], bitcast(value));\n }\n`,Qc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"binCountSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push(\"w\"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`\n ${this.binaryOutput?Cue:bue}\n ${G(\"index\")} {\n ${this.rank===1?`if (index < uniforms.xShape) {\n let indexVal = i32(getX(index));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(index)\":\"1.\"};\n bincount_write(indexVal, value);\n }\n }`:`let coord = getCoordsFromIndex(index);\n if (coordsInBounds2D(coord, uniforms.xShape)) {\n let indexVal = i32(getX(coord[0], coord[1]));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(coord[0], coord[1])\":\"1.\"};\n bincount_write(coord.x * uniforms.binCountSize + indexVal, value);\n }\n }`}\n }\n `}};function wue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=vt({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new Qc([i],u),f=[{type:\"int32\",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var gV={kernelName:Jo,backendName:\"webgpu\",kernelFunc:wue};var cx=class{constructor(e){this.outputShape=[],this.variableNames=[\"s0\",\"s1\"],this.uniforms=\"s0Size : i32, s1Size : i32, \",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"broadcastArgs\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var s0 = 1.0;\n var s1 = 1.0;\n let indexS0 = index - uniforms.size + uniforms.s0Size;\n let indexS1 = index - uniforms.size + uniforms.s1Size;\n if (indexS0 >= 0) {\n s0 = getS0(indexS0);\n }\n if (indexS1 >= 0) {\n s1 = getS1(indexS1);\n }\n\n if (s0 == 1.0) {\n setOutputAtIndex(index, s1);\n } else if (s1 == 1.0) {\n setOutputAtIndex(index, s0);\n } else if (s0 != s1) {\n setOutputAtIndex(index, uniforms.NAN);\n } else {\n setOutputAtIndex(index, s0);\n }\n }\n }\n `}};function Sue(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e;if(t.shouldExecuteOnCPU([o,n])){let c=t.tensorMap.get(o.dataId),l=t.tensorMap.get(n.dataId),m=c.values,d=l.values,f=w.assertAndGetBroadcastShape(Array.from(m),Array.from(d));return t.makeTensorInfo([f.length],\"int32\",Int32Array.from(f))}let s=y.sizeFromShape(o.shape),a=y.sizeFromShape(n.shape),i=Math.max(s,a),p=new cx(i),u=[{type:\"int32\",data:[s]},{type:\"int32\",data:[a]}];return t.runWebGPUProgram(p,[o,n],\"int32\",u)}var xV={kernelName:ea,backendName:\"webgpu\",kernelFunc:Sue};var n0=et({opType:fe.NOT_EQUAL,dtype:\"bool\",cpuKernelImpl:Az}),yV={kernelName:Yn,backendName:\"webgpu\",kernelFunc:n0};function vi(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.real},backend:t})}var bV={kernelName:Hi,backendName:\"webgpu\",kernelFunc:vi};function CV(r,e){let t=new Jr(r.shape,Z.TO_INT),o=e.runWebGPUProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function s0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return At({inputs:{x:n},backend:t});let a=Gr(n.shape),i=s0({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=xo({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype===\"complex64\"){let a=vi({inputs:{input:n},backend:t}),i=s0({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=dz(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return CV(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=n0({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var wV={kernelName:yo,backendName:\"webgpu\",kernelFunc:s0};var Iue=ye({opType:Z.CEIL,cpuKernelImpl:fz}),SV={kernelName:en,backendName:\"webgpu\",kernelFunc:Iue};var lx=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n var clampedValue = clamp(\n value, vec4(uniforms.minVal), vec4(uniforms.maxVal));\n clampedValue = select(clampedValue, value, isnanVec4(value));\n setOutputAtIndex(index, clampedValue);\n }\n }\n `}};var mx=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n if (isnan(value)) {\n setOutputAtIndex(index, value);\n return;\n }\n setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n }\n }\n `}};function vue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:\"float32\",data:[s]},{type:\"float32\",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new lx(n.shape):i=new mx(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var IV={kernelName:bo,backendName:\"webgpu\",kernelFunc:vue};var dx=class{constructor(e){this.outputShape=[],this.variableNames=[\"real\",\"imag\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"complexAbs\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let re = abs(getRealByOutputIndex(index));\n let im = abs(getImagByOutputIndex(index));\n let mx = max(re, im);\n\n // The length function in wgsl may be not underflow-safe on some GPUs.\n // So the safe solution is to ensure underflow-safety in all cases.\n setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0));\n }\n }\n `}};function vV(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function kue(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.tensorMap.get(o.dataId),s=new dx(o.shape),a=[vV(o,n.complexTensorInfos.real),vV(o,n.complexTensorInfos.imag)];return t.runWebGPUProgram(s,a,a[0].dtype)}var kV={kernelName:Ai,backendName:\"webgpu\",kernelFunc:kue};var fx=class{constructor(e){this.uniforms=\"\",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=w.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let s=1;svi({inputs:{input:C},backend:t})),h=r.map(C=>Rp({inputs:{input:C},backend:t})),g=Zc(f,e,t),x=Zc(h,e,t),b=xo({inputs:{real:g,imag:x},backend:t});return f.forEach(C=>t.disposeData(C.dataId)),h.forEach(C=>t.disposeData(C.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let f=r.map(k=>{let $=[-1,y.sizeFromShape(k.shape.slice(e))];return pe({inputs:{x:k},backend:t,attrs:{shape:$}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=w.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=hz(h,g,o,x),C=w.computeOutShape(r.map(k=>k.shape),e),S=t.makeTensorInfo(C,o,b);return f.forEach(k=>t.disposeData(k.dataId)),S}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new fx(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:\"int32\",data:[l[0]]});for(let f=1;ft.disposeData(f.dataId));let d=pe({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function Nue(r,e,t){let o=w.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>pe({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function a0(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:t}):Zc(p,s,t)}var TV={kernelName:ta,backendName:\"webgpu\",kernelFunc:a0};function Tue(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=D=>{switch(D){case 1:return\"resData = f32(x[xIndex]);\";case 3:return\"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";case 4:return\"resData = vec4(x[xIndex / 4]);\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},l=D=>{switch(D){case 1:return\"return f32(W[row * uniforms.wShape[3] + col]);\";case 4:return\"return vec4(W[(row * uniforms.wShape[3] + col) / 4]);\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},m=r?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,d=r?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,f=r?\"uniforms.xShape[1]\":\"uniforms.xShape[2]\",h=r?\"uniforms.xShape[2]\":\"uniforms.xShape[3]\",g=r?\"row\":\"col\",x=r?\"col\":\"row\",b=`\n let inChannels = uniforms.wShape[2];\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n let outRow = ${g} / outWidth;\n let outCol = ${g} % outWidth;\n\n let WRow = ${x} / (uniforms.filterDims[1] * inChannels);\n let WCol = ${x} / inChannels % uniforms.filterDims[1];\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * WRow - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * WCol - uniforms.pads[1];\n let xCh = ${x} % inChannels;\n var resData = ${Ae(i)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the 'same' padding type.\n if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {\n ${m}\n let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n ${c(i)}\n }\n return resData;`,C=r?e&&o?`\n ${b}`:`\n if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${b}\n }\n return ${Ae(i)}(0.0);`:o&&t?`\n ${b}`:`\n if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n ${b}\n }\n return ${Ae(i)}(0.0);`,S=`${l(p)}`,k=Ae(u),_=r?Ae(i):Ae(p),$=r?Ae(p):Ae(i);return`\n ${dr(s,a,u===4,4)}\n fn mm_readA(batch: i32, row : i32, col : i32) -> ${_} {\n ${r?C:S}\n }\n\n fn mm_readB(batch: i32, row : i32, col : i32) -> ${$} {\n ${r?S:C}\n }\n\n fn mm_write(batch: i32, row : i32, col : i32, valueIn : ${k}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n {\n var value = valueIn;\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n ${d}\n ${Zr(n,s)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`}var hx=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=lm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=mm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),s&&(this.variableNames.push(\"bias\"),this.variableComponents.push(4)),i&&(this.variableNames.push(\"preluActivationWeights\"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?_p(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Ep(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`\n ${Tue(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}\n ${e}\n `}};var gx=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,\",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights,!1,4)}\n fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{\n let coords = vec4(batch, row, col, chan);\n if (coordsInBounds4D(coords, uniforms.xShape)) {\n return getX(batch, row, col, chan);\n } else {\n return 0.0;\n }\n }\n fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n let coords = vec4(row, col, xChannel, outChannel);\n if(coordsInBounds4D(coords, uniforms.wShape)) {\n return getW(row, col, xChannel, outChannel);\n } else {\n return 0.0;\n }\n }\n fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {\n let coords = ${this.isChannelsLast?\"vec4(batch, row, col, chan);\":\"vec4(batch, chan, row, col);\"}\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = valueIn;\n ${Zr(this.addBias,this.activation)}\n setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);\n }\n }\n ${G(\"index\")} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let outChannel = ${this.isChannelsLast?\"coords[3];\":\"coords[1];\"}\n let outRow = ${this.isChannelsLast?\"coords[1];\":\"coords[2];\"}\n let outCol = ${this.isChannelsLast?\"coords[2];\":\"coords[3];\"}\n var acc : f32 = 0.0;\n for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * row - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * col - uniforms.pads[1];\n for (var xChannel = 0; xChannel < ${this.isChannelsLast?\"uniforms.xShape[3];\":\"uniforms.xShape[1];\"} xChannel = xChannel + 1) {\n ${this.isChannelsLast?\"let v = readInp(batch, xRow, xCol, xChannel);\":\"let v = readInp(batch, xChannel, xRow, xCol);\"}\n let f = readFilt(row, col, xChannel, outChannel);\n acc = acc + v * f;\n }\n }\n }\n writeResult(batch, outRow, outCol, outChannel, acc);\n }\n `}};var xx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32,\n inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?\"coords[1]\":\"coords[2]\",n=this.isChannelsLast?\"coords[2]\":\"coords[1]\",s=this.isChannelsLast?\"getX(batch, xRow, xCol, ch)\":\"getX(batch, ch, xRow, xCol)\";return`\n ${G(\"index\")} {\n let coords = getCoordsFromIndex(index);\n if(index < uniforms.size) {\n let batch = coords[0];\n let row = ${o};\n let col = ${n};\n let offsetY = (row / uniforms.outWidth) * uniforms.strides[0] - uniforms.pads[0];\n let xRow = offsetY + uniforms.dilations[0] * (col / uniforms.itemsPerBlockRow);\n var value = 0.0;\n if(xRow < uniforms.xShape[${e}] && xRow >= 0) {\n let offsetX = (row % uniforms.outWidth) * uniforms.strides[1] -\n uniforms.pads[1];\n let xCol = offsetX + uniforms.dilations[1] * ((col %\n uniforms.itemsPerBlockRow) / uniforms.inChannels);\n let ch = col % uniforms.inChannels;\n if(xCol < uniforms.xShape[${t}] && xCol >= 0) {\n value = ${s};\n }\n }\n setOutputAtIndex(index, value);\n }\n }\n `}};function yx(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function _ue({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat===\"channelsLast\",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=pe({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=pe({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=yx(s.shape,p);x!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=yx(n.shape,p);x!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=$p({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=pe({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function Eue({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,C=b===\"channelsLast\",S=p*u*c,k=h*f,_=C?[t.batchSize,k,S]:[t.batchSize,S,k],$=new xx(_,C),R=[{type:\"int32\",data:[d.top,d.left]},{type:\"int32\",data:[m,l]},{type:\"int32\",data:[x,g]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*p]},{type:\"int32\",data:[c]}],D=o.runWebGPUProgram($,[r],r.dtype,R),P=[];P.push(D);let O=pe({inputs:{x:e},backend:o,attrs:{shape:[1,S,-1]}});if(P.push(O),s!=null){let U=yx(s.shape,C);U!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:U}}),P.push(s))}if(n!=null){let U=yx(n.shape,C);U!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:U}}),P.push(n))}let B=$p({a:C?D:O,b:C?O:D,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=pe({inputs:{x:B},backend:o,attrs:{shape:t.outShape}});P.push(B);for(let U of P)o.disposeData(U.dataId);return z}function bx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat===\"channelsLast\",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=A().getBool(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type===\"SAME\"||t.padInfo.type===\"VALID\")))return _ue({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=A().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),f=d>-1?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(A().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")||h<=f)return Eue({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:\"int32\",data:[t.filterHeight,t.filterWidth]},{type:\"int32\",data:[...x]},{type:\"int32\",data:[t.strideHeight,t.strideWidth]},{type:\"int32\",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new gx(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,$=c?t.outChannels:t.outHeight*t.outWidth,R=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:\"int32\",data:[_]},{type:\"int32\",data:[$]},{type:\"int32\",data:[R]});let D=o.adapterInfo.isIntel();g=new hx(t,_,$,R,p,i,u,D)}let C=[],S=[r,e];p&&(!c&&n.shape.length===1&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),S.push(n)),u&&(!c&&s.shape.length===1&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),S.push(s)),i===\"leakyrelu\"&&(b.push({type:\"float32\",data:[a]}),g.uniforms+=\" alpha : f32,\");let k=o.runWebGPUProgram(g,S,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function $ue(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return bx({x:n,filter:s,convInfo:m,backend:o})}var _V={kernelName:tn,backendName:\"webgpu\",kernelFunc:$ue};var Cx=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,\",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=e.inShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=this.isChannelsLast&&e.outChannels%4===0&&e.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1,n=`\n ${G()} {\n let batch = i32(globalId.z) / uniforms.outShape[1];\n let r = i32(globalId.z) % uniforms.outShape[1];\n let c = i32(globalId.y) * ${this.workPerThread};\n let d1 = i32(globalId.x) * 4;\n\n let dyCorner = vec2(r, c) - uniforms.pads;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd: array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = f32(dyCorner.x + wR) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) ||\n fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = f32(dyCorner.y + wC) / f32(uniforms.strides.y);\n let dyC2 = f32(dyCorner.y + 1 + wC) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n var bDyCVal = true;\n var bDyCVal2 = true;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0) {\n bDyCVal = false;\n }\n if (dyC2 < 0.0 || dyC2 >= f32(uniforms.outBackprop[2]) ||\n fract(dyC2) > 0.0) {\n bDyCVal2 = false;\n }\n\n let idyC = i32(dyC);\n let idyC2 = i32(dyC2);\n if (bDyCVal && bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n xValue = getDy(batch, idyR, idyC2, d2);\n dotProd[1] = dotProd[1] + vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n }\n } else if (bDyCVal) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n }\n } else if (bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC2, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[1] = dotProd[1] + tmpval;\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n }\n }\n }\n `;return this.isVec4?`\n ${n}\n `:`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[${o}];\n\n let dyCorner = vec2(coords[${e}], coords[${t}]) - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC = i32(dyC);\n\n for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n let xValue = ${this.isChannelsLast?\"getDy(batch, idyR, idyC, d2)\":\"getDy(batch, d2, idyR, idyC)\"};\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},wx=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=\"pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let d2 = coords[3];\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b = b + 1) {\n for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n if (${this.isChannelsLast}) {\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd = dotProd + xValue * dyValue;\n } else {\n let dyValue = getDy(b, d2, yR, yC);\n let xValue = getX(b, d1, xR, xC);\n dotProd = dotProd + xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Sx=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32,\n outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerFilter\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wF = coords.x;\n let wR = coords.y;\n let wC = coords.z;\n let d1 = coords.w;\n let d2 = coords.u;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yF = 0; yF < uniforms.outDepth; yF++) {\n let xF = wF + yF * uniforms.strides[0] - uniforms.pads[0];\n if (xF < 0 || xF >= uniforms.inDepth) {\n continue;\n }\n\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[1] - uniforms.pads[1];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[2] - uniforms.pads[2];\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yF, yR, yC, d2);\n let xValue = getX(b, xF, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Ix=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerInput\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let d1 = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyFCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let dyF = f32(dyFCorner + wF) / f32(uniforms.strides[0]);\n if (dyF < 0.0 || dyF >= f32(uniforms.outDepth) || fract(dyF) > 0.0) {\n continue;\n }\n let idyF = i32(dyF);\n\n let wFPerm = uniforms.filterDims[0] - 1 - wF;\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n let wRPerm = uniforms.filterDims[1] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let wCPerm = uniforms.filterDims[2] - 1 - wC;\n\n for (var d2 = 0; d2 < uniforms.outChannels; d2++) {\n let xValue = getDy(batch, idyF, idyR, idyC, d2);\n let wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Rue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new wx(m),f=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]},{type:\"int32\",data:[m.inHeight]},{type:\"int32\",data:[m.inWidth]}];return t.runWebGPUProgram(d,[n,s],n.dtype,f)}var EV={kernelName:Fi,backendName:\"webgpu\",kernelFunc:Rue};function Due(r=4){let e=s=>{switch(s){case 1:return\"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n return vec4(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${`\n let outRow = row / uniforms.outShape[2];\n let outCol = row % uniforms.outShape[2];\n\n let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.strides[0]);\n let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.strides[1]);\n if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n let coord = vec4(\n batch,\n i32(xR),\n i32(xC),\n col % uniforms.outBackprop[3]);\n return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}\n }\n return ${Ae(r)}(0.0);`;return`\n fn mm_readA(batch: i32, row : i32, col : i32) -> ${Ae(r)} {\n ${o}\n }\n\n fn mm_readB(batch: i32, row : i32, col : i32) -> ${Ae(r)} {\n let coordX = uniforms.filterDims.x - 1 -\n row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let coordY = uniforms.filterDims.y - 1 -\n (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n coordX >= 0 && coordY >= 0) {\n let rowInner = row % uniforms.outBackprop[3];\n let coord = vec4(coordX, coordY, col, rowInner);\n ${e(r)}\n }\n return ${Ae(r)}(0.0);\n }\n\n fn mm_write(batch: i32, row : i32, col : i32, valueInput : ${Ae(r)}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n var value = valueInput;\n let outCoord = vec4(\n batch,\n row / uniforms.outShape[2],\n row % uniforms.outShape[2],\n col);\n result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;\n }\n }`}var vx=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=lm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=mm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?_p(this.elementsPerThread,this.workgroupSize):Ep(this.elementsPerThread,this.workgroupSize);return`\n ${Due(this.isVec4?4:1)}\n ${e}\n `}};function Aue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:\"int32\",data:[m.filterHeight,m.filterWidth]},{type:\"int32\",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(A().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")||m.dataFormat!==\"channelsLast\")f=new Cx(m);else{f=new vx(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:\"uint32\",data:[h]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[x]})}return t.runWebGPUProgram(f,[n,s],\"float32\",d)}var $V={kernelName:rn,backendName:\"webgpu\",kernelFunc:Aue};var kx=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3dnaive\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords.x;\n let d2 = coords.u;\n\n let xFRCCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xFCorner = xFRCCorner.x;\n let xRCorner = xFRCCorner.y;\n let xCCorner = xFRCCorner.z;\n\n let inputDepthNearestVec4 = (uniforms.xShape.u / 4) * 4;\n let inputDepthVec4Remainder = uniforms.xShape.u % 4;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let xF = xFCorner + wF * uniforms.dilations[0];\n if (xF < 0 || xF >= uniforms.xShape.y) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let xR = xRCorner + wR * uniforms.dilations[1];\n if (xR < 0 || xR >= uniforms.xShape.z) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let xC = xCCorner + wC * uniforms.dilations[2];\n if (xC < 0 || xC >= uniforms.xShape.w) {\n continue;\n }\n\n for (var d1 = 0; d1 < inputDepthNearestVec4; d1 += 4) {\n let xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n let wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (inputDepthVec4Remainder == 1) {\n dotProd += getX(batch, xF, xR, xC, inputDepthNearestVec4) *\n getW(wF, wR, wC, inputDepthNearestVec4, d2);\n } else if (inputDepthVec4Remainder == 2) {\n let xValues = vec2(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1)\n );\n let wValues = vec2(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (inputDepthVec4Remainder == 3) {\n let xValues = vec3(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2)\n );\n let wValues = vec3(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }`}};function Fue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=[u.padInfo.front,u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new kx(u),d=dt(n.dtype,s.dtype);return t.runWebGPUProgram(m,[n,s],d,l)}var RV={kernelName:on,backendName:\"webgpu\",kernelFunc:Fue};function Pue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Sx(u),l=[{type:\"int32\",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.batchSize]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.inDepth]},{type:\"int32\",data:[u.inHeight]},{type:\"int32\",data:[u.inWidth]}];return t.runWebGPUProgram(c,[n,s],s.dtype,l)}var DV={kernelName:ja,backendName:\"webgpu\",kernelFunc:Pue};function Oue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,pad:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,a,1,i),c=new Ix(u),l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.outChannels]}];return t.runWebGPUProgram(c,[n,s],n.dtype,l)}var AV={kernelName:nn,backendName:\"webgpu\",kernelFunc:Oue};var Mue=ye({opType:Z.COS}),FV={kernelName:\"Cos\",backendName:\"webgpu\",kernelFunc:Mue};var Lue=ye({opType:Z.COSH}),PV={kernelName:an,backendName:\"webgpu\",kernelFunc:Lue};var Nx=class{constructor(e,t,o,n){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let height_ratio = f32(${o});\n let width_ratio = f32(${a});\n let b = coords[0];\n let y = coords[1];\n let x = coords[2];\n let d = coords[3];\n // get box vals\n let y1 = getBoxes(b, 0);\n let x1 = getBoxes(b, 1);\n let y2 = getBoxes(b, 2);\n let x2 = getBoxes(b, 3);\n // get image in batch index\n let bInd = i32(round(getBoxInd(b)));\n if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n return;\n }\n let height_scale = ${n};\n let width_scale = ${i};\n let in_y = ${s};\n if( in_y < 0.0 || in_y > ${e} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let in_x = ${p};\n if( in_x < 0.0 || in_x > ${t} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let sourceFracIndexCR = vec2(in_x,in_y);\n if(${this.methodId} == 1) {\n // Compute the four integer indices.\n let sourceFloorCR = vec2(sourceFracIndexCR);\n let sourceCeilCR = vec2(ceil(sourceFracIndexCR));\n let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n let fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n let top = topLeft + (topRight - topLeft) * fracCR.x;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n let newValue = top + (bottom - top) * fracCR.y;\n setOutputAtIndex(index, newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n let sourceNearestCR = vec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n let newValue = getImage(\n bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutputAtIndex(index, newValue);\n }\n }\n }\n `}};var Bue=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Nx(n.shape[3],s.shape,i,p),l=[{type:\"float32\",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],\"float32\",l)},OV={kernelName:cn,backendName:\"webgpu\",kernelFunc:Bue};var Dp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Dp||(Dp={}));var xm=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Dp.Prod?\"1.0\":\"0.0\",o=this.exclusive?t:`getX(${MV(e,\"coords\",this.op)})`,n=this.outputShape[this.outputShape.length-1],s=\"\",a=\"\";return this.exclusive?(s=this.reverse?`end != ${n-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(s=this.reverse?`end + pow2 < ${n}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var coords = getCoordsFromIndex(index);\n\n let end = ${LV(e,\"coords\",this.op)};\n var val = ${o};\n let pow2 = i32(pow(2.0, uniforms.index));\n if (${s}) {\n let idx = ${a};\n ${LV(e,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${MV(e,\"coords\",this.op)});\n }\n setOutputAtIndex(index, val);\n }\n }\n `}};function MV(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function LV(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Tx(r,e,t,o,n,s){let a=e.shape.length,i=w.getAxesPermutation([o],a),p=e;i!=null&&(p=xr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new xm(r,p.shape,!1,s),f=l,h=[{type:\"float32\",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new xm(r,p.shape,n,s),d=l,f=[{type:\"float32\",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=xr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function zue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Tx(Dp.Prod,n,t,s,a,i)}var BV={kernelName:un,backendName:\"webgpu\",kernelFunc:zue};function Vue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Tx(Dp.Sum,n,t,s,a,i)}var zV={kernelName:pn,backendName:\"webgpu\",kernelFunc:Vue};function Wue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=vt({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new Qc(m,c,i),g=[{type:\"int32\",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var VV={kernelName:ra,backendName:\"webgpu\",kernelFunc:Wue};var _x=class{constructor(e,t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let h = ${this.getHeightCoordString()};\n let w = ${this.getWidthCoordString()};\n let d = ${this.getDepthCoordString()};\n\n let in_h = h / uniforms.blockSize;\n let offset_h = h % uniforms.blockSize;\n let in_w = w / uniforms.blockSize;\n let offset_w = w % uniforms.blockSize;\n let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n ${this.getOutputDepthSize()};\n let in_d = d + offset_d;\n\n let rlt = ${this.getInputSamplingString()};\n setOutputAtIndex(index, rlt);\n }\n }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function Uue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=[{type:\"int32\",data:[s]}],g=new _x(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var WV={kernelName:ln,backendName:\"webgpu\",kernelFunc:Uue};var Ex=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pads : vec2, inDims : vec2,\",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push(\"bias\"),a&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return`\n ${dr(this.activation,this.hasPreluActivation,!1,4)}\n\n var mm_Asub : array, ${o}>;\n var mm_Bsub : array, ${this.filterHeight}>;\n fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n var value = 0.0;\n if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n {\n value = getX(batch, channel, row, col);\n }\n return value;\n }\n\n ${G()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.zw) - uniforms.pads;\n let channelMul = uniforms.wShape[3];\n let d1 = coords[1] / channelMul;\n let q = coords[1] % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n\n // Load one tile of X into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {\n for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {\n let rowOffset = inputRow - localRow;\n let colOffset = inputCol - localCol;\n mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n }\n }\n\n // Load one tile of W into local memory.\n var wIndex = i32(localIndex);\n ${e, inDims : vec2, virtualWidth : i32,\",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=e.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let s=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=X(s),this.dispatch=H(this.dispatchLayout,s,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return`\n ${dr(this.activation,this.hasPreluActivation,!0,4)}\n fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 {\n var value = vec4(0.0);\n if (col >=0 && col < uniforms.inDims[1]) {\n value = getX(batch, row, col, channel);\n }\n return value;\n }\n\n ${G(\"index\")} {\n let width0 = uniforms.outShape[3] / ${this.outputComponent};\n let d1 = (index % width0) * ${this.outputComponent};\n var index1 = index / width0;\n let width1 = uniforms.virtualWidth / ${this.workPerThread};\n let c = (index1 % width1) * ${this.workPerThread};\n index1 = index1 / width1;\n let r = index1 % uniforms.outShape[1];\n let batch = index1 / uniforms.outShape[1];\n\n let xRCCorner = vec2(r, c) * vec2(${t}, ${o}) - uniforms.pads;\n\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n var xVals : array, ${e}>;\n var dotProd : array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n\n // Use constant instead of uniform can give better performance.\n for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n let xR = xRCorner + wR;\n if (xR >=0 && xR < uniforms.inDims[0]) {\n for (var i = 0; i < ${e}; i++) {\n xVals[i] = readX(batch, xR, xCCorner + i, d1);\n }\n for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n let wValue = getW(wR, wC, d1, 0);\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]);\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = dotProd[i];\n ${Zr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n }\n `}};var el=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32,\n filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?\"getX(batch, xR, xC, d1);\":\"getX(batch, d1, xR, xC);\";return`\n ${dr(this.activation,this.hasPreluActivation,!1,4)}\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.${this.isChannelsLast?\"yz\":\"zw\"}) * uniforms.strides - uniforms.pads;\n let d2 = coords[${this.isChannelsLast?3:1}];\n let channelMul = uniforms.wShape[3];\n let d1 = d2 / channelMul;\n let q = d2 % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n let inputRowEnd = inputRowStart + uniforms.filterHeight *\n uniforms.dilations[0];\n let inputColEnd = inputColStart + uniforms.filterWidth *\n uniforms.dilations[1];\n\n // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get\n // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n var value = 0.0;\n\n // Extract if checking out of for loop for performance.\n if (inputRowStart >= 0 && inputColStart >= 0 &&\n inputRowEnd < uniforms.inDims[0] &&\n inputColEnd < uniforms.inDims[1]) {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n } else {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n if (xR < 0 || xR >= uniforms.inDims[0]) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n if (xC < 0 || xC >= uniforms.inDims[1]) {\n continue;\n }\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n }\n ${Zr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n `}};function Gue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:\"int32\",data:[d.padInfo.top,d.padInfo.left]},{type:\"int32\",data:[d.inHeight,d.inWidth]}],h=d.dataFormat===\"channelsLast\",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new Ex(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?(g=new Jc(d),f.push({type:\"int32\",data:[g.virtualWidth]})):(g=new el(d),f.push({type:\"int32\",data:[d.filterHeight]},{type:\"int32\",data:[d.filterWidth]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var UV={kernelName:mn,backendName:\"webgpu\",kernelFunc:Gue};var $x=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32,\n outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_filter\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let dm = coords[3];\n let d2 = d1 * uniforms.channelMul + dm;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Rx=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_input\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[3];\n let dyCorner = coords.yz - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n\n let idyR = i32(dyR);\n let wRPerm = uniforms.filterDims[0] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n\n let idyC = i32(dyC);\n let wCPerm = uniforms.filterDims[1] - 1 - wC;\n\n for (var dm = 0; dm < uniforms.channelMul; dm++) {\n let d2 = d1 * uniforms.channelMul + dm;\n let xValue = getDy(batch, idyR, idyC, d2);\n let wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Hue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new $x(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.inHeight]},{type:\"int32\",data:[l.inWidth]},{type:\"int32\",data:[l.batchSize]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],\"float32\",d)}var GV={kernelName:Pi,backendName:\"webgpu\",kernelFunc:Hue};function Kue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Rx(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.filterHeight-1-l.padInfo.top,l.filterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],n.dtype,d)}var HV={kernelName:Oi,backendName:\"webgpu\",kernelFunc:Kue};var Dx=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"diag\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);\n setOutputAtIndex(index, value);\n }\n }\n `}};function que(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Dx(s),p=t.runWebGPUProgram(i,[a],a.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var KV={kernelName:oa,backendName:\"webgpu\",kernelFunc:que};var Ax=class{constructor(e){this.variableNames=[\"x\",\"w\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"dilation2d\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let neg_infinity = -3.4e38;\n let coords = getOutputCoords();\n let batch = coords.x;\n let d1 = coords.w;\n let outTopLeftCorner = coords.yz * uniforms.strides - uniforms.pads;\n let hBeg = outTopLeftCorner.x;\n let wBeg = outTopLeftCorner.y;\n\n var curVal = neg_infinity;\n for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) {\n let hIn = hBeg + h * uniforms.dilations[0];\n\n if (hIn >= 0 && hIn < uniforms.xShape[1]) {\n for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) {\n let wIn = wBeg + w * uniforms.dilations[1];\n\n if (wIn >= 0 && wIn < uniforms.xShape[2]) {\n let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1);\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n setOutputAtIndex(index, curVal);\n }\n }\n `}};function jue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationHeight,u.dilationWidth]}],m=new Ax(u);return t.runWebGPUProgram(m,[n,s],n.dtype,l)}var qV={kernelName:dn,backendName:\"webgpu\",kernelFunc:jue};var Fx=class{constructor(e,t){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.inShape,this.dispatchLayout=X(e.outShape),this.dispatch=H(this.dispatchLayout,e.outShape,this.workgroupSize),t!==\"float32\"&&t!==\"int32\")throw new Error(`Dilation2DBackpropInput only supports float32 and int32\n types, does not support ${t} type.`);this.type=t,this.shaderKey=\"dilation2DBackpropInput\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var xRMax = 0;\n var xCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n xRMax = xR;\n xCMax = xC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.xShape[3] *\n (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b));\n let value = getDy(b, r, c, d);\n ${Qr(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}},Px=class{constructor(e,t,o){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(e.outShape),this.dispatch=H(this.dispatchLayout,e.outShape,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"dilation2DBackpropFilter\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var wRMax = 0;\n var wCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n wRMax = wR;\n wCMax = wC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]);\n let value = getDy(b, r, c, d);\n ${Qr(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}};function Xue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=s.dtype,m=new Px(c,s.shape,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=vt({backend:t,attrs:{shape:s.shape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var jV={kernelName:Li,backendName:\"webgpu\",kernelFunc:Xue};function Yue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=n.dtype,m=new Fx(c,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=vt({backend:t,attrs:{shape:c.inShape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var XV={kernelName:Mi,backendName:\"webgpu\",kernelFunc:Yue};var Ox=class{constructor(e,t,o){this.variableNames=[\"Image\"],this.uniforms=\"alpha: f32,\",this.workgroupSize=[64,1,1],this.pixelsOpType=wi.DRAW,this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.type=t,this.textureFormat=o,this.shaderKey=`draw_${t}_${o}`}getUserCode(){let e,t=this.type===\"float32\"?\"value\":\"value / 255.0\";return e=`\n if (uniforms.numChannels == 1) {\n rgba[0] = ${t};\n rgba[1] = ${t};\n rgba[2] = ${t};\n } else {\n rgba[d] = ${t};\n }`,`\n @group(0) @binding(0) var outImage : texture_storage_2d<${this.textureFormat}, write>;\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var rgba = vec4(0.0, 0.0, 0.0, uniforms.alpha);\n for (var d = 0; d < uniforms.numChannels; d = d + 1) {\n let value = f32(inBuf[index * uniforms.numChannels + d]);\n ${e}\n }\n rgba.x = rgba.x * rgba.w;\n rgba.y = rgba.y * rgba.w;\n rgba.z = rgba.z * rgba.w;\n let coords = getCoordsFromIndex(index);\n textureStore(outImage, vec2(coords.yx), rgba);\n }\n }\n `}};function Que(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{canvas:s,options:a}=o,[i,p]=n.shape.slice(0,2),{imageOptions:u}=a||{},c=(u==null?void 0:u.alpha)||1,l=t.device.features.has(\"bgra8unorm-storage\")?\"bgra8unorm\":\"rgba8unorm\",m=[i,p],d=new Ox(m,n.dtype,l);s.width=p,s.height=i;let f=\"webgpu\",h=s.getContext(f),g;h||(g=new OffscreenCanvas(p,i),h=g.getContext(f));let x=n.shape.length===3?n.shape[2]:1;h.configure({device:t.device,format:l,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:\"premultiplied\"});let b=\"int32\",C=t.makeTensorInfo(m,b),S=t.tensorMap.get(C.dataId);S.resource=h.getCurrentTexture(),S.external=!0;let k=[{type:\"uint32\",data:[x]},{type:\"float32\",data:[c]}];if(t.runWebGPUProgram(d,[n],b,k,C),g){let _=s.getContext(\"2d\");if(!_)throw new Error(\"Please make sure this canvas has only been used for 2d or webgpu context!\");_.drawImage(g,0,0)}return t.disposeData(C.dataId),n}var YV={kernelName:$u,backendName:\"webgpu\",kernelFunc:Que};var i0=et({opType:fe.MUL,cpuKernelImpl:Rz,supportsComplex:!0}),QV={kernelName:Xn,backendName:\"webgpu\",kernelFunc:i0};function u0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,\"sum\",t)}var ZV={kernelName:\"Sum\",backendName:\"webgpu\",kernelFunc:u0};function Zue(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=u0({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var JV={kernelName:Bi,backendName:\"webgpu\",kernelFunc:Zue};var Jue=ye({opType:Z.ELU}),eW={kernelName:\"Elu\",backendName:\"webgpu\",kernelFunc:Jue};var epe=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=new Ii(fe.ELU_DER,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],o.dtype)},tW={kernelName:Xa,backendName:\"webgpu\",kernelFunc:epe};var tpe=et({opType:fe.EQUAL,dtype:\"bool\",cpuKernelImpl:gz}),rW={kernelName:xn,backendName:\"webgpu\",kernelFunc:tpe};var rpe=ye({opType:Z.ERF}),oW={kernelName:\"Erf\",backendName:\"webgpu\",kernelFunc:rpe};var ope=ye({opType:Z.EXP,cpuKernelImpl:xz,dtype:\"float32\"}),nW={kernelName:\"Exp\",backendName:\"webgpu\",kernelFunc:ope};function Mx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var sW={kernelName:na,backendName:\"webgpu\",kernelFunc:Mx};var npe=ye({opType:Z.EXPM1,cpuKernelImpl:yz}),aW={kernelName:bn,backendName:\"webgpu\",kernelFunc:npe};var ym=class{constructor(e,t){this.variableNames=[\"real\",\"imag\"],this.outputShape=[],this.uniforms=\"exponentMultiplier : f32, denominator: f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return`\n fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {\n ${this.component===\"real\"?\"return real * expR - imag * expI;\":\"return real * expI + imag * expR;\"}\n }\n\n fn mulMatDFT(batch: i32, index: i32) -> f32 {\n let indexRatio = f32(index) / f32(uniforms.realShape[1]);\n let exponentMultiplierTimesIndexRatio =\n uniforms.exponentMultiplier * indexRatio;\n\n var result = 0.0;\n\n for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {\n // x = (-2|2 * PI / N) * index * i;\n let x = exponentMultiplierTimesIndexRatio * f32(i);\n let expR = cos(x);\n let expI = sin(x);\n let real = getReal(batch, i);\n let imag = getImag(batch, i);\n\n result = result +\n unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;\n }\n\n return result;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));\n }\n }\n `}};function Lx(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=pe({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new ym(\"real\",u),l=new ym(\"imag\",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:\"float32\",data:[d]},{type:\"float32\",data:[f]}],g=t.runWebGPUProgram(c,m,\"float32\",h);i.push(g);let x=t.runWebGPUProgram(l,m,\"float32\",h);i.push(x);let b=xo({inputs:{real:g,imag:x},backend:t});i.push(b);let C=pe({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(S=>t.disposeData(S.dataId)),C}function spe(r){let{inputs:e,backend:t}=r,{input:o}=e;return Lx(o,!1,t)}var iW={kernelName:\"FFT\",backendName:\"webgpu\",kernelFunc:spe};var Bx=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordX = uniforms.xShape[2] - coords[2] - 1;\n let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var uW={kernelName:Cn,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Bx(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var ape=ye({opType:Z.FLOOR,cpuKernelImpl:bz}),pW={kernelName:wn,backendName:\"webgpu\",kernelFunc:ape};var ipe=et({opType:fe.FLOOR_DIV,cpuKernelImpl:Cz,dtype:\"int32\"}),cW={kernelName:Sn,backendName:\"webgpu\",kernelFunc:ipe};var zx=class{constructor(e,t,o=!1){this.pixelsOpType=wi.FROM_PIXELS,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?\"textureLoad(src, vec2(coords.yx));\":\"textureLoad(src, vec2(coords.yx), 0)\";return`\n @binding(1) @group(0) var src: ${this.importVideo?\"texture_external\":\"texture_2d\"};\n ${G(\"index\")} {\n let flatIndex = index * uniforms.numChannels;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n let values = ${e};\n for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n result[flatIndex + i] = i32(floor(255.0 * values[i]));\n }\n }\n }\n `}};var lW={kernelName:Du,backendName:\"webgpu\",kernelFunc:upe},tl,p0=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function upe(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o;if(n==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!=\"undefined\"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!=\"undefined\"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=A().getBool(\"WEBGPU_IMPORT_EXTERNAL_TEXTURE\")&&a,f=a||i;if(u||p||f){let b;if(d)b=t.device.importExternalTexture({source:n});else{if(f){let L=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(tl==null||L!==p0)&&(p0=L,tl=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:p0})),tl.canvas.width=c,tl.canvas.height=l,tl.drawImage(n,0,0,c,l),n=tl.canvas}let P=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,M=t.textureManager.acquireTexture(m[1],m[0],\"rgba8unorm\",P);t.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b=M}let C=y.sizeFromShape(m),S=y.computeStrides(m),k=new zx(m,s,d),_=[{type:\"uint32\",data:[C]},{type:\"uint32\",data:[s]},{type:\"uint32\",data:[...S]}],$=t.makeTensorInfo([l,c],\"int32\"),R=t.tensorMap.get($.dataId);R.resource=b;let D=t.runWebGPUProgram(k,[$],\"int32\",_);return t.disposeData($.dataId),D}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,C=0;for(let S=0;S(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0)));\n }\n }\n `}};var mW={kernelName:In,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=e,u=t,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new Vx(o.shape,a.shape,i.shape,l,m),f=[{type:\"float32\",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function ppe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return bx({x:n,filter:s,convInfo:g,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var dW={kernelName:Io,backendName:\"webgpu\",kernelFunc:ppe};function cpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=w.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:\"int32\",data:[h.padInfo.top,h.padInfo.left]},{type:\"int32\",data:[h.inHeight,h.inWidth]}],S;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(S=new Jc(h,x,m,b),C.push({type:\"int32\",data:[S.virtualWidth]})):(S=new el(h,x,m,b),C.push({type:\"int32\",data:[h.filterHeight]},{type:\"int32\",data:[h.filterWidth]},{type:\"int32\",data:[h.strideHeight,h.strideWidth]},{type:\"int32\",data:[h.dilationHeight,h.dilationWidth]})),m===\"leakyrelu\"&&(C.push({type:\"float32\",data:[d]}),S.uniforms+=\" alpha : f32,\"),t.runWebGPUProgram(S,g,\"float32\",C)}var fW={kernelName:vo,backendName:\"webgpu\",kernelFunc:cpe};var Wx=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${ft(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var flattenIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexTemp = i32(round(getIndices(coords[0], j)));\n let strideNum = ${e};\n flattenIndex = flattenIndex + indexTemp * strideNum;\n }\n\n setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n }\n }\n `}};function lpe(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=pe({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=pe({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let b=t.readSync(n.dataId),C=t.bufferSync(o),S=wz(b,C,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,S.values)}let f=new Wx(a,[u,c]),h=[{type:\"int32\",data:[a]},{type:\"int32\",data:l}],g=t.runWebGPUProgram(f,[d,m],d.dtype,h),x=pe({inputs:{x:g},backend:t,attrs:{shape:p}});return t.disposeData(m.dataId),t.disposeData(d.dataId),t.disposeData(g.dataId),x}var hW={kernelName:vn,backendName:\"webgpu\",kernelFunc:lpe};var Ux=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=mpe(this.aShape);return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let resRC = getCoordsFromIndex(index);\n let indexZ = i32(getIndices(resRC.x, resRC.z));\n let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n setOutputAtIndex(index, inBounds * getA(${e}));\n }\n }\n `}};function mpe(r){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],t=[];for(let o=0;ot.disposeData(D.dataId)),t.makeTensorInfo(u.outputShape,R.dtype,R.values)}let h=new Ux(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=pe({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var gW={kernelName:aa,backendName:\"webgpu\",kernelFunc:c0};var dpe=et({opType:fe.GREATER,cpuKernelImpl:vz,dtype:\"bool\"}),xW={kernelName:kn,backendName:\"webgpu\",kernelFunc:dpe};var fpe=et({opType:fe.GREATER_EQUAL,dtype:\"bool\",cpuKernelImpl:Iz}),yW={kernelName:Nn,backendName:\"webgpu\",kernelFunc:fpe};function hpe(r){let{inputs:e,backend:t}=r,{input:o}=e;return Lx(o,!0,t)}var bW={kernelName:Vi,backendName:\"webgpu\",kernelFunc:hpe};var gpe=ye({opType:Z.IS_FINITE,dtype:\"bool\"}),CW={kernelName:Tn,backendName:\"webgpu\",kernelFunc:gpe};var xpe=ye({opType:Z.IS_INF,dtype:\"bool\"}),wW={kernelName:_n,backendName:\"webgpu\",kernelFunc:xpe};var ype=ye({opType:Z.IS_NAN,dtype:\"bool\"}),SW={kernelName:En,backendName:\"webgpu\",kernelFunc:ype};function bpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:\"float32\",data:[s]}],i=new Jr(n.shape,Z.LEAKYRELU,\"alpha : f32,\");return t.runWebGPUProgram(i,[n],\"float32\",a)}var IW={kernelName:$n,backendName:\"webgpu\",kernelFunc:bpe};var Cpe=et({opType:fe.LESS,dtype:\"bool\",cpuKernelImpl:Nz}),vW={kernelName:Rn,backendName:\"webgpu\",kernelFunc:Cpe};var wpe=et({opType:fe.LESS_EQUAL,dtype:\"bool\",cpuKernelImpl:kz}),kW={kernelName:Dn,backendName:\"webgpu\",kernelFunc:wpe};var Gx=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"start : f32, step : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"linSpace\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);\n }\n }\n `}};function Spe(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=(n-o)/(s-1),i=new Gx(s),p=[{type:\"float32\",data:[o]},{type:\"float32\",data:[a]}];return e.runWebGPUProgram(i,[],\"float32\",p)}var NW={kernelName:An,backendName:\"webgpu\",kernelFunc:Spe};var Ipe=ye({opType:Z.LOG,cpuKernelImpl:Tz}),TW={kernelName:\"Log\",backendName:\"webgpu\",kernelFunc:Ipe};var vpe=ye({opType:Z.LOG1P}),_W={kernelName:Pn,backendName:\"webgpu\",kernelFunc:vpe};var kpe=et({opType:fe.LOGICAL_AND,dtype:\"bool\"}),EW={kernelName:On,backendName:\"webgpu\",kernelFunc:kpe};var Npe=ye({opType:Z.LOGICAL_NOT}),$W={kernelName:Mn,backendName:\"webgpu\",kernelFunc:Npe};var Tpe=et({opType:fe.LOGICAL_OR}),RW={kernelName:Ln,backendName:\"webgpu\",kernelFunc:Tpe};var DW=`\n var powValue = 0.0;\n let basis = uniforms.bias + uniforms.alpha * sum;\n if (uniforms.beta == 0.5) {\n powValue = inverseSqrt(basis);\n } else if (uniforms.beta == 1.0) {\n powValue = 1.0 / basis;\n } else {\n powValue = exp(log(basis) * (-uniforms.beta));\n }\n`,Hx=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let x = getX(b, r, c, d);\n var sum = 0.0;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let idx = d + i;\n if (idx >= 0 && idx < uniforms.xShape[3]) {\n let z = getX(b, r, c, idx);\n sum = sum + z * z;\n }\n }\n ${DW}\n\n setOutputAtIndex(index, x * powValue);\n }\n }\n `}},Kx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey=\"lrn_shared\"}getUserCode(){return`\n var lrnSub: array;\n const elementsPerWorkgroup = ${this.elementsPerWorkgroup};\n const maxAllowRadius = ${this.maxAllowRadius};\n\n ${G()} {\n let localDepth = i32(localId.x);\n let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup;\n let xDepth = workgroupDepth + localDepth - maxAllowRadius;\n let b = i32(globalId.z) / uniforms.xShape[1];\n let r = i32(globalId.z) - b * uniforms.xShape[1];\n let c = i32(globalId.y);\n let d = workgroupDepth + localDepth;\n\n var x = 0.0;\n if (xDepth >= 0 && xDepth < uniforms.xShape[3]) {\n x = getX(b, r, c, xDepth);\n }\n lrnSub[localDepth] = x;\n workgroupBarrier();\n\n if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) {\n var sum = 0.0;\n let index = localDepth + maxAllowRadius;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let z = lrnSub[index + i];\n sum = sum + z * z;\n }\n ${DW}\n\n setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);\n }\n } `}};function _pe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new Hx(n.shape):u=new Kx(n.shape,s);let c=[{type:\"int32\",data:[s]},{type:\"float32\",data:[a]},{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var AW={kernelName:\"LRN\",backendName:\"webgpu\",kernelFunc:_pe};var qx=class{constructor(e){this.outputShape=[],this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.uniforms=\"depthRadius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn_grad\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n\n let MIN_DEPTH_BEGIN = 0;\n let MAX_DEPTH_END = uniforms.outShape[3];\n var result = 0.0;\n for (var d = MIN_DEPTH_BEGIN; d < MAX_DEPTH_END; d++) {\n let depthBegin = max(MIN_DEPTH_BEGIN, d - uniforms.depthRadius);\n let depthEnd = min(MAX_DEPTH_END, d + uniforms.depthRadius + 1);\n\n var norm = 0.0;\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n } else {\n break;\n }\n }\n\n norm = uniforms.alpha * norm + uniforms.bias;\n\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n var dyi = -2.0 * uniforms.alpha * uniforms.beta\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * uniforms.beta);\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n } else {\n break;\n }\n }\n }\n\n setOutputAtIndex(index, result);\n }\n }\n `}};function Epe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new qx(n.shape),m=[{type:\"int32\",data:[i]},{type:\"float32\",data:[p]},{type:\"float32\",data:[u]},{type:\"float32\",data:[c]}];return t.runWebGPUProgram(l,[n,s,a],n.dtype,m)}var FW={kernelName:Ya,backendName:\"webgpu\",kernelFunc:Epe};var $pe=et({opType:fe.MAX,cpuKernelImpl:Ez}),PW={kernelName:Vn,backendName:\"webgpu\",kernelFunc:$pe};function Rpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,c=w.computePool2DInfo(n.shape,s,a,1,i,p);return ax(n,c,\"max\",t)}var OW={kernelName:Wn,backendName:\"webgpu\",kernelFunc:Rpe};function Dpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Iu(l,\"max\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var MW={kernelName:ia,backendName:\"webgpu\",kernelFunc:Dpe};var jx=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool2DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] - 1;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR += uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC += uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wR * uniforms.filterDims[1] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Xx=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool3DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] * uniforms.filterDims[2] - 1;\n\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wD * uniforms.filterDims[1] * uniforms.filterDims[2] + wR * uniforms.filterDims[2] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n }\n\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Ape(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Iu(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.inDepth,m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[a],\"int32\",f),g=new Xx(m);f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterDepth-1-m.padInfo.front,m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outDepth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],a.dtype,f);return t.disposeData(h.dataId),x}var LW={kernelName:Gi,backendName:\"webgpu\",kernelFunc:Ape};function Fpe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;fm([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=new Ba(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[i],\"int32\",f),g=new jx(m);f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],i.dtype,f);return t.disposeData(h.dataId),x}var BW={kernelName:Ui,backendName:\"webgpu\",kernelFunc:Fpe};function Ppe(r){let{inputs:e,backend:t,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=e;y.assert(p.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(p.shape,n,s,u,a),l=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]}],m=new Ba(c,\"max\",!1),d=t.runWebGPUProgram(m,[p],p.dtype,l);m=new Ba(c,\"max\",!0,!0,i);let f=t.runWebGPUProgram(m,[p],\"int32\",l);return[d,f]}var zW={kernelName:ua,backendName:\"webgpu\",kernelFunc:Ppe};function Ope(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,\"min\",t)}var VW={kernelName:\"Min\",backendName:\"webgpu\",kernelFunc:Ope};var Mpe=et({opType:fe.MIN,cpuKernelImpl:$z}),WW={kernelName:Hn,backendName:\"webgpu\",kernelFunc:Mpe};var Yx=class{constructor(e,t,o){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(\",\"),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:\"\"}`).join(\",\"),n=e===1?\"start\":\"start[i]\",s=e===1?\"end\":\"end[i]\",a=e===1?\"outC\":\"outC[i]\",i=ft(e),p=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let start = ${i}(${t});\n let end = ${i}(${o});\n var outC = getCoordsFromIndex(index);\n for (var i = 0; i < ${e}; i = i + 1) {\n if (${a} < ${n}) {\n ${a} = ${n} * 2 - ${a} - ${this.offset};\n } else if(${a} >= ${s}) {\n ${a} = (${s} - 1) * 2 - ${a} + ${this.offset};\n }\n }\n let coords = outC - start;\n setOutputAtIndex(index, getX(${p}));\n }\n }\n `}};var UW={kernelName:Kn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:\"int32\",data:[c[0],c[1]]})),p=new Yx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var Lpe=et({opType:fe.MOD}),GW={kernelName:\"Mod\",backendName:\"webgpu\",kernelFunc:Lpe};var Qx=class{constructor(e,t){this.variableNames=[\"probs\"],this.outputShape=[],this.uniforms=\"seed : f32, numOutcomes: i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"multinomial\"}getUserCode(){return`\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n fn random (seed : f32, resultUV : vec2) -> f32 {\n let HASHSCALE1 = 443.8975;\n let p = resultUV * seed;\n var p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 = p3 + dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n\n let resUV = vec2(f32(coords[1]) / f32(uniforms.outShape[1]),\n f32(coords[0]) / f32(uniforms.outShape[0]));\n let r = random(uniforms.seed, resUV);\n var cdf = 0.0;\n for (var i = 0; i < uniforms.numOutcomes - 1; i = i + 1) {\n cdf = cdf + getProbs(batch, i);\n\n if (r < cdf) {\n setOutputAtIndexI32(index, i);\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutputAtIndexI32(index, uniforms.numOutcomes - 1);\n }\n }\n `}};var Zx=class{constructor(e){this.variableNames=[\"logits\"],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey=\"softmax\"}getUserCode(){return`\n var buf : array;\n var rowMaxShared : f32;\n var rowSumShared : f32;\n const blockSize = ${this.workgroupSize[0]};\n ${G(\"index\")} {\n let row = index / blockSize;\n let tid = i32(localId.x);\n let cols = uniforms.outShape[1];\n\n var threadMax = -3.402823e+38f;\n for (var col = tid; col < cols; col += blockSize) {\n let value = getLogits(row, col);\n threadMax = max(threadMax, value);\n }\n if (tid < cols) {\n buf[tid] = threadMax;\n }\n workgroupBarrier();\n\n var reduceSize = min(cols, blockSize);\n for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) {\n reduceSize = currSize + (reduceSize & 1);\n if (tid < currSize) {\n buf[tid] = max(buf[tid], buf[tid + reduceSize]);\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowMaxShared = buf[0];\n }\n workgroupBarrier();\n\n var threadSum = 0.0;\n for (var col = tid; col < cols; col += blockSize) {\n let subExp = exp(getLogits(row, col) - rowMaxShared);\n threadSum += subExp;\n }\n buf[tid] = threadSum;\n workgroupBarrier();\n\n for (var currSize = blockSize >> 1; currSize > 0; currSize = currSize >> 1) {\n if (tid < currSize) {\n buf[tid] = buf[tid] + buf[tid + currSize];\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowSumShared = buf[0];\n }\n workgroupBarrier();\n\n for (var col = tid; col < cols; col += blockSize) {\n let value = exp(getLogits(row, col) - rowMaxShared) / rowSumShared;\n setOutputAtCoords(row, col, value);\n }\n }\n `}};function l0(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=pe({inputs:{x:n},backend:t,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new Zx(a.shape),p=t.runWebGPUProgram(i,[a],n.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n.shape}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var HW={kernelName:Is,backendName:\"webgpu\",kernelFunc:l0};function Bpe(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:l0({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new Qx(u,s),m=[{type:\"float32\",data:[a]},{type:\"int32\",data:[c]}],d=t.runWebGPUProgram(l,[p],\"int32\",m);return i||t.disposeData(p.dataId),d}var KW={kernelName:jn,backendName:\"webgpu\",kernelFunc:Bpe};function zpe(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=Dz(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Jr(o.shape,Z.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var qW={kernelName:\"Neg\",backendName:\"webgpu\",kernelFunc:zpe};function Vpe(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Vt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],\"int32\",new Int32Array(l))}var jW={kernelName:Qn,backendName:\"webgpu\",kernelFunc:Vpe};function Wpe(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Vt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),t.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var XW={kernelName:Zn,backendName:\"webgpu\",kernelFunc:Wpe};var Jx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"onValue : f32, offValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"onehot\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,\n f32(i32(round(getX(coords.x))) == coords.y)));\n }\n }\n `}};function Upe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Jx(u,a),l=pe({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=pe({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var YW={kernelName:Jn,backendName:\"webgpu\",kernelFunc:Upe};function bm(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"complex64\"){let n=vi({inputs:{input:o},backend:t}),s=bm({inputs:{x:n},backend:t}),a=Rp({inputs:{input:o},backend:t}),i=bm({inputs:{x:a},backend:t}),p=xo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return vt({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:t})}var QW={kernelName:Sa,backendName:\"webgpu\",kernelFunc:bm};function ZW(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(o.dtype===\"complex64\"){let n=vi({inputs:{input:o},backend:t}),s=ZW({inputs:{x:n},backend:t}),a=Rp({inputs:{input:o},backend:t}),i=bm({inputs:{x:a},backend:t}),p=xo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return vt({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var JW={kernelName:ca,backendName:\"webgpu\",kernelFunc:ZW};function Gpe(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Mx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=Mx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=a0({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var eU={kernelName:la,backendName:\"webgpu\",kernelFunc:Gpe};function m0(r,e=!1){let t=r.length,o=ft(t),n=r.map((l,m)=>`uniforms.pad${m}[0]`).join(\",\"),s=r.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${t>1?`[${m}]`:\"\"}`).join(\",\"),a=t>1?`${o}(${n})`:`${n}`,i=t>1?`${o}(${s})`:`${s}`,p=t>1?\"any(paddedCoords < start)\":\"paddedCoords < start\",u=t>1?\"any(paddedCoords >= end)\":\"paddedCoords >= end\",c=t>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,t):\"coords\";return`\n let start = ${a};\n let end = ${i};\n if (${p} || ${u}) {\n setOutputAtIndex(index, ${e?0:\"uniforms.constantValue\"});\n } else {\n let coords = paddedCoords - start;\n setOutputAtIndex(index, getX(${c}));\n }\n `}var ey=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let paddedCoords = getCoordsFromIndex(index);\n ${m0(this.xShape)}\n }\n }\n `}};var Hpe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return At({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return vt({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:\"float32\",data:[a]}];s.map(u=>i.push({type:\"int32\",data:[u[0],u[1]]}));let p=new ey(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},tU={kernelName:es,backendName:\"webgpu\",kernelFunc:Hpe};var Kpe=et({opType:fe.POW}),rU={kernelName:\"Pow\",backendName:\"webgpu\",kernelFunc:Kpe};function qpe(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Ii(fe.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],\"float32\")}var oU={kernelName:rs,backendName:\"webgpu\",kernelFunc:qpe};function jpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,\"prod\",t)}var nU={kernelName:os,backendName:\"webgpu\",kernelFunc:jpe};var Xpe=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=Pz(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},sU={kernelName:ma,backendName:\"webgpu\",kernelFunc:Xpe};var Ype=et({opType:fe.DIV}),aU={kernelName:fn,backendName:\"webgpu\",kernelFunc:Ype};var Qpe=ye({opType:Z.RECIPROCAL}),iU={kernelName:ns,backendName:\"webgpu\",kernelFunc:Qpe};var Zpe=ye({opType:Z.RELU}),uU={kernelName:ss,backendName:\"webgpu\",kernelFunc:Zpe};var Jpe=ye({opType:Z.RELU6}),pU={kernelName:us,backendName:\"webgpu\",kernelFunc:Jpe};var ty=class{constructor(e,t,o){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, halfPixelCenters : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC =\n (vec2(rc) + vec2(uniforms.halfPixelCenters)) *\n effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters);\n\n // Compute the four integer indices.\n let sourceFloorRC = vec2(sourceFracIndexRC);\n let sourceCeilRC = vec2(\n min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC)));\n\n let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n let fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n let top = topLeft + (topRight - topLeft) * fracRC.y;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n let newValue = top + (bottom - top) * fracRC.x;\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function ece(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[i?.5:0]}],f=new ty(n.shape,p,u);return t.runWebGPUProgram(f,[n],\"float32\",d)}var cU={kernelName:is,backendName:\"webgpu\",kernelFunc:ece};var ry=class{constructor(e,t){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32,\n invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeBilinearBackprop_${t}`}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(startRLerp - f32(uniforms.winHeight / 2));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(startCLerp - f32(uniforms.winWidth / 2));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let dxR = f32(dyR) * uniforms.heightScale;\n let topDxRIndex = i32(floor(dxR));\n let bottomDxRIndex = i32(min(ceil(dxR), f32(uniforms.outShape[1] - 1)));\n let dxRLerp = dxR - f32(topDxRIndex);\n let inverseDxRLerp = 1.0 - dxRLerp;\n\n let dxC = f32(dyC) * uniforms.widthScale;\n let leftDxCIndex = i32(floor(dxC));\n let rightDxCIndex = i32(min(ceil(dxC), f32(uniforms.outShape[2] - 1)));\n let dxCLerp = dxC - f32(leftDxCIndex);\n let inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function tce(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ry(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[d]},{type:\"float32\",data:[f]},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return t.runWebGPUProgram(C,[s],s.dtype,S)}var lU={kernelName:Ja,backendName:\"webgpu\",kernelFunc:tce};var oy=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, roundBase : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":e=\"vec2(rc) * effectiveInputOverOutputRatioRC\",`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC = ${e};\n\n // Compute the coordinators of nearest neighbor point.\n let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n let sourceNearestRC = vec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function rce(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[s?.5:0]}],f=new oy(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var mU={kernelName:as,backendName:\"webgpu\",kernelFunc:rce};var ny=class{constructor(e,t){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32,\n winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeNearestNeigborBackprop_${t}`}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(floor(startRLerp - f32(uniforms.winHeight / 2)));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(floor(startCLerp - f32(uniforms.winWidth / 2)));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let sourceFracRow = f32(uniforms.effectiveXSize[0]) *\n (f32(dyR) / f32(uniforms.effectiveYSize[0]));\n\n let sourceFracCol = f32(uniforms.effectiveXSize[1]) *\n (f32(dyC) / f32(uniforms.effectiveYSize[1]));\n\n let sourceNearestRow =\n i32(min(f32(uniforms.outShape[1] - 1),\n ${this.alignCorners?\"floor(sourceFracRow + 0.5)\":\"floor(sourceFracRow)\"}));\n\n let sourceNearestCol =\n i32(min(f32(uniforms.outShape[2] - 1),\n ${this.alignCorners?\"floor(sourceFracCol + 0.5)\":\"floor(sourceFracCol)\"}));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function oce(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ny(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return t.runWebGPUProgram(C,[s],s.dtype,S)}var dU={kernelName:Za,backendName:\"webgpu\",kernelFunc:oce};var sy=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\" axis : vec4,\",this.shaderKey=\"reverse\"}getUserCode(){return`\n \n // Using uniform variables as judging conditions, so the function has\n // coherent execution within all threads.\n fn getReverseCoords(coords : vec4) -> vec4 {\n var reverseCoords = coords;\n if (uniforms.axis[0] == 1) {\n reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;\n }\n if (uniforms.axis[1] == 1) {\n reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;\n }\n if (uniforms.axis[2] == 1) {\n reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;\n }\n if (uniforms.axis[3] == 1) {\n reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;\n }\n\n return reverseCoords;\n }\n \n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let reverseCoords = getReverseCoords(coords);\n setOutputAtIndex(index, getX(reverseCoords[0],\n reverseCoords[1], reverseCoords[2], reverseCoords[3]));\n }\n }\n `}};function nce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return At({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:\"int32\",data:c}],m=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new sy(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=pe({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var fU={kernelName:ps,backendName:\"webgpu\",kernelFunc:nce};var ay=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n uniforms.sinRadians;\n let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n uniforms.cosRadians;\n let coordX = i32(round(coordXFloat + uniforms.centerX));\n let coordY = i32(round(coordYFloat + uniforms.centerY));\n ${this.fillSnippet}\n if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n coordY < uniforms.xShape[1]) {\n outputValue = getX(coords[0], coordY, coordX, coords[3]);\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var hU={kernelName:Ds,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new ay(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:\"float32\",data:[u]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(n)]},{type:\"float32\",data:[Math.cos(n)]}];return typeof s==\"number\"?l.push({type:\"float32\",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:\"float32\",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var sce=ye({opType:Z.ROUND}),gU={kernelName:cs,backendName:\"webgpu\",kernelFunc:sce};var ace=ye({opType:Z.RSQRT,cpuKernelImpl:Oz}),xU={kernelName:ls,backendName:\"webgpu\",kernelFunc:ace};var za=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=[\"updates\",\"indices\"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}_${s.length}`;let u=ft(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",n=\"\",s=\"\";this.dispatchLayout.x.length===1?(n=\"flattenedIndex\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n return index;\n }\n `):this.dispatchLayout.x.length===2&&(n=\"vec2(flattenedIndex, coords[1])\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 {\n // N.B. |updates| could be a scalar tensor, conceptually representing a\n // 2D tensor with all values equal to that. By design, its size must be\n // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n // gives the other.\n let sliceSize = uniforms.outShape[1];\n let d0 = index / sliceSize;\n let d1 = index - d0 * sliceSize;\n return vec2(d0, d1);\n }\n `);let i=`getUpdates(${Array.from({length:this.updatesRank},(u,c)=>`coords[${c}]`).join(\", \")})`;return`\n ${s}\n ${G(\"index\")} {\n if (index < uniforms.updatesSize) {\n let coords = getUpdatesCoordsFromFlatIndex(index);\n var flattenedIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexInside = i32(round(${t}));\n flattenedIndex = flattenedIndex + indexInside * ${o};\n }\n let updateValue =\n ${Su(this.type)}(${i});\n let flatIndex = getOutputIndexFromCoords(${n});\n\n ${this.sumDupeIndices?Qr(\"&result[flatIndex]\",\"updateValue\",this.type):\"atomicStore(&result[flatIndex], bitcast(updateValue));\"}\n }\n }`}};function ice(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=pe({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=vt({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[x]}],C=new za(f.shape,i,d.shape.length,f.shape.length,c,m,h),S=t.runWebGPUProgram(C,[f,d],h,b,g),k=pe({inputs:{x:S},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(S.dataId),k}var yU={kernelName:ms,backendName:\"webgpu\",kernelFunc:ice};var iy=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"sortedSequence\",\"values\"],this.uniforms=\"numInputs : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return`\n fn findBound(batch: i32, value: f32) -> i32 {\n var left = i32(0);\n var right = uniforms.numInputs;\n while (left < right) {\n var mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${this.side===\"left\"?\"<\":\"<=\"} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let value = getValuesByOutputIndex(index);\n setOutputAtIndexI32(index, findBound(coords[0], value));\n }\n }\n `}};function uce(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new iy([s.shape[0],s.shape[1]],a),p=[{type:\"int32\",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],\"int32\",p)}var bU={kernelName:fs,backendName:\"webgpu\",kernelFunc:uce};var uy=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=o,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[],a=[];for(let i=0;i= 1.0) {\n setOutputAtIndex(index, getA(${t}));\n } else {\n setOutputAtIndex(index, getB(${t}));\n }\n }\n }\n `}};function pce(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new uy(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var CU={kernelName:fa,backendName:\"webgpu\",kernelFunc:pce};var cce=ye({opType:Z.SELU}),wU={kernelName:hs,backendName:\"webgpu\",kernelFunc:cce};var lce=ye({opType:Z.SIGMOID}),SU={kernelName:bs,backendName:\"webgpu\",kernelFunc:lce};var mce=ye({opType:Z.SIGN}),IU={kernelName:ys,backendName:\"webgpu\",kernelFunc:mce};var dce=ye({opType:Z.SIN}),vU={kernelName:\"Sin\",backendName:\"webgpu\",kernelFunc:dce};var fce=ye({opType:Z.SINH}),kU={kernelName:xs,backendName:\"webgpu\",kernelFunc:fce};var hce=ye({opType:Z.SOFTPLUS}),NU={kernelName:Cs,backendName:\"webgpu\",kernelFunc:hce};var py=class{constructor(e,t,o,n,s,a){this.variableNames=[\"x\"],this.outputShape=[],this.uniforms=\"\",this.workgroupSize=[64,1,1],this.size=!0;let i=new Array(n.length);for(let p=0;p{this.uniforms+=` pad${u} : vec2,`}),this.shaderKey=`spaceToBatchND_${s}`}getUserCode(){let e=ft(this.outputShape.length),t=e0(this.newDim);return`\n ${cm(this.paddedXShape,\"PaddedX\")}\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${e}(${t}), uniforms.reshapedPaddedXShape);\n let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex);\n ${m0(this.xShape,!0)}\n }\n }\n `}};var gce=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=[[0,0]];p.push(...a);for(let b=1+s.length;bb[0]+n.shape[C]+b[1]),c=w.getReshaped(u,s,i,!1),l=w.getPermuted(c.length,s.length,!1),m=w.getReshapedPermuted(u,s,i,!1),d=y.computeStrides(u),f=new py(n.shape,u,p,c,l,d.length),h=[{type:\"int32\",data:c},{type:\"int32\",data:d}];p.map(b=>h.push({type:\"int32\",data:[b[0],b[1]]}));let g=t.runWebGPUProgram(f,[n],n.dtype,h),x=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeData(g.dataId),x},TU={kernelName:ga,backendName:\"webgpu\",kernelFunc:gce};var cy=class{constructor(e,t,o){this.variableNames=[\"input\",\"indices\",\"segmentIds\"],this.outputShape=[],this.uniforms=\"segmentSize : i32, sparseSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e,this.type=o,this.dispatchLayout=X([t]),this.dispatch=H(this.dispatchLayout,[t],this.workgroupSize),this.shaderKey=\"sparseSegmentSum\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.sparseSize) {\n let indexInSegmentIds = index / uniforms.segmentSize;\n let indexInSegment = index % uniforms.segmentSize;\n let indexInInput = indices[indexInSegmentIds];\n let segmentId = segmentIds[indexInSegmentIds];\n\n let value = input[indexInInput * uniforms.segmentSize + indexInSegment];\n let outIndex = segmentId * uniforms.segmentSize + indexInSegment;\n ${Qr(\"&result[outIndex]\",\"value\",this.type)}\n }\n }\n `}},ly=class{constructor(e,t){this.variableNames=[\"segmentIds\"],this.outputShape=[],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=[e],this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.shaderKey=\"sparseSegmentIdCountProgram\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.segmentIdsShape) {\n let segmentId = segmentIds[index];\n ${Qr(\"&result[segmentId]\",\"1\",\"int32\")}\n }\n }\n `}},my=class{constructor(e,t){this.variableNames=[\"segmentSum\",\"sameSegmentIdCount\"],this.outputShape=[],this.uniforms=\"segmentSize : i32\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.type=t,this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),this.shaderKey=\"sparseSegmentMean\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let segmentId = index / uniforms.segmentSize;\n let count = sameSegmentIdCount[segmentId];\n if (count != 0) {\n ${this.type===\"float32\"?\"setOutputAtIndex(index, segmentSum[index] / f32(count));\":\"setOutputAtIndexI32(index, segmentSum[index] / count);\"}\n }\n }\n }\n `}};function dy(r,e,t,o=!1,n){let a=y.sizeFromShape(r.shape)/r.shape[0],i=r.dtype,p=y.sizeFromShape(e.shape),u=n.readSync(t.dataId),l=p>0?u[p-1]+1:0,m,d=r.shape.slice();d[0]=l;let f=p*a,h=vt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new cy(d,f,i);let g=[{type:\"int32\",data:[a]},{type:\"int32\",data:[f]}],x=n.runWebGPUProgram(m,[r,e,t],i,g,h);if(o)return x;let b=vt({backend:n,attrs:{shape:[l],value:0,dtype:\"int32\"}});m=new ly(l,t.shape);let C=n.runWebGPUProgram(m,[t],\"int32\",null,b),S=vt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new my(d,i),g=[{type:\"int32\",data:[a]}];let k=n.runWebGPUProgram(m,[x,C],i,g,S);return n.disposeData(x.dataId),n.disposeData(C.dataId),k}function xce(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;return dy(o,n,s,!1,t)}var _U={kernelName:ya,backendName:\"webgpu\",kernelFunc:xce};function yce(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;return dy(o,n,s,!0,t)}var EU={kernelName:ba,backendName:\"webgpu\",kernelFunc:yce};var fy=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=Uz(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new fy(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var $U={kernelName:po,backendName:\"webgpu\",kernelFunc:Cm};function Cce(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let R=t.bufferSync(n),D=t.bufferSync(s),P=y.decodeString(t.readSync(a.dataId)[0]),O=Mz(R,D,i,m,c,u,p,l,P,d);return t.makeTensorInfo(i,O.dtype,O.values)}let f=[m/c,c],h=pe({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?pe({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):At({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=pe({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),S=Cm({inputs:{x:C},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:\"int32\",data:[p]},{type:\"int32\",data:l},{type:\"int32\",data:[k]}];switch(u){case 0:break;case 1:{let R=new za([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(R,[g,h],x,_,S)}break;default:{let R=new za([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(R,[b,h],x,_,S)}{let R=new za([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(R,[g,h],x,_,S)}}let $=pe({inputs:{x:S},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(C.dataId),t.disposeData(b.dataId),t.disposeData(S.dataId),$}var RU={kernelName:vs,backendName:\"webgpu\",kernelFunc:Cce};function wce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Hs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var DU={kernelName:xa,backendName:\"webgpu\",kernelFunc:wce};var Sce=ye({opType:Z.SQRT}),AU={kernelName:ws,backendName:\"webgpu\",kernelFunc:Sce};var FU={kernelName:qi,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Jr(t.shape,Z.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var Ice=et({opType:fe.SQUARED_DIFFERENCE}),PU={kernelName:ks,backendName:\"webgpu\",kernelFunc:Ice};function vce({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=new Jr(o.shape,Z.STEP,\"stepAlpha : f32,\"),s=[{type:\"float32\",data:[e.alpha]}];return t.runWebGPUProgram(n,[o],o.dtype,s)}var OU={kernelName:wo,backendName:\"webgpu\",kernelFunc:vce};var hy=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=ft(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, getX(${t}));\n }\n }\n `}};function kce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=pt.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=pe({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=pt.computeOutShape(b,C,S),$=Hs({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=pe({inputs:{x:$},backend:t,attrs:{shape:f}}),t.disposeData($.dataId)}else if(t.shouldExecuteOnCPU([n])){let $=t.readSync(n.dataId),R=me(n.shape,n.dtype,$),D=zz(d,R,S,b);k=t.makeTensorInfo(f,n.dtype,D.values)}else{let $=new hy(d),R=[{type:\"int32\",data:b},{type:\"int32\",data:S}],D=t.runWebGPUProgram($,[n],n.dtype,R);k=pe({inputs:{x:D},backend:t,attrs:{shape:f}}),t.disposeData(D.dataId)}return k}var MU={kernelName:Ns,backendName:\"webgpu\",kernelFunc:kce};function Nce(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=Vz(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var LU={kernelName:Ca,backendName:\"webgpu\",kernelFunc:Nce};var Tce=et({opType:fe.SUB,cpuKernelImpl:Wz,supportsComplex:!0}),BU={kernelName:\"Sub\",backendName:\"webgpu\",kernelFunc:Tce};var _ce=ye({opType:Z.TAN}),zU={kernelName:\"Tan\",backendName:\"webgpu\",kernelFunc:_ce};var Ece=ye({opType:Z.TANH}),VU={kernelName:Es,backendName:\"webgpu\",kernelFunc:Ece};function $ce(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return t.makeTensorInfo(n.shape,s.dtype);let d=[],f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,i]}});d.push(f);let h=pe({inputs:{x:a},backend:t,attrs:{shape:[p,u]}});d.push(h);let g=pe({inputs:{x:n},backend:t,attrs:{shape:m}});d.push(g);let x=Cm({inputs:{x:g},backend:t,attrs:{reps:Array(m.length).fill(1)}}),b=new za([p,u],i,f.shape.length,h.shape.length,c,m,n.dtype,!1),C=y.sizeFromShape([p,u]),S=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[C]}],k=t.runWebGPUProgram(b,[h,f],g.dtype,S,x);d.push(k);let _=pe({inputs:{x:k},backend:t,attrs:{shape:n.shape}});return d.forEach($=>t.disposeData($.dataId)),_}var WU={kernelName:ds,backendName:\"webgpu\",kernelFunc:$ce};var gy=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced\n // above, Figure5(a) shows that element[1] is in the second half of\n // the group when group size is 2, but it is in the first half of\n // the group when group size is 4.\n let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n var i = 0;\n if (isFirstInPair) {\n i = elemIdx;\n } else {\n i = elemIdx - uniforms.inc;\n }\n\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.inc;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.inc));\n }\n\n var x0 = f32(0.0);\n var x1 = f32(0.0);\n if (i0 < uniforms.inputSize) {\n x0 = getX(batch, i0);\n } else {\n x0 = uniforms.negativeInf;\n }\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = uniforms.negativeInf;\n }\n\n let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) {\n // Elements in opposite order of direction\n let iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}},xy=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _\n // (k=4), we only need to output the indices at positions |, the\n // indices at positions _ can be thrown away, see Figure5(b) After\n // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n // above.\n // For example, the paper shows we only need to output the orange\n // bars. The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back to\n // the previous sequence to find the corresponding value, we need\n // to double the index. When we double the index, we basically\n // interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n // position of each 2k positions by - elemIdx % k. E.g. for output\n // at index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n var i = 0;\n if (elemIdx < uniforms.k) {\n i = elemIdx;\n } else {\n i = elemIdx * 2 - elemIdx % uniforms.k;\n }\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.k;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.k));\n }\n\n let x0 = getX(batch, i0);\n var x1 = f32(0.0);\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = x0;\n }\n\n if (x0 >= x1) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}};function rl(r,e){e!==null&&r.disposeData(e.dataId)}function UU(r){let e=1;for(;ef===null?[l,l]:[l,f],g=(k,_,$)=>{let R=h(),D=new gy($),O=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[_]}],M=f;f=t.runWebGPUProgram(D,R,\"int32\",O),rl(t,M)};for(let k=1;k=1;$/=2)g(_,$,[c,d])}for(let k=d;k>m;k/=2){let _=h(),$=new xy([c,k/2]),D=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[m]}],P=f;f=t.runWebGPUProgram($,_,\"int32\",D),rl(t,P);let O=m/2,M=O*2;for(let L=O;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=Hs({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),rl(t,x);let b=c0({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});rl(t,l);let C=i.slice(0,-1);C.push(s),x=f,f=pe({inputs:{x:f},attrs:{shape:C},backend:t}),rl(t,x);let S=b;return b=pe({inputs:{x:b},attrs:{shape:C},backend:t}),rl(t,S),[b,f]}var GU={kernelName:$s,backendName:\"webgpu\",kernelFunc:Rce};var yy=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n fn mapCoord(outCoord : f32, len : f32) -> f32{\n var inCoord = outCoord;\n if(uniforms.fillModeId == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n inCoord;\n }\n if (inCoord < -len) {\n inCoord = inCoord + sz2;\n } else {\n inCoord = -inCoord - 1.0;\n }\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n }\n return outCoord;\n }\n fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n channel : i32) -> f32 {\n var outputValue : f32;\n if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = uniforms.fillValue;\n }\n return outputValue;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var outputValue : f32;\n let batch = coords[0];\n let x = coords[2];\n let y = coords[1];\n let channel = coords[3];\n let xf = f32(x);\n let yf = f32(y);\n let a1 = getTransforms(batch, 0);\n let a2 = getTransforms(batch, 1);\n let a3 = getTransforms(batch, 2);\n let b1 = getTransforms(batch, 3);\n let b2 = getTransforms(batch, 4);\n let b3 = getTransforms(batch, 5);\n let c1 = getTransforms(batch, 6);\n let c2 = getTransforms(batch, 7);\n let projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = uniforms.fillValue;\n } else {\n let inX = (a1 * xf + a2 * yf + a3) / projection;\n let inY = (b1 * xf + b2 * yf + b3) / projection;\n let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n if (uniforms.interpolationModeId == 1) {\n let coordY = i32(round(mapY));\n let coordX = i32(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n let yFloor = floor(mapY);\n let xFloor = floor(mapX);\n let yCeil = yFloor + 1.0;\n let xCeil = xFloor + 1.0;\n let valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n let valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};function Dce(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new yy(g),b=a===\"nearest\"?1:2,C;switch(i){case\"constant\":C=1;break;case\"reflect\":C=2;break;case\"wrap\":C=3;break;case\"nearest\":C=4;break;default:C=1;break}let S=[{type:\"int32\",data:[b]},{type:\"int32\",data:[C]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(x,[n,s],\"float32\",S)}var HU={kernelName:Rs,backendName:\"webgpu\",kernelFunc:Dce};function Ace(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var KU={kernelName:wa,backendName:\"webgpu\",kernelFunc:Ace};var by=class{constructor(e,t,o){if(this.outputShape=[],this.variableNames=[\"x\",\"segmentIds\"],this.uniforms=\"numSegments : i32, xSize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t,this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`UnsortedSegmentSum only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"unsortedSegmentSum\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.xSize) {\n let coords = getXCoordsFromIndex(index);\n let b = coords[0];\n let inCol = coords[1];\n\n let segmentId = i32(getSegmentIds(inCol));\n if (segmentId >= 0) {\n let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments;\n let value = getX(b, inCol);\n\n ${Qr(\"&result[flatIndex]\",\"value\",this.type)}\n }\n }\n }\n `}};function Fce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=xr({inputs:{x:n},backend:t,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=pe({inputs:{x:l},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=n.dtype,g=[f.shape[0],a],x=vt({backend:t,attrs:{shape:g,value:0,dtype:h}}),b=new by(f.shape,g,h),C=[{type:\"int32\",data:[a]},{type:\"int32\",data:[y.sizeFromShape(f.shape)]}],S=t.runWebGPUProgram(b,[f,s],h,C,x),k=pe({inputs:{x:S},backend:t,attrs:{shape:m}});p.push(S);let _=k;if(c!=null){p.push(k);let $=w.getUndoAxesPermutation(c);_=xr({inputs:{x:_},backend:t,attrs:{perm:$}})}return p.forEach($=>t.disposeData($.dataId)),_}var qU={kernelName:Qi,backendName:\"webgpu\",kernelFunc:Fce};var Pce=[pz,Kz,qz,jz,Xz,Yz,Zz,Jz,eV,tV,rV,oV,nV,sV,aV,pV,cV,lV,mV,dV,hV,gV,xV,wV,SV,IV,lz,kV,TV,_V,EV,$V,RV,DV,AV,FV,PV,OV,BV,zV,VV,WV,GV,HV,UV,KV,qV,jV,XV,YV,JV,eW,tW,rW,oW,nW,sW,aW,iW,iz,uW,lW,pW,cW,mW,dW,fW,hW,gW,xW,yW,cz,bW,NV,CW,wW,SW,IW,vW,kW,NW,_W,TW,EW,$W,RW,AW,FW,iV,PW,OW,BW,MW,LW,zW,uV,VW,WW,UW,GW,KW,QV,qW,jW,XW,yV,YW,JW,eU,tU,rU,oU,nU,sU,bV,aU,iU,uU,pU,uz,cU,lU,mU,dU,fU,hU,gU,xU,yU,bU,CU,wU,SU,IU,vU,kU,fV,OU,MU,LU,HW,NU,TU,_U,EU,RU,DU,AU,FU,PU,BU,ZV,zU,VU,WU,$U,GU,HU,Qz,KU,qU,QW];for(let r of Pce)ti(r);var jU=\"4.22.0\",Oce=\"4.22.0\",Mce=\"4.22.0\",Lce=\"4.22.0\",Bce=\"4.22.0\",zce=\"4.22.0\",Vce={tfjs:jU,\"tfjs-core\":jU,\"tfjs-converter\":Oce,\"tfjs-backend-cpu\":Mce,\"tfjs-backend-webgl\":Lce,\"tfjs-backend-wasm\":Bce,\"tfjs-backend-webgpu\":zce};var EQt=void 0;export{Xs as Abs,Vo as Acos,Wo as Acosh,Ju as AdadeltaOptimizer,ep as AdagradOptimizer,tp as AdamOptimizer,rp as AdamaxOptimizer,uo as Add,Uo as AddN,Go as All,Ho as Any,Ys as ArgMax,Qs as ArgMin,Ko as Asin,qo as Asinh,jo as Atan,Yo as Atan2,Xo as Atanh,Qo as AvgPool,Zs as AvgPool3D,Ri as AvgPool3DGrad,$i as AvgPoolGrad,pm as BackendWasm,Zo as BatchMatMul,Js as BatchToSpaceND,Jo as Bincount,qa as BitwiseAnd,ea as BroadcastArgs,qce as BroadcastTo,yo as Cast,en as Ceil,bo as ClipByValue,Di as Complex,Ai as ComplexAbs,ta as Concat,tn as Conv2D,Fi as Conv2DBackpropFilter,rn as Conv2DBackpropInput,on as Conv3D,ja as Conv3DBackpropFilterV2,nn as Conv3DBackpropInputV2,sn as Cos,an as Cosh,cn as CropAndResize,un as Cumprod,pn as Cumsum,Bo as DataStorage,ra as DenseBincount,ln as DepthToSpace,mn as DepthwiseConv2dNative,Pi as DepthwiseConv2dNativeBackpropFilter,Oi as DepthwiseConv2dNativeBackpropInput,oa as Diag,dn as Dilation2D,Li as Dilation2DBackpropFilter,Mi as Dilation2DBackpropInput,$u as Draw,nw as ENV,Bi as Einsum,hn as Elu,Xa as EluGrad,dl as Environment,xn as Equal,gn as Erf,yn as Exp,na as ExpandDims,bn as Expm1,zi as FFT,sa as Fill,Cn as FlipLeftRight,wn as Floor,Sn as FloorDiv,Du as FromPixels,In as FusedBatchNorm,Io as FusedConv2D,vo as FusedDepthwiseConv2D,bp as GPGPUContext,vn as GatherNd,aa as GatherV2,Bl as GraphModel,kn as Greater,Nn as GreaterEqual,Vi as IFFT,Co as Identity,Wi as Imag,Tn as IsFinite,_n as IsInf,En as IsNan,ao as KernelBackend,Bn as LRN,Ya as LRNGrad,$n as LeakyRelu,Rn as Less,Dn as LessEqual,An as LinSpace,Fn as Log,Pn as Log1p,jce as LogSoftmax,On as LogicalAnd,Mn as LogicalNot,Ln as LogicalOr,R0 as LogicalXor,Xce as LowerBound,xc as MathBackendCPU,Lc as MathBackendWebGL,Yce as MatrixBandPart,zn as Max,Wn as MaxPool,ia as MaxPool3D,Gi as MaxPool3DGrad,Ui as MaxPoolGrad,ua as MaxPoolWithArgmax,Vn as Maximum,Un as Mean,Gn as Min,Hn as Minimum,Kn as MirrorPad,qn as Mod,op as MomentumOptimizer,jn as Multinomial,Xn as Multiply,pa as Neg,Qn as NonMaxSuppressionV3,Qa as NonMaxSuppressionV4,Zn as NonMaxSuppressionV5,Yn as NotEqual,Nw as OP_SCOPE_SUFFIX,Jn as OneHot,ca as OnesLike,kr as Optimizer,Fl as OptimizerConstructors,la as Pack,es as PadV2,Qce as Pool,ts as Pow,rs as Prelu,os as Prod,np as RMSPropOptimizer,Hp as RaggedGather,Kp as RaggedRange,qp as RaggedTensorToTensor,ma as Range,gw as Rank,Hi as Real,fn as RealDiv,ns as Reciprocal,$t as Reduction,ss as Relu,us as Relu6,da as Reshape,is as ResizeBilinear,Ja as ResizeBilinearGrad,as as ResizeNearestNeighbor,Za as ResizeNearestNeighborGrad,ps as Reverse,Ds as RotateWithOffset,cs as Round,ls as Rsqrt,mi as SGDOptimizer,ms as ScatterNd,fs as SearchSorted,fa as Select,hs as Selu,bs as Sigmoid,ys as Sign,gs as Sin,xs as Sinh,ha as Slice,Is as Softmax,Cs as Softplus,ga as SpaceToBatchND,Ki as SparseFillEmptyRows,ei as SparseReshape,ya as SparseSegmentMean,ba as SparseSegmentSum,vs as SparseToDense,xa as SplitV,ws as Sqrt,qi as Square,ks as SquaredDifference,Ru as StaticRegexReplace,wo as Step,Ns as StridedSlice,Ca as StringNGrams,ji as StringSplit,Xi as StringToHashBucketFast,Ts as Sub,Ss as Sum,_s as Tan,Es as Tanh,mt as Tensor,tt as TensorBuffer,ds as TensorScatterUpdate,po as Tile,$s as TopK,Rs as Transform,co as Transpose,Yi as Unique,wa as Unpack,Qi as UnsortedSegmentSum,Zce as UpperBound,ri as Variable,jc as WebGPUBackend,Sa as ZerosLike,So as _FusedMatMul,Qt as abs,Rk as acos,Dk as acosh,Ce as add,Ak as addN,Fk as all,Pk as any,Ok as argMax,Mk as argMin,Lk as asin,Bk as asinh,zk as atan,Vk as atan2,Wk as atanh,dd as avgPool,Hk as avgPool3d,ak as backend,w as backend_util,Kk as basicLSTMCell,nu as batchNorm,jk as batchNorm2d,Xk as batchNorm3d,Yk as batchNorm4d,fd as batchToSpaceND,hd as bincount,Qk as bitwiseAnd,L6 as booleanMaskAsync,Zk as broadcastArgs,su as broadcastTo,Sr as broadcast_util,cT as browser,me as buffer,Ue as cast,Jk as ceil,e2 as clipByValue,Ur as clone,Er as complex,yt as concat,t2 as concat1d,r2 as concat2d,o2 as concat3d,n2 as concat4d,s2 as conv1d,au as conv2d,a2 as conv2dTranspose,i2 as conv3d,p2 as conv3dTranspose,ale as copyRegisteredKernels,c2 as cos,l2 as cosh,$l as cosineWindow,m2 as cumprod,d2 as cumsum,Ir as customGrad,f2 as denseBincount,Tw as deprecationWarn,h2 as depthToSpace,sc as depthwiseConv2d,V5 as deregisterOp,eu as device_util,g2 as diag,x2 as dilation2d,xme as disableDeprecationWarnings,Ot as dispose,yme as disposeVariables,je as div,b2 as divNoNan,C2 as dot,Y6 as dropout,iu as einsum,bd as elu,gme as enableDebugMode,hme as enableProdMode,Zw as enclosingPowerOfTwo,ur as engine,w2 as ensureShape,A as env,yd as equal,S2 as erf,k2 as euclideanNorm,_o as exp,Ms as expandDims,N2 as expm1,Cd as eye,uc as fft,$a as fill,kme as findBackend,Nme as findBackendFactory,wd as floor,md as floorDiv,GD as forceHalfFloat,Jw as fused,Sd as gather,j6 as gatherND,af as gather_util,sk as getBackend,iw as getGradient,Xp as getKernel,Ym as getKernelsForBackend,aae as getThreadsCount,mv as gpgpu_util,VK as grad,WK as grads,Wu as greater,Id as greaterEqual,ju as ifft,pu as imag,eX as image,Z6 as inTopKAsync,di as io,Hd as irfft,T2 as isFinite,_2 as isInf,E2 as isNaN,$r as keep,Vt as kernel_impls,vd as leakyRelu,Tl as less,ac as lessEqual,tX as linalg,$2 as linspace,M8 as loadGraphModel,L8 as loadGraphModelSync,R2 as localResponseNormalization,pi as log,kd as log1p,D2 as logSigmoid,A2 as logSoftmax,_d as logSumExp,Uu as logicalAnd,Ed as logicalNot,$d as logicalOr,F2 as logicalXor,rX as losses,P2 as lowerBound,Ze as matMul,aT as math,Ra as max,Dd as maxPool,O2 as maxPool3d,M2 as maxPoolWithArgmax,Ad as maximum,Gu as mean,bme as memory,L2 as meshgrid,Nl as min,Hu as minimum,B2 as mirrorPad,z2 as mod,V2 as moments,V6 as movingAverage,se as mul,W2 as multiRNNCell,U2 as multinomial,pr as neg,cS as nextFrame,EQt as node,Vu as norm,Fd as notEqual,El as oneHot,Da as ones,G2 as onesLike,N as op,H2 as outerProduct,Aa as pad,K2 as pad1d,q2 as pad2d,j2 as pad3d,X2 as pad4d,Y2 as pool,ui as pow,Od as prelu,ld as print,Q2 as prod,Cme as profile,Z2 as raggedGather,J2 as raggedRange,e1 as raggedTensorToTensor,t1 as rand,S1 as randomGamma,Wd as randomNormal,I1 as randomStandardNormal,ic as randomUniform,v1 as randomUniformInt,cu as range,Ime as ready,ci as real,k1 as reciprocal,tu as registerBackend,ole as registerGradient,ti as registerKernel,z5 as registerOp,lu as relu,Ud as relu6,vme as removeBackend,W as reshape,mo as reverse,N1 as reverse1d,T1 as reverse2d,_1 as reverse3d,E1 as reverse4d,pc as rfft,Gd as round,$1 as rsqrt,ke as scalar,U6 as scatterND,du as scatter_util,_l as searchSorted,R1 as selu,D1 as separableConv2d,jN as serialization,Sme as setBackend,Tme as setPlatform,sae as setThreadsCount,oae as setWasmPath,nae as setWasmPaths,NI as setWebGLContext,A1 as setdiff1dAsync,Ic as shared,Ea as sigmoid,F1 as sign,Jj as signal,P1 as sin,O1 as sinh,Xe as slice,M1 as slice1d,L1 as slice2d,B1 as slice3d,z1 as slice4d,pt as slice_util,V1 as softmax,Td as softplus,Pd as spaceToBatchND,oX as sparse,K6 as sparseToDense,Zj as spectral,li as split,Rr as sqrt,Zt as square,Kd as squaredDifference,cc as squeeze,vr as stack,qd as step,W1 as stridedSlice,nX as string,Te as sub,ot as sum,oi as sumOutType,U1 as tan,kl as tanh,ar as tensor,Jt as tensor1d,mu as tensor2d,jd as tensor3d,G1 as tensor4d,H1 as tensor5d,K1 as tensor6d,j1 as tensorScatterUpdate,rk as tensor_util,w1 as test_util,De as tidy,uu as tile,wme as time,X1 as topk,OGe as train,mc as transpose,Y1 as truncatedNormal,Q1 as unique,sle as unregisterGradient,nle as unregisterKernel,Z1 as unsortedSegmentSum,fo as unstack,dt as upcastType,J1 as upperBound,y as util,UK as valueAndGrad,GK as valueAndGrads,eN as variable,Vw as variableGrads,Vce as version,z8 as version_converter,OX as version_core,yY as version_cpu,iae as version_wasm,d9 as version_webgl,$at as webgl,Ec as webgl_util,Zv as webgpu_util,lo as where,Yd as whereAsync,Gr as zeros,Gt as zerosLike};\n", "import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial, config: Partial, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n for (const key of Object.keys(config)) {\n if (typeof config[key] === 'object') {\n validate(defaults[key], config[key], key, msgs);\n } else {\n const defined = defaults && (typeof defaults[key] !== 'undefined');\n if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n const same = defaults && typeof defaults[key] === typeof config[key];\n if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n }\n // ok = ok && defined && same;\n }\n if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: number[]) => data.reduce((acc: number[], val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n await waiting;\n}\n", "/* eslint-disable no-multi-spaces */\n\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu' | 'none';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/** Possible segmentation model behavior */\nexport type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n /** is module enabled? */\n enabled: boolean,\n /** path to model json file (relative to `modelBasePath` */\n modelPath: string,\n /** how many max frames to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipFrames: number,\n /** how many max milliseconds to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n /** is face rotation correction performed after detecting face?\n * used to correctly analyze faces under high angles\n */\n rotation: boolean,\n /** maximum number of detected faces */\n maxDetected: number,\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n /** minimum size in pixels of a detected face box before resutls are discared */\n minSize: number,\n /** minimum overlap between two detected faces before one is discarded */\n iouThreshold: number,\n /** how much should face box be enlarged over the min/max facial coordinates */\n scale: number,\n /** automatically pad image to square */\n square: boolean,\n /** should child models perform on masked image of a face */\n mask: boolean,\n /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n * if enabled it must be manually deallocated to avoid memory leak */\n return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n /** Keep detected faces that cannot be verified using facemesh */\n keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {\n /** how much should iris box be enlarged over the min/max iris coordinates */\n scale: number,\n}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n /** minimum confidence for a detected race before results are discarded */\n minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n detector: Partial,\n mesh: Partial,\n attention: Partial,\n iris: Partial,\n description: Partial,\n emotion: Partial,\n antispoof: Partial,\n liveness: Partial,\n gear: Partial,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n /** maximum number of detected bodies */\n maxDetected: number,\n /** minimum confidence for a detected body before results are discarded */\n minConfidence: number,\n /* experimental\n /** experimental: detector used for body model before actual analysis\n detector?: {\n /** experimental: enable body detector before body landmarks\n enabled: boolean,\n /** experimental: path to optional body detector model json file\n modelPath: string,\n /** experimental: minimum confidence for a detected body before results are discarded\n minConfidence: number,\n /** experimental: minimum overlap between two detected bodies before one is discarded\n iouThreshold: number\n },\n */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n /** should hand rotation correction be performed after hand detection? */\n rotation: boolean,\n /** minimum confidence for a detected hand before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected hands before one is discarded */\n iouThreshold: number,\n /** maximum number of detected hands */\n maxDetected: number,\n /** should hand landmarks be detected or just return detected hand box */\n landmarks: boolean,\n detector: {\n /** path to hand detector model json */\n modelPath?: string,\n },\n skeleton: {\n /** path to hand skeleton model json */\n modelPath?: string,\n },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n /** minimum confidence for a detected objects before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected objects before one is discarded */\n iouThreshold: number,\n /** maximum number of detected objects */\n maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n /** downsample ratio, adjust to reflect approximately how much of input is taken by body */\n ratio: number,\n /** possible rvm segmentation mode */\n mode: SegmentationEnum,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n /** are image filters enabled? */\n enabled: boolean,\n /** perform image histogram equalization\n * - equalization is performed on input as a whole and detected face before its passed for further analysis\n */\n equalization: boolean,\n /** resize input width\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n width: number,\n /** resize input height\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n height: number,\n /** return processed canvas imagedata in result */\n return: boolean,\n /** flip input as mirror image */\n flip: boolean,\n /** apply auto-brighness */\n autoBrightness: boolean,\n /** range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** image negative */\n negative: boolean,\n /** image sepia colors */\n sepia: boolean,\n /** image vintage colors */\n vintage: boolean,\n /** image kodachrome colors */\n kodachrome: boolean,\n /** image technicolor colors */\n technicolor: boolean,\n /** image polaroid camera effect */\n polaroid: boolean,\n /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n /** is gesture detection enabled? */\n enabled: boolean,\n}\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n /** Backend used for TFJS operations\n * valid build-in backends are:\n * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n * - NodeJS: `cpu`, `wasm`, `tensorflow`\n * default: `webgl` for browser and `tensorflow` for nodejs\n */\n backend: BackendEnum,\n\n /** Path to *.wasm files if backend is set to `wasm`\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPath: string,\n\n /** Force WASM loader to use platform fetch\n *\n * default: false\n */\n wasmPlatformFetch: boolean,\n\n /** Print debug statements to console\n *\n * default: `true`\n */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially\n *\n * default: `true`\n */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - used by `webgl`, `humangl` and `webgpu` backends\n *\n * default: `full`\n */\n warmup: WarmupEnum,\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n *\n * default: `../models/` for browsers and `file://models/` for nodejs\n */\n modelBasePath: string,\n\n /** Cache models in IndexDB on first sucessfull load\n * default: true if indexdb is available (browsers), false if its not (nodejs)\n */\n cacheModels: boolean,\n\n /** Validate kernel ops used in model during model load\n * default: true\n * any errors will be printed on console but will be treated as non-fatal\n */\n validateModels: boolean,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n *\n * default: 0.7\n */\n cacheSensitivity: number;\n\n /** Explicit flags passed to initialize TFJS */\n flags: Record,\n\n /** Software Kernels\n * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n */\n softwareKernels: boolean,\n\n /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n deallocate: boolean;\n\n /** Internal Variable */\n skipAllowed: boolean;\n\n /** Filter config {@link FilterConfig} */\n filter: Partial,\n\n /** Gesture config {@link GestureConfig} */\n gesture: Partial;\n\n /** Face config {@link FaceConfig} */\n face: Partial,\n\n /** Body config {@link BodyConfig} */\n body: Partial,\n\n /** Hand config {@link HandConfig} */\n hand: Partial,\n\n /** Object config {@link ObjectConfig} */\n object: Partial,\n\n /** Segmentation config {@link SegmentationConfig} */\n segmentation: Partial,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n backend: '',\n modelBasePath: '',\n cacheModels: true,\n validateModels: true,\n wasmPath: '',\n wasmPlatformFetch: false,\n debug: false,\n async: true,\n warmup: 'full',\n cacheSensitivity: 0.70,\n skipAllowed: false,\n deallocate: false,\n flags: {},\n softwareKernels: false,\n filter: {\n enabled: true,\n equalization: false,\n width: 0,\n height: 0,\n flip: false,\n return: true,\n autoBrightness: true,\n brightness: 0,\n contrast: 0,\n sharpness: 0,\n blur: 0,\n saturation: 0,\n hue: 0,\n negative: false,\n sepia: false,\n vintage: false,\n kodachrome: false,\n technicolor: false,\n polaroid: false,\n pixelate: 0,\n },\n gesture: {\n enabled: true,\n },\n face: {\n enabled: true,\n detector: {\n modelPath: 'blazeface.json',\n rotation: false,\n maxDetected: 1,\n skipFrames: 99,\n skipTime: 2500,\n minConfidence: 0.2,\n minSize: 0,\n iouThreshold: 0.1,\n scale: 1.4,\n mask: false,\n return: false,\n },\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json',\n keepInvalid: false,\n },\n attention: {\n enabled: false,\n modelPath: 'facemesh-attention.json',\n },\n iris: {\n enabled: true,\n scale: 2.3,\n modelPath: 'iris.json',\n },\n emotion: {\n enabled: true,\n minConfidence: 0.1,\n skipFrames: 99,\n skipTime: 1500,\n modelPath: 'emotion.json',\n },\n description: {\n enabled: true,\n modelPath: 'faceres.json',\n skipFrames: 99,\n skipTime: 3000,\n minConfidence: 0.1,\n },\n antispoof: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'antispoof.json',\n },\n liveness: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'liveness.json',\n },\n },\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json',\n maxDetected: -1,\n minConfidence: 0.3,\n skipFrames: 1,\n skipTime: 200,\n },\n hand: {\n enabled: true,\n rotation: true,\n skipFrames: 99,\n skipTime: 1000,\n minConfidence: 0.50,\n iouThreshold: 0.2,\n maxDetected: -1,\n landmarks: true,\n detector: {\n modelPath: 'handtrack.json',\n },\n skeleton: {\n modelPath: 'handlandmark-lite.json',\n },\n },\n object: {\n enabled: false,\n modelPath: 'centernet.json',\n minConfidence: 0.2,\n iouThreshold: 0.4,\n maxDetected: 10,\n skipFrames: 99,\n skipTime: 2000,\n },\n segmentation: {\n enabled: false,\n modelPath: 'rvm.json',\n ratio: 0.5,\n mode: 'default',\n },\n};\n\nexport { config as defaults };\n", "export const vertexIdentity = `\n precision highp float;\n attribute vec2 pos;\n attribute vec2 uv;\n varying vec2 vUv;\n uniform float flipY;\n void main(void) {\n vUv = uv;\n gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n }\n`;\n\nexport const fragmentIdentity = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n void main(void) {\n gl_FragColor = texture2D(texture, vUv);\n }\n`;\n\nexport const colorMatrixWithAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n gl_FragColor.a = c.a;\n }\n`;\n\nexport const pixelate = `\n precision highp float;\n varying vec2 vUv;\n uniform vec2 size;\n uniform sampler2D texture;\n vec2 pixelate(vec2 coord, vec2 size) {\n return floor( coord / size ) * size;\n }\n void main(void) {\n gl_FragColor = vec4(0.0);\n vec2 coord = pixelate(vUv, size);\n gl_FragColor += texture2D(texture, coord);\n }\n`;\n\nexport const blur = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n void main(void) {\n gl_FragColor = vec4(0.0);\n gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv )*0.159576912161;\n gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;\n }\n`;\n\nexport const convolution = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n uniform float m[9];\n void main(void) {\n vec4 c11 = texture2D(texture, vUv - px); // top left\n vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n vec4 c22 = texture2D(texture, vUv); // mid center\n vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n gl_FragColor = \n c11 * m[0] + c12 * m[1] + c22 * m[2] +\n c21 * m[3] + c22 * m[4] + c23 * m[5] +\n c31 * m[6] + c32 * m[7] + c33 * m[8];\n gl_FragColor.a = c22.a;\n }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, collection) => {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n};\n\nclass GLProgram {\n uniform = {};\n attribute = {};\n gl: WebGLRenderingContext;\n id: WebGLProgram;\n\n constructor(gl, vertexSource, fragmentSource) {\n this.gl = gl;\n const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n this.id = this.gl.createProgram() as WebGLProgram;\n if (!vertexShader || !fragmentShader) return;\n if (!this.id) {\n log('filter: could not create webgl program');\n return;\n }\n this.gl.attachShader(this.id, vertexShader);\n this.gl.attachShader(this.id, fragmentShader);\n this.gl.linkProgram(this.id);\n if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || 'unknown'}`);\n return;\n }\n this.gl.useProgram(this.id);\n collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n }\n\n compile = (source, type): WebGLShader | null => {\n const shader = this.gl.createShader(type);\n if (!shader) {\n log('filter: could not create shader');\n return null;\n }\n this.gl.shaderSource(shader, source);\n this.gl.compileShader(shader);\n if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || 'unknown'}`);\n return null;\n }\n return shader;\n };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n let drawCount = 0;\n let sourceTexture: WebGLTexture | null = null;\n let lastInChain = false;\n let currentFramebufferIndex = -1;\n let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n let filterChain: Record[] = [];\n let vertexBuffer: WebGLBuffer | null = null;\n let currentProgram: GLProgram | null = null;\n const fxcanvas = canvas(100, 100) as HTMLCanvasElement;\n const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n const DRAW = { INTERMEDIATE: 1 };\n const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n if (!gl) {\n log('filter: cannot get webgl context');\n return;\n }\n // @ts-ignore used for sanity checks outside of imagefx\n this.gl = gl;\n\n function resize(width, height) {\n if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n fxcanvas.width = width;\n fxcanvas.height = height;\n if (!vertexBuffer) { // Create the context if we don't have it yet\n const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n vertexBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n tempFramebuffers = [null, null]; // Delete old temp framebuffers\n }\n\n function createFramebufferTexture(width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n }\n\n function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n }\n\n function draw(flags = 0) {\n if (!currentProgram) return;\n let source: WebGLTexture | null = null;\n let target: WebGLFramebuffer | null = null;\n let flipY = false;\n if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n drawCount++;\n if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n target = null;\n flipY = drawCount % 2 === 0;\n } else {\n currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n }\n gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n }\n\n function compileShader(fragmentSource): GLProgram | null {\n if (shaderProgramCache[fragmentSource]) {\n currentProgram = shaderProgramCache[fragmentSource];\n gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n return currentProgram;\n }\n currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n if (!currentProgram) {\n log('filter: could not get webgl program');\n return null;\n }\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n shaderProgramCache[fragmentSource] = currentProgram;\n return currentProgram;\n }\n\n const filter = {\n colorMatrix: (matrix: number[]) => { // general color matrix filter\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n ? shaders.colorMatrixWithoutAlpha\n : shaders.colorMatrixWithAlpha;\n const program = compileShader(shader);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n draw();\n },\n\n brightness: (brightness: number) => {\n const b = (brightness || 0) + 1;\n filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n saturation: (amount: number) => {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturate: () => {\n filter.saturation(-1);\n },\n\n contrast: (amount: number) => {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n negative: () => {\n filter.contrast(-2);\n },\n\n hue: (rotation: number) => {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturateLuminance: () => {\n filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n sepia: () => {\n filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n brownie: () => {\n filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n vintagePinhole: () => {\n filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n kodachrome: () => {\n filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n technicolor: () => {\n filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n polaroid: () => {\n filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n shiftToBGR: () => {\n filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n convolution: (matrix: number[]) => { // general convolution Filter\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / fxcanvas.width;\n const pixelSizeY = 1 / fxcanvas.height;\n const program = compileShader(shaders.convolution);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n draw();\n },\n\n detectEdges: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n },\n\n sobelX: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n },\n\n sobelY: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n },\n\n sharpen: (amount) => {\n const a = amount || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n },\n\n emboss: (size: number) => {\n const s = size || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n },\n\n blur: (size: number) => {\n const blurSizeX = (size / 7) / fxcanvas.width;\n const blurSizeY = (size / 7) / fxcanvas.height;\n const program = compileShader(shaders.blur);\n if (!program) return;\n // Vertical\n gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n draw();\n },\n\n pixelate: (size: number) => {\n const blurSizeX = (size) / fxcanvas.width;\n const blurSizeY = (size) / fxcanvas.height;\n const program = compileShader(shaders.pixelate);\n if (!program) return;\n gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n draw();\n },\n };\n\n // @ts-ignore this\n this.add = function (name) {\n const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\n const func = filter[name];\n filterChain.push({ func, args });\n };\n\n // @ts-ignore this\n this.reset = function () {\n filterChain = [];\n };\n\n // @ts-ignore this\n this.get = function () {\n return filterChain;\n };\n\n // @ts-ignore this\n this.apply = function (image) {\n resize(image.width, image.height);\n drawCount = 0;\n if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n for (let i = 0; i < filterChain.length; i++) {\n lastInChain = (i === filterChain.length - 1);\n const f = filterChain[i];\n // @ts-ignore function assigment\n f.func.apply(this, f.args || []);\n }\n return fxcanvas;\n };\n\n // @ts-ignore this\n this.draw = function (image) {\n this.add('brightness', 0);\n return this.apply(image);\n };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise {\n const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n const rgb = tf.split(squeeze, 3, 2);\n const min: Tensor[] = [tf.min(rgb[0]), tf.min(rgb[1]), tf.min(rgb[2])]; // minimum pixel value per channel T[]\n const max: Tensor[] = [tf.max(rgb[0]), tf.max(rgb[1]), tf.max(rgb[2])]; // maximum pixel value per channel T[]\n // const absMin = await Promise.all(min.map((channel) => channel.data())); // minimum pixel value per channel A[]\n // const minValue = Math.min(absMax[0][0], absMin[1][0], absMin[2][0]);\n const absMax = await Promise.all(max.map((channel) => channel.data())); // maximum pixel value per channel A[]\n const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n const maxRange = maxValue > 1 ? 255 : 1;\n const factor = maxRange / maxValue;\n let final: Tensor;\n if (factor > 1) {\n const sub = [tf.sub(rgb[0], min[0]), tf.sub(rgb[1], min[1]), tf.sub(rgb[2], min[2])]; // channels offset by min values\n const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])]; // channel ranges\n // const fact = [tf.div(maxRange, absMax[0]), tf.div(maxRange, absMax[1]), tf.div(maxRange, absMax[1])]; // factors between\n const enh = [tf.mul(sub[0], factor), tf.mul(sub[1], factor), tf.mul(sub[2], factor)];\n const stack = tf.stack([enh[0], enh[1], enh[2]], 2);\n final = tf.reshape(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);\n tf.dispose([...sub, ...range, ...enh, stack]);\n } else {\n final = tf.expandDims(squeeze, 0);\n }\n tf.dispose([...rgb, ...min, ...max, rgb, squeeze, inputImage]);\n return final;\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Config } from '../exports';\nimport type { Tensor, Tensor3D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // eslint-disable-line @typescript-eslint/no-redundant-type-constituents\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n inputSum: 0,\n cacheDiff: 1,\n sumMethod: 0,\n inputTensor: undefined,\n};\n\nexport function reset() {\n last.inputSum = 0;\n last.cacheDiff = 1;\n last.sumMethod = 0;\n last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n let c: AnyCanvas;\n if (env.browser) { // browser defines canvas object\n if (env.worker) { // if runing in web worker use OffscreenCanvas\n if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n c = new OffscreenCanvas(width, height);\n } else { // otherwise use DOM canvas\n if (typeof document !== 'undefined') {\n c = document.createElement('canvas');\n c.width = width;\n c.height = height;\n } else if (typeof navigator !== 'undefined' && navigator.product === 'ReactNative') {\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n else throw new Error('canvas error: attempted to use canvas in react-native without canvas support installed');\n } else {\n throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n }\n }\n } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n }\n // @ts-ignore its either defined or we already threw an error\n return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n const outputCanvas = output || canvas(input.width, input.height);\n const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctx.drawImage(input, 0, 0);\n return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor4D | null, canvas: AnyCanvas | null }> {\n if (!input) {\n // throw new Error('input is missing');\n if (config.debug) log('input error: input is missing');\n return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n }\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('input error: type not recognized');\n }\n if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n let tensor: Tensor | null = null;\n if (input['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n if (!(input as Tensor).shape) throw new Error('input error: attempted to use tensor without a shape');\n if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n tensor = tf.expandDims(input, 0);\n } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n const rgb = tf.slice3d(input as Tensor3D, [0, 0, 0], [-1, -1, 3]);\n tensor = tf.expandDims(rgb, 0);\n tf.dispose(rgb);\n }\n } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n tensor = tf.clone(input);\n } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n tensor = tf.slice4d(input as Tensor4D, [0, 0, 0, 0], [-1, -1, -1, 3]);\n }\n }\n // at the end shape must be [1, height, width, 3]\n if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${((input as Tensor).shape).toString()}`);\n if ((tensor).dtype === 'int32') {\n const cast = tf.cast(tensor, 'float32');\n tf.dispose(tensor);\n tensor = cast;\n }\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n }\n // check if resizing will be needed\n if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).readyState <= 2) {\n if (config.debug) log('input stream is not ready');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n const originalWidth: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n const originalHeight: number = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n if (!originalWidth || !originalHeight) {\n if (config.debug) log('cannot determine input dimensions');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n let targetWidth: number = originalWidth;\n let targetHeight: number = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n }\n\n // create our canvas and resize it if needed\n if ((config.filter?.width || 0) > 0) targetWidth = config.filter.width as number;\n else if ((config.filter?.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n if ((config.filter.height || 0) > 0) targetHeight = config.filter.height as number;\n else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n // draw input to our canvas\n const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n inCtx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n inCtx.translate(originalWidth, 0);\n inCtx.scale(-1, 1);\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n }\n }\n\n if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n // imagefx transforms using gl from input canvas to output canvas\n if (config.filter.enabled && env.webgl.supported) {\n if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n env.filter = !!fx;\n if (!fx?.add) {\n if (config.debug) log('input process error: cannot initialize filters');\n env.webgl.supported = false;\n config.filter.enabled = false;\n copy(inCanvas, outCanvas); // filter failed to initialize\n // return { tensor: null, canvas: inCanvas };\n } else {\n fx.reset();\n if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n if (config.filter.negative) fx.add('negative');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.vintage) fx.add('brownie');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.kodachrome) fx.add('kodachrome');\n if (config.filter.technicolor) fx.add('technicolor');\n if (config.filter.polaroid) fx.add('polaroid');\n if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n if (fx.get()?.length > 1) outCanvas = fx.apply(inCanvas);\n else outCanvas = fx.draw(inCanvas);\n }\n } else {\n copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n if (fx) fx = null;\n env.filter = !!fx;\n }\n\n if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n // create tensor from image unless input was a tensor already\n let pixels;\n let depth = 3;\n if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || ((input as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n if (env.browser && tf.browser) {\n pixels = tf.browser ? tf.browser.fromPixels(input as ImageData) : null;\n } else {\n depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n // const arr = Uint8Array.from(input['data']);\n const arr = new Uint8Array((input as ImageData).data.buffer);\n pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).width, depth], 'int32');\n }\n } else {\n if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n if (tf.browser && env.browser) {\n if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n pixels = tf.browser.fromPixels(outCanvas as HTMLCanvasElement); // safe to reuse since both backend and context are gl based\n } else {\n tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n pixels = tf.browser.fromPixels(tmpCanvas as HTMLCanvasElement);\n }\n } else {\n const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n depth = tempData.data.length / targetWidth / targetHeight;\n const arr = new Uint8Array(tempData.data.buffer);\n pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n }\n }\n if (depth === 4) { // rgba to rgb\n const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n tf.dispose(pixels);\n pixels = rgb;\n }\n if (!pixels) throw new Error('input error: cannot create tensor');\n const casted: Tensor = tf.cast(pixels, 'float32');\n const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n tf.dispose([pixels, casted]);\n\n if (config.filter.autoBrightness) {\n const max = tf.max(tensor);\n const maxVal = await max.data();\n config.filter.brightness = maxVal[0] > 1 ? (1 - maxVal[0] / 255) : (1 - maxVal[0]);\n tf.dispose(max);\n }\n\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n}\n\n/*\nconst checksum = async (input: Tensor): Promise => { // use tf sum or js based sum loop depending on which is faster\n const resizeFact = 48;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n const tfSum = async (): Promise => {\n const sumT = tf.sum(reduced);\n const sum0 = await sumT.data();\n tf.dispose(sumT);\n return sum0[0];\n };\n const jsSum = async (): Promise => {\n const reducedData = await reduced.data(); // raw image rgb array\n let sum0 = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n return sum0;\n };\n if (last.sumMethod === 0) {\n const t0 = now();\n await jsSum();\n const t1 = now();\n await tfSum();\n const t2 = now();\n last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n }\n const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n tf.dispose(reduced);\n return res;\n};\n*/\n\nexport async function skip(config: Partial, input: Tensor) {\n let skipFrame = false;\n if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n /*\n const checkSum = await checksum(input);\n const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n last.inputSum = checkSum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n */\n\n if (!last.inputTensor) {\n last.inputTensor = tf.clone(input);\n } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n tf.dispose(last.inputTensor);\n last.inputTensor = tf.clone(input);\n } else {\n const t: Record = {};\n t.diff = tf.sub(input, last.inputTensor);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n last.inputTensor = tf.clone(input);\n skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n }\n return skipFrame;\n}\n\nexport async function compare(config: Partial, input1: Tensor, input2: Tensor): Promise {\n const t: Record = {};\n if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n return 0;\n }\n if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n return 0;\n }\n t.input1 = tf.clone(input1);\n t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2 as Tensor3D, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n t.diff = tf.sub(t.input1, t.input2);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n return diffRelative;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n /** Running in Browser */\n browser: boolean;\n /** Running in NodeJS */\n node: boolean;\n /** Running in WebWorker thread */\n worker: boolean;\n /** Detected platform */\n platform: string = '';\n /** Detected agent */\n agent: string = '';\n /** List of supported backends */\n backends: string[] = [];\n /** Has any work been performed so far */\n initial: boolean;\n /** Are image filters supported? */\n filter: boolean | undefined;\n /** TFJS instance details */\n tfjs: {\n version: undefined | string,\n };\n /** Is offscreenCanvas supported? */\n offscreen: undefined | boolean;\n /** Are performance counter instant values or additive */\n perfadd: boolean = false;\n /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n tensorflow: {\n version: undefined | string,\n gpu: undefined | boolean,\n } = {\n version: undefined,\n gpu: undefined,\n };\n /** WASM detected capabilities */\n wasm: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n simd: undefined | boolean,\n multithread: undefined | boolean,\n } = {\n supported: undefined,\n backend: undefined,\n simd: undefined,\n multithread: undefined,\n };\n /** WebGL detected capabilities */\n webgl: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n version: undefined | string,\n renderer: undefined | string,\n shader: undefined | string,\n vendor: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n version: undefined,\n renderer: undefined,\n shader: undefined,\n vendor: undefined,\n };\n /** WebGPU detected capabilities */\n webgpu: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n adapter: undefined | GPUAdapterInfo,\n } = {\n supported: undefined,\n backend: undefined,\n adapter: undefined,\n };\n /** CPU info */\n cpu: {\n model: undefined | string,\n flags: string[],\n } = {\n model: undefined,\n flags: [],\n };\n /** List of supported kernels for current backend */\n kernels: string[] = [];\n\n /** MonkeyPatch for Canvas/Image/ImageData */\n #canvas: undefined;\n #image: undefined;\n #imageData: undefined;\n\n get Canvas() { return this.#canvas; }\n set Canvas(val) { this.#canvas = val; globalThis.Canvas = val; }\n get Image() { return this.#image; }\n // @ts-ignore monkey-patch;\n set Image(val) { this.#image = val; globalThis.Image = val; }\n get ImageData() { return this.#imageData; }\n // @ts-ignore monkey-patch;\n set ImageData(val) { this.#imageData = val; globalThis.ImageData = val; }\n\n constructor() {\n this.browser = (typeof navigator !== 'undefined') && (typeof navigator.appVersion !== 'undefined');\n this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n this.tfjs = { version: tf.version['tfjs-core'] };\n this.offscreen = typeof OffscreenCanvas !== 'undefined';\n this.initial = true;\n\n // @ts-ignore WorkerGlobalScope evaluated in browser only\n this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n if ((typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined')) { // TBD replace with navigator.userAgentData once in mainline\n const agent = navigator.userAgent || '';\n const raw = agent.match(/\\(([^()]+)\\)/g);\n if (raw?.[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n this.agent = agent.replace(raw[0], '');\n if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n this.agent = this.agent.replace(/ /g, ' ');\n }\n } else if (typeof process !== 'undefined') {\n this.platform = `${process.platform} ${process.arch}`;\n this.agent = `NodeJS ${process.version}`;\n }\n }\n\n /** update backend information */\n async updateBackend() {\n // analyze backends\n this.backends = Object.keys(tf.engine().registryFactory);\n try { // backend may not be initialized\n this.tensorflow = {\n version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),\n gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),\n };\n } catch { /**/ }\n this.wasm.supported = typeof WebAssembly !== 'undefined';\n this.wasm.backend = this.backends.includes('wasm');\n if (this.wasm.supported && this.wasm.backend) {\n this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n }\n const c = image.canvas(100, 100);\n const gl = c ? c.getContext('webgl2') as WebGL2RenderingContext : undefined; // causes too many gl contexts\n this.webgl.supported = typeof gl !== 'undefined';\n this.webgl.backend = this.backends.includes('webgl');\n if (this.webgl.supported && this.webgl.backend && gl) {\n this.webgl.version = gl.getParameter(gl.VERSION);\n this.webgl.vendor = gl.getParameter(gl.VENDOR);\n this.webgl.renderer = gl.getParameter(gl.RENDERER);\n this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);\n }\n this.webgpu.supported = this.browser && typeof navigator !== 'undefined' && typeof navigator.gpu !== 'undefined';\n this.webgpu.backend = this.backends.includes('webgpu');\n try {\n if (this.webgpu.supported) {\n const adapter = await navigator.gpu.requestAdapter();\n if (adapter) {\n // @ts-ignore requestAdapterInfo is not in tslib\n if ('requestAdapterInfo' in adapter) this.webgpu.adapter = await adapter.requestAdapterInfo();\n // @ts-ignore adapter.info is not in tslib\n else this.webgpu.adapter = await adapter.info;\n }\n }\n } catch {\n this.webgpu.supported = false;\n }\n try {\n this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n } catch { /**/ }\n }\n\n /** update cpu information */\n updateCPU() {\n const cpu = { model: '', flags: [] };\n if (this.node && this.platform.startsWith('linux')) {\n /*\n const fs = require('fs');\n try {\n const data = fs.readFileSync('/proc/cpuinfo').toString();\n for (const line of data.split('\\n')) {\n if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n }\n } catch { }\n */\n }\n if (!this.cpu) Object.defineProperty(this, 'cpu', { value: cpu });\n else this.cpu = cpu;\n }\n}\n\nexport const env = new Env();\n", "import { log } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n /**\n * element can be:\n * - string which indicates dom element id\n * - actual HTMLVideo dom element\n * - undefined in which case a new HTMLVideoElement will be created\n */\n element: string | HTMLVideoElement | undefined,\n /** print messages on console */\n debug: boolean,\n /** use front or back camera */\n mode: 'front' | 'back',\n /** camera crop mode */\n crop: boolean,\n /** desired webcam width */\n width: number,\n /** desired webcam height */\n height: number,\n /** deviceId of the video device to use */\n id?: string,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n /** current webcam configuration */\n config: WebCamConfig;\n /** instance of dom element associated with webcam stream */\n element: HTMLVideoElement | undefined;\n /** active webcam stream */\n stream: MediaStream | undefined;\n /** enumerated video devices */\n devices: MediaDeviceInfo[] = [];\n\n constructor() {\n this.config = {\n element: undefined,\n debug: true,\n mode: 'front',\n crop: false,\n width: 0,\n height: 0,\n };\n }\n\n /** get active webcam stream track */\n public get track(): MediaStreamTrack | undefined {\n if (!this.stream) return undefined;\n return this.stream.getVideoTracks()[0];\n }\n\n /** get webcam capabilities */\n public get capabilities(): MediaTrackCapabilities | undefined {\n if (!this.track) return undefined;\n return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n }\n\n /** get webcam constraints */\n public get constraints(): MediaTrackConstraints | undefined {\n if (!this.track) return undefined;\n return this.track.getConstraints ? this.track.getConstraints() : undefined;\n }\n\n /** get webcam settings */\n public get settings(): MediaTrackSettings | undefined {\n if (!this.stream) return undefined;\n const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n return track.getSettings ? track.getSettings() : undefined;\n }\n\n /** get webcam label */\n public get label(): string {\n if (!this.track) return '';\n return this.track.label;\n }\n\n /** is webcam paused */\n public get paused(): boolean {\n return this.element?.paused || false;\n }\n\n /** webcam current width */\n public get width(): number {\n return this.element?.videoWidth || 0;\n }\n\n /** webcam current height */\n public get height(): number {\n return this.element?.videoHeight || 0;\n }\n\n public enumerate = async (): Promise => {\n try {\n const devices = await navigator.mediaDevices.enumerateDevices();\n this.devices = devices.filter((device) => device.kind === 'videoinput');\n } catch {\n this.devices = [];\n }\n return this.devices;\n };\n\n /** start method initializizes webcam stream and associates it with a dom video element */\n public start = async (webcamConfig?: Partial): Promise => {\n // set config\n if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n if (webcamConfig?.id) this.config.id = webcamConfig?.id;\n\n // use or create dom element\n if (webcamConfig?.element) {\n if (typeof webcamConfig.element === 'string') {\n const el = document.getElementById(webcamConfig.element);\n if (el && el instanceof HTMLVideoElement) {\n this.element = el;\n } else {\n if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n return `webcam error: cannot get dom element: ${webcamConfig.element}`;\n }\n } else if (webcamConfig.element instanceof HTMLVideoElement) {\n this.element = webcamConfig.element;\n } else {\n if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n return `webcam error: unknown dom element: ${webcamConfig.element}`;\n }\n } else {\n this.element = document.createElement('video');\n }\n\n // set constraints to use\n const requestedConstraints: MediaStreamConstraints = {\n audio: false,\n video: {\n facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n // @ts-ignore // resizeMode is still not defined in tslib\n resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n },\n };\n if (this.config?.width > 0) (requestedConstraints.video as MediaTrackConstraints).width = { ideal: this.config.width };\n if (this.config?.height > 0) (requestedConstraints.video as MediaTrackConstraints).height = { ideal: this.config.height };\n if (this.config.id) (requestedConstraints.video as MediaTrackConstraintSet).deviceId = this.config.id;\n\n // set default event listeners\n this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n if (!this.element || !this.stream) return;\n if (this.element.paused) await this.element.play();\n else this.element.pause();\n });\n\n // get webcam and set it to run in dom element\n if (!navigator?.mediaDevices) {\n if (this.config.debug) log('webcam error', 'no devices');\n return 'webcam error: no devices';\n }\n try {\n this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n } catch (err) {\n log('webcam', err);\n return `webcam error: ${err}`;\n }\n if (!this.stream) {\n if (this.config.debug) log('webcam error', 'no stream');\n return 'webcam error no stream';\n }\n this.element.srcObject = this.stream; // assign it to dom element\n const ready = new Promise((resolve) => { // wait until stream is ready\n if (!this.element) resolve(false);\n else this.element.onloadeddata = () => resolve(true);\n });\n await ready;\n await this.element.play(); // start playing\n\n if (this.config.debug) {\n log('webcam', {\n width: this.width,\n height: this.height,\n label: this.label,\n stream: this.stream,\n track: this.track,\n settings: this.settings,\n constraints: this.constraints,\n capabilities: this.capabilities,\n });\n }\n return `webcam: ${this.label}`;\n };\n\n /** pause webcam video method */\n public pause = (): void => {\n if (this.element) this.element.pause();\n };\n\n /** play webcam video method */\n public play = async (): Promise => {\n if (this.element) await this.element.play();\n };\n\n /** stop method stops active webcam stream track and disconnects webcam */\n public stop = (): void => {\n if (this.config.debug) log('webcam', 'stop');\n if (this.track) this.track.stop();\n };\n}\n", "{\n \"antispoof\": 853098,\n \"blazeface\": 538928,\n \"centernet\": 4030290,\n \"emotion\": 820516,\n \"facemesh\": 1477958,\n \"faceres\": 6978814,\n \"handlandmark-lite\": 2023432,\n \"handtrack\": 2964837,\n \"iris\": 2599092,\n \"liveness\": 592976,\n \"models\": 0,\n \"movenet-lightning\": 4650216,\n \"affectnet-mobilenet\": 6920630,\n \"age\": 161240,\n \"blazeface-back\": 538928,\n \"blazeface-front\": 402048,\n \"blazepose-detector\": 5928856,\n \"blazepose-full\": 6339202,\n \"blazepose-heavy\": 27502466,\n \"blazepose-lite\": 2726402,\n \"efficientpose\": 5651240,\n \"faceboxes\": 2013002,\n \"facemesh-attention-pinto\": 2387598,\n \"facemesh-attention\": 2382414,\n \"facemesh-detection-full\": 1026192,\n \"facemesh-detection-short\": 201268,\n \"faceres-deep\": 13957620,\n \"gear-e1\": 112438,\n \"gear-e2\": 112438,\n \"gear\": 1498916,\n \"gender-ssrnet-imdb\": 161236,\n \"gender\": 201808,\n \"handdetect\": 3515612,\n \"handlandmark-full\": 5431368,\n \"handlandmark-sparse\": 5286322,\n \"handskeleton\": 5502280,\n \"meet\": 372228,\n \"mobileface\": 2183192,\n \"mobilefacenet\": 5171976,\n \"movenet-multipose\": 9448838,\n \"movenet-thunder\": 12477112,\n \"nanodet\": 7574558,\n \"posenet\": 5032780,\n \"rvm\": 3739355,\n \"selfie\": 212886,\n \"anti-spoofing\": 853098,\n \"efficientpose-i-lite\": 2269064,\n \"efficientpose-ii-lite\": 5651240,\n \"efficientpose-iv\": 25643252,\n \"insightface-efficientnet-b0\": 13013224,\n \"insightface-ghostnet-strides1\": 8093408,\n \"insightface-ghostnet-strides2\": 8049584,\n \"insightface-mobilenet-emore\": 6938536,\n \"insightface-mobilenet-swish\": 12168584,\n \"nanodet-e\": 12319156,\n \"nanodet-g\": 7574558,\n \"nanodet-m\": 1887474,\n \"nanodet-t\": 5294216\n}", "import * as tf from 'dist/tfjs.esm.js';\nimport { log, join } from '../util/util';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n cacheModels: true,\n cacheSupported: true,\n verbose: true,\n debug: false,\n modelBasePath: '',\n};\n\nexport interface ModelInfo {\n name: string,\n loaded: boolean,\n inCache: boolean,\n sizeDesired: number,\n sizeFromManifest: number,\n sizeLoadedWeights: number,\n url: string,\n}\n\nexport const modelStats: Record = {};\n\nasync function httpHandler(url: string, init?: RequestInit): Promise {\n if (options.debug) log('load model fetch:', url, init);\n return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n options.cacheModels = config.cacheModels;\n options.verbose = config.debug;\n options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise {\n let modelUrl = join(options.modelBasePath, modelPath || '');\n if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n modelStats[shortModelName] = {\n name: shortModelName,\n loaded: false,\n sizeFromManifest: 0,\n sizeLoadedWeights: 0,\n sizeDesired: modelsDefs[shortModelName],\n inCache: false,\n url: '',\n };\n options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n let cachedModels = {};\n try {\n cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n } catch {\n options.cacheSupported = false;\n }\n modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;\n const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].url, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n modelStats[shortModelName].loaded = false;\n try {\n // @ts-ignore private function\n model.findIOHandler(); // decide how to actually load a model\n if (options.debug) log('model load handler:', model['handler']);\n } catch (err) {\n log('error finding model i/o handler:', modelUrl, err);\n }\n try {\n // @ts-ignore private property\n const artifacts = await model.handler?.load() || null; // load manifest\n modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n if (artifacts) model.loadSync(artifacts); // load weights\n else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n // @ts-ignore private property\n modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || model.artifacts?.weightData?.[0].byteLength || 0;\n if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n modelStats[shortModelName].loaded = true;\n } catch (err) {\n log('error loading model:', modelUrl, err);\n }\n if (modelStats[shortModelName].loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n try {\n const saveResult = await model.save(cachedModelName);\n if (options.debug) log('model saved:', cachedModelName, saveResult);\n } catch (err) {\n log('error saving model:', modelUrl, err);\n }\n }\n return model;\n}\n", "{\n \"name\": \"@vladmandic/human\",\n \"version\": \"3.3.6\",\n \"description\": \"Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition\",\n \"sideEffects\": false,\n \"main\": \"dist/human.node.js\",\n \"module\": \"dist/human.esm.js\",\n \"browser\": \"dist/human.esm.js\",\n \"types\": \"types/human.d.ts\",\n \"exports\": {\n \"node\": \"./dist/human.node.js\",\n \"script\": \"./dist/human.js\",\n \"module\": \"./dist/human.esm.js\",\n \"types\": \"./types/human.d.ts\",\n \"dist/human\": \"./dist/human.js\",\n \"dist/human.js\": \"./dist/human.js\",\n \"dist/human.esm\": \"./dist/human.esm.js\",\n \"dist/human.esm.js\": \"./dist/human.esm.js\",\n \"dist/human.esm-nobundle\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.esm-nobundle.js\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.node\": \"./dist/human.node.js\",\n \"dist/human.node.js\": \"./dist/human.node.js\",\n \"dist/human.node-wasm\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-wasm.js\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-gpu\": \"./dist/human.node-gpu.js\",\n \"dist/human.node-gpu.js\": \"./dist/human.node-gpu.js\",\n \"require\": \"./dist/human.node.js\",\n \"import\": \"./dist/human.esm.js\"\n },\n \"author\": \"Vladimir Mandic \",\n \"bugs\": {\n \"url\": \"https://github.com/vladmandic/human/issues\"\n },\n \"homepage\": \"https://vladmandic.github.io/human/demo/index.html\",\n \"license\": \"MIT\",\n \"engines\": {\n \"node\": \">=14.0.0\"\n },\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/vladmandic/human.git\"\n },\n \"scripts\": {\n \"start\": \"node --no-warnings demo/nodejs/node.js\",\n \"dev\": \"build --profile development\",\n \"clean\": \"build --profile clean\",\n \"build\": \"rimraf test/build.log && node build.js\",\n \"test\": \"node --no-warnings --unhandled-rejections=strict --trace-uncaught test/node.js\",\n \"lint\": \"eslint *.json *.js src demo test models wiki\",\n \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n },\n \"keywords\": [\n \"human\",\n \"human-library\",\n \"face-detection\",\n \"faceid\",\n \"face-geometry\",\n \"face-embedding\",\n \"face-recognition\",\n \"face-description\",\n \"face-matching\",\n \"body-tracking\",\n \"body-segmentation\",\n \"hand-tracking\",\n \"iris-tracking\",\n \"age-estimation\",\n \"emotion-detection\",\n \"gender-prediction\",\n \"gesture-recognition\",\n \"gaze-tracking\",\n \"age-gender\",\n \"tensorflowjs\",\n \"tfjs\",\n \"tensorflow\"\n ],\n \"devDependencies\": {\n \"@html-eslint/eslint-plugin\": \"^0.46.1\",\n \"@html-eslint/parser\": \"^0.46.0\",\n \"@microsoft/api-extractor\": \"^7.52.11\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"4.22.0\",\n \"@tensorflow/tfjs-converter\": \"^4.22.0\",\n \"@tensorflow/tfjs-core\": \"^4.22.0\",\n \"@tensorflow/tfjs-data\": \"^4.22.0\",\n \"@tensorflow/tfjs-layers\": \"^4.22.0\",\n \"@tensorflow/tfjs-node\": \"^4.22.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.22.0\",\n \"@types/emscripten\": \"^1.40.1\",\n \"@types/node\": \"^24.3.0\",\n \"@types/offscreencanvas\": \"^2019.7.3\",\n \"@typescript-eslint/eslint-plugin\": \"^8.41.0\",\n \"@typescript-eslint/parser\": \"^8.41.0\",\n \"@vladmandic/build\": \"^0.10.3\",\n \"@vladmandic/pilogger\": \"^0.5.2\",\n \"@vladmandic/tfjs\": \"github:vladmandic/tfjs\",\n \"canvas\": \"^3.2.0\",\n \"esbuild\": \"^0.25.9\",\n \"eslint\": \"8.57.0\",\n \"eslint-config-airbnb-base\": \"^15.0.0\",\n \"eslint-plugin-html\": \"^8.1.3\",\n \"eslint-plugin-import\": \"^2.32.0\",\n \"eslint-plugin-json\": \"^4.0.1\",\n \"eslint-plugin-markdown\": \"^5.1.0\",\n \"eslint-plugin-node\": \"^11.1.0\",\n \"eslint-plugin-promise\": \"^7.2.1\",\n \"rimraf\": \"^6.0.1\",\n \"tslib\": \"^2.8.1\",\n \"typedoc\": \"0.28.11\",\n \"typescript\": \"5.9.2\"\n }\n}\n", "/** TFJS custom backend registration */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as image from '../image/image';\nimport type { AnyCanvas } from '../exports';\n\nexport const config = {\n name: 'humangl',\n priority: 999,\n canvas: null as null | AnyCanvas,\n gl: null as null | WebGL2RenderingContext,\n extensions: [] as string[] | null,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false, // default=true\n desynchronized: true, // default=undefined\n },\n};\n\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions();\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(instance: Human): void {\n // force backend reload if gl context is not valid\n if (instance.config.backend !== 'humangl') return;\n if ((config.name in tf.engine().registry) && !config?.gl?.getParameter(config.gl.VERSION)) {\n log('humangl error: backend invalid context');\n instance.models.reset();\n /*\n log('resetting humangl backend');\n await tf.removeBackend(config.name);\n await register(instance); // re-register\n */\n }\n if (!tf.findBackend(config.name)) {\n try {\n config.canvas = image.canvas(100, 100);\n } catch (err) {\n log('humangl error: cannot create canvas:', err);\n return;\n }\n try {\n config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n if (!config.gl) {\n log('humangl error: cannot get webgl context');\n return;\n }\n const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n if (!glv2) {\n log('backend override: using fallback webgl backend as webgl 2.0 is not detected');\n instance.config.backend = 'webgl';\n return;\n }\n if (config.canvas) {\n config.canvas.addEventListener('webglcontextlost', (e) => {\n log('humangl error:', e.type);\n log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n instance.emit('error');\n throw new Error('backend error: webgl context lost');\n });\n config.canvas.addEventListener('webglcontextrestored', (e) => {\n log('humangl error: context restored:', e);\n });\n config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n log('humangl error: context create:', e);\n });\n }\n } catch (err) {\n log('humangl error: cannot get webgl context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('humangl error: cannot set webgl context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n // @ts-ignore uncompatible kernelMs timing info\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('humangl error: cannot register webgl backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('humangl error: cannot update webgl backend registration:', err);\n return;\n }\n try {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n } catch (err) {\n log('humangl error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n const backend = tf.backend();\n const current = typeof backend['gpgpu'] !== 'undefined' ? backend['getGPGPUContext']().gl : null;\n if (current) {\n if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n } else {\n log('humangl error: no current gl context:', current, config.gl);\n }\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record = {\n tf255: 255.0,\n tf1: 1.0,\n tf2: 2.0,\n tf05: 0.5,\n tf127: 127.5,\n rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n constants.tf255 = tf.scalar(255.0, 'float32');\n constants.tf1 = tf.scalar(1.0, 'float32');\n constants.tf2 = tf.scalar(2.0, 'float32');\n constants.tf05 = tf.scalar(0.5, 'float32');\n constants.tf127 = tf.scalar(127.5, 'float32');\n constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/** TFJS backend initialization and customization */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human, Config, BackendEnum } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as constants from './constants';\nimport type { TensorInfo } from './types';\n\nexport async function getBestBackend(): Promise {\n await env.updateBackend(); // update env on backend init\n if (env.tensorflow?.version) return 'tensorflow';\n if (env.webgpu.supported && env.webgpu.backend) return 'webgpu';\n if (env.webgl.supported && env.webgl.backend) return 'webgl';\n if (env.wasm.supported && env.wasm.backend) return 'wasm';\n return 'cpu';\n}\n\nfunction registerCustomOps(config: Config) {\n const newKernels: string[] = [];\n if (!env.kernels.includes('mod')) {\n const kernelMod = {\n kernelName: 'Mod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('mod');\n newKernels.push('mod');\n }\n if (!env.kernels.includes('floormod')) {\n const kernelFloorMod = {\n kernelName: 'FloorMod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n };\n tf.registerKernel(kernelFloorMod);\n env.kernels.push('floormod');\n newKernels.push('floormod');\n }\n /*\n if (!env.kernels.includes('atan2') && config.softwareKernels) {\n const kernelAtan2 = {\n kernelName: 'Atan2',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu');\n const t = tf.atan2(op.inputs.a, op.inputs.b);\n tf.setBackend(backend);\n return t;\n }),\n };\n if (config.debug) log('registered kernel:', 'atan2');\n log('registered kernel:', 'atan2');\n tf.registerKernel(kernelAtan2);\n env.kernels.push('atan2');\n newKernels.push('atan2');\n }\n */\n if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n const kernelRotateWithOffset = {\n kernelName: 'RotateWithOffset',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu'); // eslint-disable-line @typescript-eslint/no-floating-promises\n const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n tf.setBackend(backend); // eslint-disable-line @typescript-eslint/no-floating-promises\n return t;\n }),\n };\n tf.registerKernel(kernelRotateWithOffset);\n env.kernels.push('rotatewithoffset');\n newKernels.push('rotatewithoffset');\n }\n if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record = {};\n\nexport async function check(instance: Human, force = false) {\n instance.state = 'backend';\n if (instance.config.backend?.length === 0) instance.config.backend = await getBestBackend();\n if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n const timeStamp = now();\n\n if (instance.config.backend && instance.config.backend.length > 0) {\n // detect web worker\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n if (instance.config.debug) log('running inside web worker');\n }\n\n if (typeof navigator !== 'undefined' && navigator?.userAgent?.toLowerCase().includes('electron')) {\n if (instance.config.debug) log('running inside electron');\n }\n\n // check available backends\n let available = Object.keys(tf.engine().registryFactory as Record);\n if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n humangl.register(instance);\n available = Object.keys(tf.engine().registryFactory as Record);\n }\n if (instance.config.debug) log('available backends:', available);\n\n // force browser vs node backend\n if (env.browser && !env.node && (instance.config.backend === 'tensorflow') && available.includes('webgl')) {\n if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n instance.config.backend = 'webgl';\n }\n if (env.node && !env.browser && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl') && available.includes('tensorflow')) {\n if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n instance.config.backend = 'tensorflow';\n }\n\n // handle webgpu\n if (env.browser && instance.config.backend === 'webgpu') {\n if (typeof navigator === 'undefined' || typeof navigator.gpu === 'undefined') {\n log('override: backend set to webgpu but browser does not support webgpu');\n instance.config.backend = 'webgl';\n } else {\n const adapter: GPUAdapter = await navigator.gpu.requestAdapter() as GPUAdapter;\n if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n if (!adapter) {\n log('override: backend set to webgpu but browser reports no available gpu');\n instance.config.backend = 'webgl';\n } else {\n let adapterInfo;\n // @ts-ignore gpu adapter info\n if ('requestAdapterInfo' in adapter) adapterInfo = await adapter?.requestAdapterInfo();\n // @ts-ignore gpu adapter info\n else adapterInfo = adapter.info;\n // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n log('webgpu adapter info:', adapterInfo);\n }\n }\n }\n\n if (!available.includes(instance.config.backend)) {\n log(`error: backend ${instance.config.backend} not found in registry`);\n instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n }\n\n if (instance.config.debug) log('setting backend:', [instance.config.backend]);\n\n // customize wasm\n if (instance.config.backend === 'wasm') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n if (typeof tf.setWasmPaths !== 'undefined') tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n let mt = false;\n let simd = false;\n try {\n mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n if (instance.config.debug) log(`wasm execution: ${simd ? 'simd' : 'no simd'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n } catch {\n log('wasm detection failed');\n }\n }\n\n try {\n await tf.setBackend(instance.config.backend);\n await tf.ready();\n } catch (err) {\n log('error: cannot set backend:', instance.config.backend, err);\n return false;\n }\n // @ts-ignore private property\n if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n }\n\n // customize humangl\n if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false \n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false \n // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV']) tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true \n // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false \n // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (instance.config.debug && typeof instance.config.deallocate !== 'undefined' && instance.config.deallocate) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n }\n\n // customize webgpu\n if (tf.getBackend() === 'webgpu') {\n // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n }\n\n if (instance.config.debug) {\n // @ts-ignore private property\n const newFlags = tf.env().flags;\n const updatedFlags = {};\n for (const key of Object.keys(newFlags)) {\n if (defaultFlags[key] === newFlags[key]) continue;\n updatedFlags[key] = newFlags[key];\n }\n if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n }\n\n if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n if (instance.config.debug) log('flags:', instance.config['flags']);\n for (const [key, val] of Object.entries(instance.config.flags)) {\n tf.env().set(key, val as number | boolean);\n }\n }\n\n tf.enableProdMode();\n constants.init();\n instance.performance.initBackend = Math.trunc(now() - timeStamp);\n instance.config.backend = tf.getBackend() as BackendEnum;\n await env.updateBackend(); // update env on backend init\n registerCustomOps(instance.config);\n // await env.updateBackend(); // update env on backend init\n // env.initial = false;\n }\n return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: string[], config) {\n // if (config.debug) log('registerKernel:', kernelNames);\n for (const kernelName of kernelNames) {\n const kernelConfig = {\n kernelName,\n backendName: config.backend,\n kernelFunc: (param): TensorInfo => {\n if (config.debug) log('kernelFunc', kernelName, config.backend, param);\n return param?.inputs?.info as TensorInfo;\n },\n // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n };\n tf.registerKernel(kernelConfig);\n }\n env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport { defaultLabels } from './labels';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\nimport type { Tensor2D } from '../tfjs/types';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\n/** draw processed canvas */\nexport function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n if (!input || !output) return;\n const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.drawImage(input, 0, 0);\n}\n\n/** draw processed canvas */\nexport async function tensor(input: Tensor2D, output: HTMLCanvasElement) {\n if (!input || !output) return;\n if (!env.browser) return;\n // const backend = tf.getBackend();\n // if (backend === 'webgpu') tf.browser.draw(input, output);\n // else await tf.browser.toPixels(input, output);\n await tf.browser.toPixels(input, output);\n // const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\n // if (!ctx) return;\n // const image = await process(input);\n // result.canvas = image.canvas;\n // human.tf.dispose(image.tensor);\n // ctx.drawImage(image.canvas, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial) {\n if (!result?.performance || !inCanvas) return null;\n const timeStamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n const promise = Promise.all([\n face(inCanvas, result.face, localOptions),\n body(inCanvas, result.body, localOptions),\n hand(inCanvas, result.hand, localOptions),\n object(inCanvas, result.object, localOptions),\n gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n // person(inCanvas, result.persons, localOptions); // already included above\n ]);\n drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n result.performance.draw = drawTime;\n return promise;\n}\n\n/** sets default label templates for face/body/hand/object/gestures */\nexport function init() {\n options.faceLabels = defaultLabels.face;\n options.bodyLabels = defaultLabels.body;\n options.bodyPartLabels = defaultLabels.bodyPart;\n options.handLabels = defaultLabels.hand;\n options.fingerLabels = defaultLabels.finger;\n options.objectLabels = defaultLabels.object;\n options.gestureLabels = defaultLabels.gesture;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n if (!input) log('draw error: invalid canvas');\n else if (!input.getContext) log('draw error: canvas context not defined');\n else {\n const ctx = input.getContext('2d', { willReadFrequently: true });\n if (!ctx) log('draw error: cannot get canvas context');\n else return ctx;\n }\n return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const replace = (str: string, source: string, target: string | number) => str.replace(source, typeof target === 'number' ? target.toFixed(1) : target);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function labels(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, str: string, startX: number, startY: number, localOptions: DrawOptions) {\n const line: string[] = str.replace(/\\[.*\\]/g, '').split('\\n').map((l) => l.trim()); // remove unmatched templates and split into array\n const x = Math.max(0, startX);\n for (let i = line.length - 1; i >= 0; i--) {\n const y = i * localOptions.lineHeight + startY;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(line[i], x + 5, y + 16);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(line[i], x + 4, y + 15);\n }\n}\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n ctx.beginPath();\n ctx.lineWidth = localOptions.lineWidth;\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.lineWidth = localOptions.lineWidth;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n let angle;\n let x;\n let y;\n ctx.beginPath();\n ctx.moveTo(from[0], from[1]);\n ctx.lineTo(to[0], to[1]);\n angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.moveTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n ctx.closePath();\n ctx.stroke();\n ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\n\nexport interface DrawOptions {\n /** draw line color */\n color: string,\n /** alpha value used for lines */\n alpha: number,\n /** label color */\n labelColor: string,\n /** label shadow color */\n shadowColor: string,\n /** label font */\n font: string,\n /** line spacing between labels */\n lineHeight: number,\n /** line width for drawn lines */\n lineWidth: number,\n /** size of drawn points */\n pointSize: number,\n /** draw rounded boxes by n pixels */\n roundRect: number,\n /** should points be drawn? */\n drawPoints: boolean,\n /** should labels be drawn? */\n drawLabels: boolean,\n /** should face attention keypoints be highlighted */\n drawAttention: boolean;\n /** should detected gestures be drawn? */\n drawGestures: boolean,\n /** should draw boxes around detection results? */\n drawBoxes: boolean,\n /** should draw polygons from detection points? */\n drawPolygons: boolean,\n /** should draw gaze arrows? */\n drawGaze: boolean,\n /** should fill polygons? */\n fillPolygons: boolean,\n /** use z-coordinate when available */\n useDepth: boolean,\n /** should lines be curved? */\n useCurves: boolean,\n /** string template for face labels */\n faceLabels: string,\n /** string template for body labels */\n bodyLabels: string,\n /** string template for body part labels */\n bodyPartLabels: string,\n /** string template for hand labels */\n handLabels: string,\n /** string template for hand labels */\n fingerLabels: string,\n /** string template for object labels */\n objectLabels: string,\n /** string template for gesture labels */\n gestureLabels: string,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n shadowColor: 'black' as string,\n alpha: 0.5 as number,\n font: 'small-caps 16px \"Segoe UI\"' as string,\n lineHeight: 18 as number,\n lineWidth: 4 as number,\n pointSize: 2 as number,\n roundRect: 8 as number,\n drawPoints: false as boolean,\n drawLabels: true as boolean,\n drawBoxes: true as boolean,\n drawAttention: true as boolean,\n drawGestures: true as boolean,\n drawPolygons: true as boolean,\n drawGaze: true as boolean,\n fillPolygons: false as boolean,\n useDepth: true as boolean,\n useCurves: false as boolean,\n faceLabels: '' as string,\n bodyLabels: '' as string,\n bodyPartLabels: '' as string,\n objectLabels: '' as string,\n handLabels: '' as string,\n fingerLabels: '' as string,\n gestureLabels: '' as string,\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n rightEyeIris: [473, 474, 475, 476, 477], // 5\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const meshLandmarks: Record = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]],\n};\n\nexport const blazeFaceLandmarks: Record = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nexport const irisIndices: { key: string, indices: number[] }[] = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: number[] = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: number[] = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: number[] = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: number[] = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: number[] = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: number[] = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const pairsLips: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n lips: connectionsToIndices(pairsLips),\n leftEye: connectionsToIndices(pairsLeftEye),\n leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n leftIris: connectionsToIndices(pairsLeftIris),\n rightEye: connectionsToIndices(pairsRightEye),\n rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n rightIris: connectionsToIndices(pairsRightIris),\n faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111],\n [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198],\n [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109],\n [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124],\n [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3],\n [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n lips: connectionsToIndices(LIPS_CONNECTIONS),\n leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n 61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n 185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n 78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n 191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n 76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n 184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n 62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n 183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n 130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n 247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n 226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n 113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n 35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n 143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n 156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n 359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n 467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n 446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n 342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n 265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n 372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n 383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n 468, // Center.\n 469, // Iris right edge.\n 470, // Iris top edge.\n 471, // Iris left edge.\n 472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n 473, // Center.\n 474, // Iris right edge.\n 475, // Iris top edge.\n 476, // Iris left edge.\n 477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow, labels, replace } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet localOptions: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (!localOptions.drawLabels || (localOptions.faceLabels?.length === 0)) return;\n let l = localOptions.faceLabels.slice();\n l = replace(l, '[id]', f.id.toFixed(0));\n if (f.score) l = replace(l, '[score]', 100 * f.score);\n if (f.gender) l = replace(l, '[gender]', f.gender);\n if (f.genderScore) l = replace(l, '[genderScore]', 100 * f.genderScore);\n if (f.age) l = replace(l, '[age]', f.age);\n if (f.distance) l = replace(l, '[distance]', 100 * f.distance);\n if (f.real) l = replace(l, '[real]', 100 * f.real);\n if (f.live) l = replace(l, '[live]', 100 * f.live);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n l = replace(l, '[emotions]', emotion.join(' '));\n }\n if (f.rotation?.angle?.roll) l = replace(l, '[roll]', rad2deg(f.rotation.angle.roll));\n if (f.rotation?.angle?.yaw) l = replace(l, '[yaw]', rad2deg(f.rotation.angle.yaw));\n if (f.rotation?.angle?.pitch) l = replace(l, '[pitch]', rad2deg(f.rotation.angle.pitch));\n if (f.rotation?.gaze?.bearing) l = replace(l, '[gaze]', rad2deg(f.rotation.gaze.bearing));\n labels(ctx, l, f.box[0], f.box[1], localOptions);\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n // iris: array[center, left, top, right, bottom]\n if (f.annotations?.leftEyeIris && f.annotations?.leftEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (f.annotations?.rightEyeIris && f.annotations?.rightEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n ctx.strokeStyle = 'pink';\n const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n const pathV = new Path2D(`\n M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n C\n ${valX} ${f.box[1]},\n ${valX} ${f.box[1] + f.box[3]},\n ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n `);\n const pathH = new Path2D(`\n M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n C \n ${f.box[0]} ${valY},\n ${f.box[0] + f.box[2]} ${valY},\n ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n `);\n ctx.stroke(pathH);\n ctx.stroke(pathV);\n }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.gaze.strength && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {\n ctx.strokeStyle = 'pink';\n ctx.fillStyle = 'pink';\n const leftGaze = [\n f.annotations.leftEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.leftEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);\n const rightGaze = [\n f.annotations.rightEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.rightEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);\n }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPolygons && f.mesh.length >= 468) {\n ctx.lineWidth = 1;\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n lines(ctx, points, localOptions);\n }\n drawIrisElipse(f, ctx);\n }\n /*\n if (localOptions.drawPolygons && f.contours.length > 1) {\n ctx.lineWidth = 5;\n lines(ctx, f.contours, opt);\n }\n ctx.lineWidth = 1;\n */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPoints) {\n if (f?.mesh.length >= 468) {\n for (let i = 0; i < f.mesh.length; i++) {\n point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n if (localOptions.drawAttention) {\n if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n }\n }\n } else {\n for (const [k, v] of Object.entries(f?.annotations || {})) {\n if (!v?.[0]) continue;\n const pt = v[0];\n point(ctx, pt[0], pt[1], 0, localOptions);\n if (localOptions.drawLabels) labels(ctx, k, pt[0], pt[1], localOptions);\n }\n }\n }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n if (localOptions.drawBoxes) {\n rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial) {\n localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n for (const f of result) {\n drawFaceBoxes(f, ctx);\n drawLabels(f, ctx);\n if (f.mesh && f.mesh.length > 0) {\n drawFacePoints(f, ctx);\n drawFacePolygons(f, ctx);\n drawGazeSpheres(f, ctx);\n drawGazeArrows(f, ctx);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.bodyLabels?.length > 0)) {\n let l = localOptions.bodyLabels.slice();\n l = replace(l, '[id]', result[i].id.toFixed(0));\n l = replace(l, '[score]', 100 * result[i].score);\n labels(ctx, l, result[i].box[0], result[i].box[1], localOptions);\n }\n }\n if (localOptions.drawPoints && result[i].keypoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels && (localOptions.bodyPartLabels?.length > 0) && result[i].keypoints) {\n ctx.font = localOptions.font;\n for (const pt of result[i].keypoints) {\n if (!pt.score || (pt.score === 0)) continue;\n let l = localOptions.bodyPartLabels.slice();\n l = replace(l, '[label]', pt.part);\n l = replace(l, '[score]', 100 * pt.score);\n labels(ctx, l, pt.position[0], pt.position[1], localOptions);\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n for (const part of Object.values(result[i].annotations)) {\n for (const connected of part) curves(ctx, connected, localOptions);\n }\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.handLabels?.length > 0)) {\n let l = localOptions.handLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = colorDepth(pt[2], localOptions);\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels && h.annotations && (localOptions.fingerLabels?.length > 0)) {\n for (const [part, pt] of Object.entries(h.annotations)) {\n let l = localOptions.fingerLabels.slice();\n l = replace(l, '[label]', part);\n labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions);\n }\n }\n if (localOptions.drawPolygons && h.annotations) {\n const addHandLine = (part: Point[]) => {\n if (!part || part.length === 0 || !part[0]) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n const z = part[i][2] || 0;\n ctx.strokeStyle = colorDepth(i * z, localOptions);\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations.index);\n addHandLine(h.annotations.middle);\n addHandLine(h.annotations.ring);\n addHandLine(h.annotations.pinky);\n addHandLine(h.annotations.thumb);\n // addPart(h.annotations.palm);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.objectLabels?.length > 0)) {\n let l = localOptions.objectLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (localOptions.drawGestures && (localOptions.gestureLabels?.length > 0)) {\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n const [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n let l = localOptions.gestureLabels.slice();\n l = replace(l, '[where]', where[0]);\n l = replace(l, '[who]', who);\n l = replace(l, '[what]', what[1]);\n labels(ctx, l, 8, 2 + (i * localOptions.lineHeight), localOptions);\n i += 1;\n }\n }\n }\n}\n", "export const defaultLabels = {\n face: `face\n confidence: [score]%\n [gender] [genderScore]%\n age: [age] years\n distance: [distance]cm\n real: [real]%\n live: [live]%\n [emotions]\n roll: [roll]\u00B0 yaw:[yaw]\u00B0 pitch:[pitch]\u00B0\n gaze: [gaze]\u00B0`,\n body: 'body [score]%',\n bodyPart: '[label] [score]%',\n object: '[label] [score]%',\n hand: '[label] [score]%',\n finger: '[label]',\n gesture: '[where] [who]: [what]',\n};\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: string[] = [\n 'nose', // 0\n 'leftEyeInside', // 1\n 'leftEye', // 2\n 'leftEyeOutside', // 3\n 'rightEyeInside', // 4\n 'rightEye', // 5\n 'rightEyeOutside', // 6\n 'leftEar', // 7\n 'rightEar', // 8\n 'leftMouth', // 9\n 'rightMouth', // 10\n 'leftShoulder', // 11\n 'rightShoulder', // 12\n 'leftElbow', // 13\n 'rightElbow', // 14\n 'leftWrist', // 15\n 'rightWrist', // 16\n 'leftPinky', // 17\n 'rightPinky', // 18\n 'leftIndex', // 19\n 'rightIndex', // 20\n 'leftThumb', // 21\n 'rightThumb', // 22\n 'leftHip', // 23\n 'rightHip', // 24\n 'leftKnee', // 25\n 'rightKnee', // 26\n 'leftAnkle', // 27\n 'rightAnkle', // 28\n 'leftHeel', // 29\n 'rightHeel', // 30\n 'leftFoot', // 31\n 'rightFoot', // 32\n 'bodyCenter', // 33\n 'bodyTop', // 34\n 'leftPalm', // 35 // z-coord not ok\n 'leftHand', // 36 // similar to wrist but z-coord not ok\n 'rightPalm', // 37 // z-coord not ok\n 'rightHand', // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record = {\n shoulders: ['leftShoulder', 'rightShoulder'],\n hips: ['rightHip', 'leftHip'],\n mouth: ['leftMouth', 'rightMouth'],\n leftLegUpper: ['leftHip', 'leftKnee'],\n leftLegLower: ['leftKnee', 'leftAnkle'],\n leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n leftTorso: ['leftShoulder', 'leftHip'],\n leftArmUpper: ['leftShoulder', 'leftElbow'],\n leftArmLower: ['leftElbow', 'leftWrist'],\n leftHand: ['leftWrist', 'leftPalm'],\n leftHandPinky: ['leftPalm', 'leftPinky'],\n leftHandIndex: ['leftPalm', 'leftIndex'],\n leftHandThumb: ['leftPalm', 'leftThumb'],\n leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n rightLegUpper: ['rightHip', 'rightKnee'],\n rightLegLower: ['rightKnee', 'rightAnkle'],\n rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n rightTorso: ['rightShoulder', 'rightHip'],\n rightArmUpper: ['rightShoulder', 'rightElbow'],\n rightArmLower: ['rightElbow', 'rightWrist'],\n rightHand: ['rightWrist', 'rightPalm'],\n rightHandPinky: ['rightPalm', 'rightPinky'],\n rightHandIndex: ['rightPalm', 'rightIndex'],\n rightHandThumb: ['rightPalm', 'rightThumb'],\n rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D } from '../tfjs/types';\n\nexport interface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nlet model: GraphModel | null;\nlet inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n const anchors: { x: number, y: number }[] = [];\n let layerId = 0;\n while (layerId < numLayers) {\n let anchorCount = 0;\n let lastSameStrideLayer = layerId;\n while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n anchorCount += 2;\n lastSameStrideLayer++;\n }\n const stride = strides[layerId];\n const featureMapHeight = Math.ceil(inputSize / stride);\n const featureMapWidth = Math.ceil(inputSize / stride);\n for (let y = 0; y < featureMapHeight; ++y) {\n for (let x = 0; x < featureMapWidth; ++x) {\n for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n }\n }\n }\n layerId = lastSameStrideLayer;\n }\n anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nexport async function loadDetector(config: Config): Promise {\n if (env.initial) model = null;\n if (!model && config.body['detector'] && config.body['detector'].modelPath || '') {\n model = await loadModel(config.body['detector'].modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug && model) log('cached model:', model['modelUrl']);\n createAnchors();\n return model as GraphModel;\n}\n\nconst cropFactor = [5.0, 5.0];\nexport function decodeBoxes(boxesTensor, anchor) {\n return tf.tidy(() => {\n const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n let xCenter = tf.squeeze(split[0]);\n let yCenter = tf.squeeze(split[1]);\n let width = tf.squeeze(split[2]);\n let height = tf.squeeze(split[3]);\n xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n const xMin = tf.sub(xCenter, tf.div(width, 2));\n const yMin = tf.sub(yCenter, tf.div(height, 2));\n const xMax = tf.add(xMin, width);\n const yMax = tf.add(yMin, height);\n const boxes = tf.stack([xMin, yMin, xMax, yMax], 1);\n return boxes;\n });\n}\n\nasync function decodeResults(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise {\n const detectedBoxes: DetectedBox[] = [];\n const t: Record = {};\n t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n t.scores = tf.sigmoid(logitsTensor);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, 1, config.body['detector']?.minConfidence || 0.1, config.body['detector']?.iouThreshold || 0.1);\n const nms = await t.nms.data();\n const scores = await t.scores.data();\n const boxes = await t.boxes.array();\n for (const i of Array.from(nms)) {\n const score = scores[i];\n const boxRaw: Box = boxes[i];\n const box: Box = [Math.round(boxRaw[0] * outputSize[0]), Math.round(boxRaw[1] * outputSize[1]), Math.round(boxRaw[2] * outputSize[0]), Math.round(boxRaw[3] * outputSize[1])];\n const detectedBox: DetectedBox = { score, boxRaw, box };\n detectedBoxes.push(detectedBox);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return detectedBoxes;\n}\n\nexport async function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n const t: Record = {};\n t.res = model?.execute(input, ['Identity']) as Tensor; //\n t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n t.logits = tf.squeeze(t.logitsRaw);\n t.boxes = tf.squeeze(t.boxesRaw);\n const boxes = await decodeResults(t.boxes, t.logits, config, outputSize);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function square(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n const dist = [box[2] * scaleFact, box[3] * scaleFact];\n const newBox: Box = [\n box[0] - (dist[0] - box[2]) / 2,\n box[1] - (dist[1] - box[3]) / 2,\n dist[0],\n dist[1],\n ];\n return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport { loadDetector, detectBoxes, DetectedBox } from './blazeposedetector';\nimport * as box from '../util/box';\nimport { env } from '../util/env';\n\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nlet model: GraphModel | null;\nlet inputSize = 256;\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n detector: [],\n};\n\nconst cache: BodyResult[] = [];\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport const loadDetect = (config: Config): Promise => loadDetector(config);\n\nexport async function loadPose(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.body.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nfunction prepareImage(input: Tensor4D, size: number, cropBox?: Box): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n let final: Tensor;\n if (cropBox) {\n t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n }\n if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n const height: [number, number] = [\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n ];\n const width: [number, number] = [\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n ];\n padding = [\n [0, 0], // dont touch batch\n height, // height before&after\n width, // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n t.resize = tf.image.resizeBilinear(t.pad as Tensor4D, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else if (input.shape[1] !== size) { // if input needs resizing\n t.resize = tf.image.resizeBilinear(t.cropped as Tensor4D || input, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else { // if input is already in a correct resolution just normalize it\n final = tf.div(t.cropped || input, constants.tf255);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nfunction rescaleKeypoints(keypoints: BodyKeypoint[], outputSize: [number, number], cropBox?: Box): BodyKeypoint[] {\n for (const kpt of keypoints) { // first rescale due to padding\n kpt.position = [\n Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n kpt.position[2] as number,\n ];\n kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n }\n if (cropBox) { // second rescale due to cropping\n const width = cropBox[2] - cropBox[0];\n const height = cropBox[3] - cropBox[1];\n for (const kpt of keypoints) {\n kpt.positionRaw = [\n kpt.positionRaw[0] / height + cropBox[1], // correct offset due to crop\n kpt.positionRaw[1] / width + cropBox[0], // correct offset due to crop\n kpt.positionRaw[2] as number,\n ];\n kpt.position = [\n Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n kpt.positionRaw[2] as number,\n ];\n }\n }\n return keypoints;\n}\n\nfunction fixKeypoints(keypoints: BodyKeypoint[]) {\n // palm z-coord is incorrect around near-zero so we approximate it\n const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise {\n /**\n * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n * t.segmentation:\n * t.heatmap:\n * t.world: 39 keypoints [x,y,z] normalized to -1..1\n * t.poseflag: body score\n */\n if (!model?.['executor']) return null;\n const t: Record = {};\n [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = model?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n const poseScore = (await t.poseflag.data())[0];\n const points = await t.ld.data();\n const distances = await t.world.data();\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n const keypointsRelative: BodyKeypoint[] = [];\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n const score = sigmoid(points[depth * i + 3]);\n const presence = sigmoid(points[depth * i + 4]);\n const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n const positionRaw: Point = [points[depth * i + 0] / inputSize, points[depth * i + 1] / inputSize, points[depth * i + 2] + 0];\n const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n }\n if (poseScore < (config.body.minConfidence || 0)) return null;\n fixKeypoints(keypointsRelative);\n const keypoints: BodyKeypoint[] = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n const kpts = keypoints.map((k) => k.position);\n const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n return body;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n skipped++;\n } else {\n let boxes: DetectedBox[] = [];\n if (config.body?.['detector']?.['enabled']) {\n const preparedImage = prepareImage(input, 224);\n boxes = await detectBoxes(preparedImage, config, outputSize);\n tf.dispose(preparedImage);\n } else {\n boxes = [{ box: [0, 0, 0, 0] as Box, boxRaw: [0, 0, 1, 1], score: 0 }]; // running without detector\n }\n for (let i = 0; i < boxes.length; i++) {\n const preparedBox = prepareImage(input, 256, boxes[i]?.boxRaw); // padded and resized\n cache.length = 0;\n const bodyResult = await detectLandmarks(preparedBox, config, outputSize);\n tf.dispose(preparedBox);\n if (!bodyResult) continue;\n bodyResult.id = i;\n // bodyResult.score = 0; // TBD\n cache.push(bodyResult);\n }\n /*\n cropBox = [0, 0, 1, 1]; // reset crop coordinates\n if (cache?.boxRaw && config.skipAllowed) {\n const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n size = (size * 1.0) / 2; // enlarge and half it\n if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n const y = 0; // cy - size;\n const x = cx - size;\n cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n }\n }\n */\n lastTime = now();\n skipped = 0;\n }\n return cache;\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n // fakeOps(['floormod'], config);\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n if (!res) return [];\n const t: Record = {};\n const results: ObjectResult[] = [];\n const detections = await res.array() as number[][][];\n t.squeeze = tf.squeeze(res);\n const arr = tf.split(t.squeeze, 6, 1); // x1, y1, x2, y2, score, class\n t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n t.boxes = tf.squeeze(t.stack);\n t.scores = tf.squeeze(arr[4]);\n t.classes = tf.squeeze(arr[5]);\n tf.dispose([res, ...arr]);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, config.object.maxDetected || 0, config.object.iouThreshold, (config.object.minConfidence || 0));\n const nms = await t.nms.data();\n let i = 0;\n for (const id of Array.from(nms)) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n if (Number.isNaN(classVal)) continue;\n const label = labels[classVal].label as ObjectType;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw: Box = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ];\n const box: Box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return results;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n lastTime = now();\n tf.dispose(resize);\n\n const obj = await process(objectT, outputSize, config);\n last = obj;\n\n resolve(obj);\n });\n}\n", "export const kpt: string[] = [\n 'head',\n 'neck',\n 'rightShoulder',\n 'rightElbow',\n 'rightWrist',\n 'chest',\n 'leftShoulder',\n 'leftElbow',\n 'leftWrist',\n 'bodyCenter',\n 'rightHip',\n 'rightKnee',\n 'rightAnkle',\n 'leftHip',\n 'leftKnee',\n 'leftAnkle',\n];\n\nexport const connected: Record = {\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record };\n\n// const keypoints: Array = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore): Promise<[number, number, number]> {\n const [width, height] = inputs.shape;\n const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n const max = tf.max(reshaped, 0);\n const newScore: number = (await max.data())[0]; // get highest score\n if (newScore > minScore) { // skip coordinate calculation is score is too low\n const coordinates = tf.argMax(reshaped, 0);\n const mod = tf.mod(coordinates, width);\n const x = (await mod.data())[0];\n const div = tf.div(coordinates, width);\n const y: number = (await div.data())[0];\n tf.dispose([reshaped, max, coordinates, mod, div]);\n return [x, y, newScore];\n }\n tf.dispose([reshaped, max]);\n return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs[0].shape) return [];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n skipped++;\n return [cache];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape?.[2] || 0, model?.inputs[0].shape?.[1] || 0], false);\n const enhance = tf.mul(resize, constants.tf2);\n const norm = tf.sub(enhance, constants.tf1);\n return norm;\n });\n let resT;\n if (config.body.enabled) resT = model?.execute(tensor);\n lastTime = now();\n tf.dispose(tensor);\n\n if (resT) {\n cache.keypoints.length = 0;\n const squeeze = tf.squeeze(resT);\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = tf.unstack(squeeze, 2);\n tf.dispose(squeeze);\n\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n if (partScore > (config.body.minConfidence || 0)) {\n cache.keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = cache.keypoints.map((a) => a.position[0]);\n const y = cache.keypoints.map((a) => a.position[1]);\n cache.box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n cache.boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n cache.annotations[name] = pt;\n }\n resolve([cache]);\n });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n Math.trunc(Math.max(0, box.startPoint[0])),\n Math.trunc(Math.max(0, box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n box.startPoint[0] / (input.shape[2] || 0),\n box.startPoint[1] / (input.shape[1] || 0),\n (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor, anchor) => {\n const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n // const centerPoint = [(startPoint[0] + endPoint[0]) / 2, (startPoint[1] + endPoint[1]) / 2];\n const landmarks = box.landmarks.map((pt) => [(pt[0] + anchor[0]) * factor[0], (pt[1] + anchor[1]) * factor[1]]);\n /**\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0]) / 2) + (pt[0] * input.shape[2] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1]) / 2) + (pt[1] * input.shape[1] / blazeface.size()),\n ]);\n */\n\n return { startPoint, endPoint, landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n const h = image.shape[1];\n const w = image.shape[2];\n const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n const norm = tf.div(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n return {\n startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point,\n endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size,\n };\n};\n\nexport const squarifyBox = (box) => {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize = Math.max(...size) / 2;\n return {\n startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point,\n endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size: [Math.round(size[0]), Math.round(size[1])] as [number, number],\n };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n const x = landmarks.map((d) => d[0]);\n const y = landmarks.map((d) => d[1]);\n return {\n startPoint: [Math.min(...x), Math.min(...y)] as Point,\n endPoint: [Math.max(...x), Math.max(...y)] as Point,\n landmarks,\n };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n let product = 0;\n for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize: number) {\n const spec = inputSize === 192\n ? { strides: [4], anchors: [1] } // facemesh-detector\n : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n const anchors: [number, number][] = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n }\n }\n }\n return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n const boxSize = getBoxSize(box);\n const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n (coord[2] || 0),\n ]));\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n const boxCenter = getBoxCenter(box);\n const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n return coordsRotated.map((coord) => ([\n Math.trunc(coord[0] + offsets[0]),\n Math.trunc(coord[1] + offsets[1]),\n Math.trunc(coord[2] || 0),\n ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n ? coords.meshLandmarks.symmetryLine\n : coords.blazeFaceLandmarks.symmetryLine;\n let angle = 0; // default\n let rotationMatrix = fixedRotationMatrix; // default\n let face; // default\n\n if (rotate && env.kernels.includes('rotatewithoffset')) {\n angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n if (largeAngle) { // perform rotation only if angle is sufficiently high\n const center: Point = getBoxCenter(box);\n const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n const rotated = tf.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);\n rotationMatrix = buildRotationMatrix(-angle, center);\n face = cutAndResize(box, rotated, [inputSize, inputSize]);\n tf.dispose(rotated);\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n const x = mesh.map((m) => m[0]);\n const y = mesh.map((m) => m[1]);\n // weighted center\n /*\n const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n return [sum(x) / mesh.length, sum(y) / mesh.length];\n */\n // absolute center\n return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n const center = findFaceCenter(mesh);\n const boxSize = getBoxSize(previousBox);\n const calculatedBox = {\n startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n };\n return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\nexport interface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number, size: [number, number] }\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.detector?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n if (!anchors || !inputSizeT) return tf.zeros([0, 0]);\n const t: Record = {};\n t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n t.centers = tf.add(t.boxStarts, anchors);\n t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n t.centersNormalized = tf.div(t.centers, inputSizeT);\n t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n t.startNormalized = tf.mul(t.starts, inputSizeT);\n t.endNormalized = tf.mul(t.ends, inputSizeT);\n const boxes = tf.concat2d([t.startNormalized as Tensor2D, t.endNormalized as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor4D, config: Config): Promise {\n // sanity check on input\n if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n const t: Record = {};\n let pad = [0, 0];\n let scale = [1, 1];\n if (config?.face?.detector?.square) {\n const xy = Math.max(inputImage.shape[2], inputImage.shape[1]);\n pad = [Math.floor((xy - inputImage.shape[2]) / 2), Math.floor((xy - inputImage.shape[1]) / 2)];\n t.padded = tf.pad(inputImage, [[0, 0], [pad[1], pad[1]], [pad[0], pad[0]], [0, 0]]);\n scale = [inputImage.shape[2] / xy, inputImage.shape[1] / xy];\n pad = [pad[0] / inputSize, pad[1] / inputSize];\n } else {\n t.padded = inputImage.clone();\n }\n t.resized = tf.image.resizeBilinear(t.padded as Tensor4D, [inputSize, inputSize]);\n t.div = tf.div(t.resized, constants.tf127);\n t.normalized = tf.sub(t.div, constants.tf1);\n const res = model?.execute(t.normalized) as Tensor[];\n if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n const sorted = res.sort((a, b) => a.size - b.size);\n t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n t.concat = tf.concat([t.concat512, t.concat384], 1);\n t.batch = tf.squeeze(t.concat, [0]);\n } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n t.batch = tf.squeeze(res[0]);\n } else { // original blazeface tfhub model\n t.batch = tf.squeeze(res);\n }\n tf.dispose(res);\n t.boxes = decodeBoxes(t.batch);\n t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.logits);\n t.scores = tf.squeeze(t.sigmoid);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n const nms = await t.nms.array() as number[];\n const boxes: DetectBox[] = [];\n const scores = await t.scores.data();\n for (let i = 0; i < nms.length; i++) {\n const confidence = scores[nms[i]];\n if (confidence > (config.face.detector?.minConfidence || 0)) {\n const b: Record = {};\n b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n b.squeeze = tf.squeeze(b.slice);\n b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n const points = await b.bbox.data();\n const unpadded = [ // TODO fix this math\n points[0] * scale[0] - pad[0],\n points[1] * scale[1] - pad[1],\n points[2] * scale[0] - pad[0],\n points[3] * scale[1] - pad[1],\n ];\n const rawBox = {\n startPoint: [unpadded[0], unpadded[1]] as Point,\n endPoint: [unpadded[2], unpadded[3]] as Point,\n landmarks: (await b.landmarks.array()) as Point[],\n confidence,\n };\n b.anchor = tf.slice(anchors as Tensor, [nms[i], 0], [1, 2]);\n const anchor = await b.anchor.data();\n const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize], anchor);\n const enlargedBox = util.enlargeBox(scaledBox, config.face.detector?.scale || 1.4);\n const squaredBox = util.squarifyBox(enlargedBox);\n if (squaredBox.size[0] > (config.face.detector?.['minSize'] || 0) && squaredBox.size[1] > (config.face.detector?.['minSize'] || 0)) boxes.push(squaredBox);\n Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst leftOutline = coords.meshAnnotations.leftEyeLower0;\nconst rightOutline = coords.meshAnnotations.rightEyeLower0;\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.iris?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize === -1) inputSize = 64;\n return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.irisIndices.length; i++) {\n const { key, indices } = coords.irisIndices[i];\n const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0],\n newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false, scale = 2.3) => {\n const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), scale));\n const boxSize = util.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / meshSize,\n box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n box.endPoint[0] / meshSize,\n ]], [0], [inputSize, inputSize]);\n if (flip && env.kernels.includes('flipleftright')) {\n const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n tf.dispose(crop);\n crop = flipped;\n }\n return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n const eyeRawCoords: Point[] = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n};\n\nexport async function augmentIris(rawCoords, face, meshSize, config: Config) {\n if (!model?.['executor']) return rawCoords;\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true, config.face.iris?.scale || 2.3);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true, config.face.iris?.scale || 2.3);\n const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n tf.dispose(leftEyeCrop);\n tf.dispose(rightEyeCrop);\n const eyePredictions = model.execute(combined) as Tensor;\n tf.dispose(combined);\n const eyePredictionsData = await eyePredictions.data();\n tf.dispose(eyePredictions);\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n return newCoords;\n}\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n const t: Record = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n // flag: results[1], // already processed in parent // conv_faceflag\n lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n };\n for (const val of Object.values(t)) {\n if (!val) return rawCoords; // could not find tensor\n }\n\n // augment iris: adds additional 5 keypoints per eye\n const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n // augment eyes: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n // augment lips: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\nimport type { DetectBox } from './blazeface';\n\nconst cache = {\n boxes: [] as DetectBox[],\n skipped: Number.MAX_SAFE_INTEGER,\n timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n // reset cached boxes\n const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n cache.timestamp = now();\n cache.skipped = 0;\n } else {\n cache.skipped++;\n }\n const faces: FaceResult[] = [];\n const newCache: DetectBox[] = [];\n let id = 0;\n const size = inputSize;\n for (let i = 0; i < cache.boxes.length; i++) {\n const box = cache.boxes[i];\n let angle = 0;\n let rotationMatrix;\n const face: FaceResult = { // init face result\n id: id++,\n mesh: [],\n meshRaw: [],\n box: [0, 0, 0, 0],\n boxRaw: [0, 0, 0, 0],\n score: 0,\n boxScore: 0,\n faceScore: 0,\n size: [0, 0],\n // contoursRaw: [],\n // contours: [],\n annotations: {} as Record,\n };\n\n // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n if (config.filter.equalization) {\n const equilized = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n tf.dispose(face.tensor);\n if (equilized) face.tensor = equilized;\n }\n face.boxScore = Math.round(100 * box.confidence) / 100;\n if (!config.face.mesh?.enabled || !model?.['executor']) { // mesh not enabled or not loaded, return resuts from detector only\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.size = box.size;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n } else if (!model) { // mesh enabled, but not loaded\n if (config.debug) log('face mesh detection requested, but model is not loaded');\n } else { // mesh enabled\n if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n config.face.attention.enabled = false;\n tf.dispose(face.tensor);\n return faces;\n }\n const results = model.execute(face.tensor as Tensor) as Tensor[];\n const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n const faceConfidence = await confidenceT.data();\n face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n box.confidence = face.faceScore; // reset confidence of cached box\n if (config.face.mesh['keepInvalid']) {\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.size = box.size;\n face.score = face.boxScore;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n }\n } else {\n const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n let rawCoords = await coordsReshaped.array();\n tf.dispose(coordsReshaped);\n if (config.face.attention?.enabled) {\n rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n } else if (config.face.iris?.enabled) {\n rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize, config); // run iris model and augment results\n }\n face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n face.score = face.faceScore;\n const calculatedBox = {\n ...util.calculateFaceBox(face.mesh, box),\n confidence: box.confidence,\n landmarks: box.landmarks,\n size: box.size,\n };\n face.box = util.clampBox(calculatedBox, input);\n face.boxRaw = util.getRawBox(calculatedBox, input);\n face.size = calculatedBox.size;\n /*\n const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n face.contoursRaw = [];\n for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n */\n newCache.push(calculatedBox);\n }\n tf.dispose(results);\n }\n if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n else tf.dispose(face.tensor);\n }\n cache.boxes = newCache; // reset cache\n return faces;\n}\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (config.face.attention?.enabled && model?.['signature']) {\n if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n }\n if (!model) {\n if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n else model = await loadModel(config.face.mesh?.modelPath);\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nlet annotations: string[] = [];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet rgb = false;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.face.emotion?.modelPath);\n rgb = model?.inputs?.[0].shape?.[3] === 3;\n if (!rgb) annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']; // oarriaga and gear\n else annotations = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']; // affectnet\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ score: number, emotion: Emotion }[]> {\n if (!model) return [];\n const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj: { score: number, emotion: Emotion }[] = [];\n if (config.face.emotion?.enabled) {\n const t: Record = {};\n const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n if (config.face.emotion?.['crop'] > 0) { // optional crop\n const crop = config.face.emotion?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n }\n if (rgb) {\n t.mul = tf.mul(t.resize, 255);\n t.normalize = tf.sub(t.mul, [103.939, 116.779, 123.68]); // affectnet uses specific norm values\n t.emotion = model?.execute(t.normalize) as Tensor; // result is already in range 0..1, no need for additional activation\n } else {\n // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n // t.redNorm = tf.mul(t.red, rgb[0]);\n // t.greenNorm = tf.mul(t.green, rgb[1]);\n // t.blueNorm = tf.mul(t.blue, rgb[2]);\n // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n t.channels = tf.mul(t.resize, constants.rgb);\n t.grayscale = tf.sum(t.channels, 3, true);\n t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n }\n lastTime = now();\n const data = await t.emotion.data();\n for (let i = 0; i < data.length; i++) {\n if (data[i] > (config.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n }\n obj.sort((a, b) => b.score - a.score);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face similarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel, Tensor4D, Tensor1D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.description?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport function enhance(input, config: Config): Tensor {\n const tensor = (input.image || input.tensor || input) as Tensor4D; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n let crop: Tensor;\n if (config.face.description?.['crop'] > 0) { // optional crop\n const cropval = config.face.description?.['crop'];\n const box = [[cropval, cropval, 1 - cropval, 1 - cropval]];\n crop = tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n const norm: Tensor = tf.mul(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n /*\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n */\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n const obj: FaceRes = {\n age: 0 as number,\n gender: 'unknown' as Gender,\n genderScore: 0 as number,\n descriptor: [] as number[],\n };\n if (!model?.['executor']) return obj;\n const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && (last?.[idx]?.age > 0) && (last?.[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (config.face.description?.enabled) {\n const enhanced = enhance(image, config);\n const resT = model?.execute(enhanced) as Tensor[];\n lastTime = now();\n tf.dispose(enhanced);\n const genderT = resT.find((t) => t.shape[1] === 1) as Tensor;\n const gender = await genderT.data();\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > (config.face.description.minConfidence || 0)) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100) as Tensor1D, 1);\n const ageIdx: number = (await argmax.data())[0];\n tf.dispose(argmax);\n const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n const all = await ageT.data();\n obj.age = Math.round(all[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n const descriptor = desc ? await desc.data() : [] as number[];\n obj.descriptor = Array.from(descriptor);\n resT.forEach((t) => tf.dispose(t));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\n// import * as tf from 'dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: { x: number, y: number }[]): boolean {\n let inside = false;\n let j = polygon.length - 1;\n for (let i = 0; i < polygon.length; j = i++) {\n if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n }\n return inside;\n}\n\nexport async function mask(face: FaceResult): Promise {\n if (!face.tensor) return face.tensor;\n if (!face.mesh || face.mesh.length < 100) return face.tensor;\n const width = face.tensor.shape[2] || 0;\n const height = face.tensor.shape[1] || 0;\n const buffer = await face.tensor.buffer();\n let silhouette: { x: number, y: number }[] = [];\n for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n for (let x = 0; x < width; x++) {\n for (let y = 0; y < height; y++) {\n const inside = insidePoly(x / width, y / width, silhouette);\n if (!inside) {\n buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n }\n }\n }\n const output = buffer.toTensor();\n // tf.dispose(buffer);\n return output;\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.liveness?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: GearType[] = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.gear?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n const skipFrame = skipped < (config.face.gear?.skipFrames || 0);\n const skipTime = (config.face.gear?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n let box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n if (config.face.gear?.['crop'] > 0) { // optional crop config value\n const crop = config.face.gear?.['crop'];\n box = [[crop, crop, 1 - crop, 1 - crop]];\n }\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n if (config.face.gear?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n const gender = await t.gender.data();\n obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n const race = await t.race.data();\n for (let i = 0; i < race.length; i++) {\n if (race[i] > (config.face.gear?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n }\n obj.race.sort((a, b) => b.score - a.score);\n // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n const ageDistribution = Array.from(await t.age.data());\n const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n let age = ageSorted[0][0]; // pick best starting point\n for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n obj.age = Math.round(10 * age) / 10;\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ age: number }> {\n if (!model) return { age: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.mul(t.resize, constants.tf255);\n const obj = { age: 0 };\n if (config.face['ssrnet']?.enabled) t.age = model.execute(t.enhance) as Tensor;\n if (t.age) {\n const data = await t.age.data();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { gender: Gender, genderScore: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet']?.modelPathGender);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n if (!model) return { gender: 'unknown', genderScore: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.tidy(() => {\n let normalize: Tensor;\n if (model?.inputs?.[0].shape?.[3] === 1) {\n const [red, green, blue] = tf.split(t.resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n } else {\n normalize = tf.mul(tf.sub(t.resize, constants.tf05), 2); // range rgb:-1..1\n }\n return normalize;\n });\n const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n if (config.face['ssrnet']?.enabled) t.gender = model.execute(t.enhance) as Tensor;\n const data = await t.gender.data();\n obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * MobileFaceNet model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['mobilefacenet']?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n const skipTime = (config.face['mobilefacenet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['mobilefacenet']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n /*\n // optional normalize outputs with l2 normalization\n const scaled = tf.tidy(() => {\n const l2 = res.norm('euclidean');\n const scale = res.div(l2);\n return scale;\n });\n\n // optional reduce feature vector complexity\n const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n */\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['insightface'].modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n const skipTime = (config.face['insightface']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['insightface']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations.rightEyeIris || !face.annotations.leftEyeIris) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v: Vector): Vector => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\n let thetaX: number;\n let thetaY: number;\n let thetaZ: number;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n // thetaZ = Math.asin(r10);\n const cosThetaZ = Math.sqrt(r00 * r00 + r20 * r20); // \n thetaZ = Math.atan2(r10, cosThetaZ);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n if (Number.isNaN(thetaX)) thetaX = 0;\n if (Number.isNaN(thetaY)) thetaY = 0;\n if (Number.isNaN(thetaZ)) thetaZ = 0;\n // return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n return { pitch: -thetaX, yaw: -thetaY, roll: -thetaZ };\n };\n /*\n const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n };\n };\n */\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n const yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n const zAxis = normalize(crossVectors(xAxis, yAxis));\n // adjust xAxis to make sure that all axes are perpendicular to each other\n xAxis = crossVectors(yAxis, zAxis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n xAxis[0], xAxis[1], xAxis[2],\n yAxis[0], yAxis[1], yAxis[2],\n zAxis[0], zAxis[1], zAxis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n", "import type { FaceResult } from '../result';\n\nexport function calculateCameraDistance(face: FaceResult, width: number): number {\n // iris points are [center, left, top, right, bottom]\n // average size of human iris is 11.7mm - fairly constant for all ages/genders/races\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // distance of eye from camera in meters\n const cameraDistance = Math.round(1.17 / irisSize) / 100;\n return cameraDistance;\n}\n\nexport function calculateEyesDistance(face: FaceResult, width: number): number {\n // average distance between eyes is 65mm - fairly constant for typical adult male, but varies otherwise\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // pixel x and y distance of centers of left and right iris, you can use edges instead\n const irisDistanceXY = [f.leftEyeIris[0][0] - f.rightEyeIris[0][0], f.leftEyeIris[0][1] - f.rightEyeIris[0][1]];\n // absolute distance bewtween eyes in 0..1 range to account for head pitch (we can ignore yaw)\n const irisDistance = Math.sqrt((irisDistanceXY[0] * irisDistanceXY[0]) + (irisDistanceXY[1] * irisDistanceXY[1])) / width;\n // distance between eyes in meters\n const eyesDistance = Math.round(1.17 * irisDistance / irisSize) / 100;\n return eyesDistance;\n}\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor4D } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\nimport { calculateCameraDistance } from './anthropometry';\n\ninterface DescRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor4D): Promise => {\n // run facemesh, includes blazeface and iris\n let timeStamp: number = now();\n let ageRes: { age: number } | Promise<{ age: number }> | null;\n let gearRes: gear.GearType | Promise | null;\n let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n let mobilefacenetRes: number[] | Promise | null;\n let insightfaceRes: number[] | Promise | null;\n let antispoofRes: number | Promise | null;\n let livenessRes: number | Promise | null;\n let descRes: DescRes | Promise | null;\n\n const faceRes: FaceResult[] = [];\n instance.state = 'run:face';\n const faces: FaceResult[] = await facemesh.predict(input, instance.config);\n instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n instance.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefied\n if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n // optional face mask\n if (instance.config.face.detector?.mask) {\n const masked = await mask.mask(faces[i]);\n tf.dispose(faces[i].tensor);\n if (masked) faces[i].tensor = masked;\n }\n\n // calculate face angles\n const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Emotion:');\n if (instance.config.async) {\n emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n } else {\n instance.state = 'run:emotion';\n timeStamp = now();\n emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Emotion:');\n\n // run antispoof, inherits face from blazeface\n instance.analyze('Start AntiSpoof:');\n if (instance.config.async) {\n antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:antispoof';\n timeStamp = now();\n antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End AntiSpoof:');\n\n // run liveness, inherits face from blazeface\n instance.analyze('Start Liveness:');\n if (instance.config.async) {\n livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:liveness';\n timeStamp = now();\n livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Liveness:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start GEAR:');\n if (instance.config.async) {\n gearRes = instance.config.face.gear?.enabled ? gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:gear';\n timeStamp = now();\n gearRes = instance.config.face.gear?.enabled ? await gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.gear = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End GEAR:');\n\n // run ssrnet, inherits face from blazeface\n instance.analyze('Start SSRNet:');\n if (instance.config.async) {\n ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:ssrnet';\n timeStamp = now();\n ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End SSRNet:');\n\n // run mobilefacenet alternative, inherits face from blazeface\n instance.analyze('Start MobileFaceNet:');\n if (instance.config.async) {\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End MobileFaceNet:');\n\n // run insightface alternative, inherits face from blazeface\n instance.analyze('Start InsightFace:');\n if (instance.config.async) {\n insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End InsightFace:');\n\n // run faceres, inherits face from blazeface\n instance.analyze('Start Description:');\n if (instance.config.async) {\n descRes = faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n } else {\n instance.state = 'run:description';\n timeStamp = now();\n descRes = await faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Description:');\n\n // if async wait for results\n if (instance.config.async) {\n [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);\n }\n instance.analyze('Finish Face:');\n\n if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n descRes = {\n ...(descRes as DescRes),\n age: (ageRes as { age: number}).age,\n gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n };\n }\n if (instance.config.face.gear?.enabled && gearRes) { // override age/gender/race if gear model is used\n descRes = {\n ...(descRes as DescRes),\n age: (gearRes as gear.GearType).age,\n gender: (gearRes as gear.GearType).gender,\n genderScore: (gearRes as gear.GearType).genderScore,\n race: (gearRes as gear.GearType).race,\n };\n }\n if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if mobilefacenet model is used\n (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n }\n\n if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n (descRes as DescRes).descriptor = insightfaceRes as number[];\n }\n\n const irisSize = instance.config.face.iris?.enabled ? calculateCameraDistance(faces[i], input.shape[2]) : 0;\n\n // optionally return tensor\n const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor as Tensor4D) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n const res: FaceResult = {\n ...faces[i],\n id: i,\n };\n if ((descRes as DescRes).age) res.age = (descRes as DescRes).age;\n if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n if (antispoofRes) res.real = antispoofRes as number;\n if (livenessRes) res.live = livenessRes as number;\n if (irisSize > 0) res.distance = irisSize;\n if (rotation) res.rotation = rotation;\n if (tensor) res.tensor = tensor;\n faceRes.push(res);\n instance.analyze('End Face');\n }\n instance.analyze('End FaceMesh:');\n if (instance.config.async) {\n if (instance.performance.face) delete instance.performance.face;\n if (instance.performance.age) delete instance.performance.age;\n if (instance.performance.gender) delete instance.performance.gender;\n if (instance.performance.emotion) delete instance.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n thumb: 0,\n index: 1,\n middle: 2,\n ring: 3,\n pinky: 4,\n all: [0, 1, 2, 3, 4], // just for convenience\n nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n // Describes mapping of joints based on the 21 points returned by handpose.\n // [0] Palm\n // [1-4] Thumb\n // [5-8] Index\n // [9-12] Middle\n // [13-16] Ring\n // [17-20] Pinky\n pointsMapping: {\n 0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n 1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n 2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n 3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n 4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n },\n getName: (value) => Finger.nameMapping[value],\n getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n none: 0,\n half: 1,\n full: 2,\n nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n verticalUp: 0,\n verticalDown: 1,\n horizontalLeft: 2,\n horizontalRight: 3,\n diagonalUpRight: 4,\n diagonalUpLeft: 5,\n diagonalDownRight: 6,\n diagonalDownLeft: 7,\n nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n name;\n curls;\n directions;\n weights;\n weightsRelative;\n\n constructor(name) {\n // name (should be unique)\n this.name = name;\n this.curls = {};\n this.directions = {};\n this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n }\n\n curl(finger, curl, confidence) {\n if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n this.curls[finger].push([curl, confidence]);\n }\n\n direction(finger, position, confidence) {\n if (!this.directions[finger]) this.directions[finger] = [];\n this.directions[finger].push([position, confidence]);\n }\n\n weight(finger, weight) {\n this.weights[finger] = weight;\n // recalculate relative weights\n const total = this.weights.reduce((a, b) => a + b, 0);\n this.weightsRelative = this.weights.map((el) => el * 5 / total);\n }\n\n matchAgainst(detectedCurls, detectedDirections) {\n let confidence = 0.0;\n // look at the detected curl of each finger and compare with\n // the expected curl of this finger inside current gesture\n for (const fingerIdx in detectedCurls) {\n const detectedCurl = detectedCurls[fingerIdx];\n const expectedCurls = this.curls[fingerIdx];\n if (typeof expectedCurls === 'undefined') {\n // no curl description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible curl of this specific finger\n for (const [expectedCurl, score] of expectedCurls) {\n if (detectedCurl === expectedCurl) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n // same for detected direction of each finger\n for (const fingerIdx in detectedDirections) {\n const detectedDirection = detectedDirections[fingerIdx];\n const expectedDirections = this.directions[fingerIdx];\n if (typeof expectedDirections === 'undefined') {\n // no direction description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible direction of this specific finger\n for (const [expectedDirection, score] of expectedDirections) {\n if (detectedDirection === expectedDirection) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n return confidence / 10;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n ThumbsUp.curl(finger, full, 1.0);\n ThumbsUp.direction(finger, horizontalLeft, 1.0);\n ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\n/* eslint-disable camelcase */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n // curl estimation\n HALF_CURL_START_LIMIT: 60.0,\n NO_CURL_START_LIMIT: 130.0,\n // direction estimation\n DISTANCE_VOTE_POWER: 1.1,\n SINGLE_ANGLE_VOTE_POWER: 0.9,\n TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n const value = (point1y - point2y) / (point1x - point2x);\n let slope = Math.atan(value) * 180 / Math.PI;\n if (slope <= 0) slope = -slope;\n else if (slope > 0) slope = 180 - slope;\n return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n if (!point1 || !point2) return [0, 0];\n const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n if (point1.length === 2) return slopeXY;\n const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n let isVertical = 0;\n let isDiagonal = 0;\n let isHorizontal = 0;\n if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n else isHorizontal = 1 * weightageAt;\n return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const start_mid_z_dist = startPoint[2] - midPoint[2];\n const start_end_z_dist = startPoint[2] - endPoint[2];\n const mid_end_z_dist = midPoint[2] - endPoint[2];\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n if (cos_in > 1.0) cos_in = 1.0;\n else if (cos_in < -1.0) cos_in = -1.0;\n let angleOfCurve = Math.acos(cos_in);\n angleOfCurve = (57.2958 * angleOfCurve) % 180;\n let fingerCurl;\n if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n else fingerCurl = FingerCurl.full;\n return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n if (max_dist_x === Math.abs(start_end_x_dist)) {\n if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else {\n if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n }\n return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n let estimatedDirection;\n if (max_dist_y === Math.abs(start_end_y_dist)) {\n if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else {\n if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n }\n return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n if (reqd_vertical_direction === FingerDirection.verticalUp) {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n else estimatedDirection = FingerDirection.diagonalUpRight;\n } else {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n else estimatedDirection = FingerDirection.diagonalDownRight;\n }\n return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n let voteVertical = 0.0;\n let voteDiagonal = 0.0;\n let voteHorizontal = 0.0;\n const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n else voteHorizontal += options.DISTANCE_VOTE_POWER;\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n let calc_start_point_x = startPoint[0];\n let calc_start_point_y = startPoint[1];\n let calc_end_point_x = endPoint[0];\n let calc_end_point_y = endPoint[1];\n if (max_dist === start_mid_dist) {\n calc_end_point_x = endPoint[0];\n calc_end_point_y = endPoint[1];\n } else if (max_dist === mid_end_dist) {\n calc_start_point_x = midPoint[0];\n calc_start_point_y = midPoint[1];\n }\n const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n voteVertical += votes[0];\n voteDiagonal += votes[1];\n voteHorizontal += votes[2];\n for (const fingerSlope of fingerSlopes) {\n const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n voteVertical += fingerVotes[0];\n voteDiagonal += fingerVotes[1];\n voteHorizontal += fingerVotes[2];\n }\n // in case of tie, highest preference goes to Vertical,\n // followed by horizontal and then diagonal\n let estimatedDirection;\n if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n } else {\n estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n }\n return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n // step 1: calculate slopes\n const slopesXY: number[][] = [];\n const slopesYZ: number[][] = [];\n const fingerCurls: number[] = [];\n const fingerDirections: number[] = [];\n if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n // step 1: calculate slopes\n for (const finger of Finger.all) {\n const points = Finger.getPoints(finger);\n const slopeAtXY: number[] = [];\n const slopeAtYZ: number[] = [];\n for (const point of points) {\n const point1 = landmarks[point[0]];\n const point2 = landmarks[point[1]];\n // calculate single slope\n const slopes = getSlopes(point1, point2);\n const slopeXY = slopes[0];\n const slopeYZ = slopes[1];\n slopeAtXY.push(slopeXY);\n slopeAtYZ.push(slopeYZ);\n }\n slopesXY.push(slopeAtXY);\n slopesYZ.push(slopeAtYZ);\n }\n\n // step 2: calculate orientations\n for (const finger of Finger.all) {\n // start finger predictions from palm - except for thumb\n const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n const fingerPointsAt = Finger.getPoints(finger);\n const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n const endPoint = landmarks[fingerPointsAt[3][1]];\n // check if finger is curled\n const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n fingerCurls[finger] = fingerCurled;\n fingerDirections[finger] = fingerPosition;\n }\n return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n if (!keypoints || keypoints.length === 0) return null;\n const estimatorRes = estimate(keypoints);\n const landmarks = {};\n for (const fingerIdx of Finger.all) {\n landmarks[Finger.getName(fingerIdx)] = {\n curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n };\n }\n return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n const poses: { name: string, confidence: number }[] = [];\n if (!keypoints || keypoints.length === 0) return poses;\n const estimatorRes = estimate(keypoints);\n for (const gesture of Gestures) {\n const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n }\n return poses;\n}\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n | 'victory'\n | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { body: number, gesture: BodyGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n }\n }\n return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { face: number, gesture: FaceGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 450) {\n const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2] || 0;\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { iris: number, gesture: IrisGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations?.leftEyeIris?.[0] || !res[i].annotations?.rightEyeIris?.[0]) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n if (rightIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking right' });\n } else {\n if (leftIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking left' });\n }\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { hand: number, gesture: HandGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: { name: string, position: Point }[] = [];\n if (res[i].annotations) {\n for (const [finger, pos] of Object.entries(res[i].annotations)) {\n if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n if (res[i].keypoints) {\n const poses = fingerPose.match(res[i].keypoints);\n for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n }\n }\n return gestures;\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 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y: 0.40625 },\n { x: 0.84375, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.84375, y: 0.46875 },\n { x: 0.84375, y: 0.46875 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y: 0.59375 },\n { x: 0.96875, y: 0.59375 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.96875, y: 0.65625 },\n { x: 0.96875, y: 0.65625 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0.15625, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n 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0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 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0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 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},\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, Tensor1D, Tensor2D, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model: GraphModel) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSize = this?.model?.inputs?.[0]?.shape?.[2] || 0;\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n const t: Record = {};\n t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n const res = tf.concat2d([t.startPoints as Tensor2D, t.endPoints as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res as Tensor;\n }\n\n normalizeLandmarks(rawPalmLandmarks, index: number): Tensor {\n const t: Record = {};\n t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n t.div = tf.div(t.reshape, this.inputSizeTensor);\n t.landmarks = tf.add(t.div, this.anchors[index] ? this.anchors[index] : 0);\n const res = tf.mul(t.landmarks, this.inputSizeTensor);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n async predict(input: Tensor4D, config: Config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n t.div = tf.div(t.resize, constants.tf127);\n t.image = tf.sub(t.div, constants.tf1);\n t.batched = this.model.execute(t.image) as Tensor;\n t.predictions = tf.squeeze(t.batched);\n t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.slice);\n t.scores = tf.squeeze(t.sigmoid);\n const scores = await t.scores.data();\n t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n t.norm = this.normalizeBoxes(t.boxes);\n // box detection is flaky so we look for 3x boxes than we need results\n t.nms = await tf.image.nonMaxSuppressionAsync(t.norm as Tensor2D, t.scores as Tensor1D, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n const nms = await t.nms.array() as number[];\n const hands: { startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }[] = [];\n for (const index of nms) {\n const p: Record = {};\n p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n p.norm = this.normalizeLandmarks(p.slice, index);\n p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n const box = await p.box.data();\n const startPoint = box.slice(0, 2) as unknown as Point;\n const endPoint = box.slice(2, 4) as unknown as Point;\n const palmLandmarks = await p.palmLandmarks.array();\n const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n const scaled = util.scaleBoxCoordinates(hand, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);\n hands.push(scaled);\n Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: ({ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null)[];\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n this.inputSize = this.handPoseModel?.inputs?.[0].shape?.[2] || 0;\n this.storedBoxes = [];\n this.skipped = Number.MAX_SAFE_INTEGER;\n this.detectedHands = 0;\n }\n\n calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = util.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...util.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(coord[2]),\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames\n let boxes;\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n this.skipped++;\n } else {\n boxes = await this.handDetector.predict(image, config);\n this.skipped = 0;\n }\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: { landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }[] = [];\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n const palmCenter = util.getBoxCenter(currentBox);\n const palmCenterNormalized: [number, number] = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = tf.div(croppedInput, constants.tf255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Tensor[];\n lastTime = now();\n tf.dispose(handImage);\n const confidence = (await confidenceT.data())[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence / 4) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n const result = {\n landmarks: coords,\n confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n const result = {\n confidence: currentBox.confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: 0,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n landmarks: [],\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport function initPipeline() {\n const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!handPipeline) initPipeline();\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: HandResult[] = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n const keypoints = predictions[i].landmarks as unknown as Point[];\n let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: Box = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n const landmarks = fingerPose.analyze(keypoints);\n hands.push({\n id: i,\n score: Math.round(100 * predictions[i].confidence) / 100,\n boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n label: 'hand',\n box,\n boxRaw,\n keypoints,\n annotations: annotations as HandResult['annotations'],\n landmarks: landmarks as HandResult['landmarks'],\n });\n }\n return hands;\n}\n\nexport async function loadDetect(config: Config): Promise {\n if (env.initial) handDetectorModel = null;\n if (!handDetectorModel) handDetectorModel = await loadModel(config.hand.detector?.modelPath);\n else if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n return handDetectorModel;\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) handPoseModel = null;\n if (!handPoseModel) handPoseModel = await loadModel(config.hand.skeleton?.modelPath);\n else if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n return handPoseModel;\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ninterface HandDetectResult {\n id: number,\n score: number,\n box: Box,\n boxRaw: Box,\n label: HandType,\n}\n\nconst cache: {\n boxes: HandDetectResult[],\n hands: HandResult[];\n} = {\n boxes: [],\n hands: [],\n};\n\nconst fingerMap = {\n /*\n thumb: [0, 1, 2, 3, 4],\n index: [0, 5, 6, 7, 8],\n middle: [0, 9, 10, 11, 12],\n ring: [0, 13, 14, 15, 16],\n pinky: [0, 17, 18, 19, 20],\n palm: [0],\n */\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n base: [0],\n palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise {\n // HandTrack Model: Original: TFJS Port: \n if (env.initial) models[0] = null;\n if (!models[0]) {\n // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n // ideally need to prune the model itself\n fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n models[0] = await loadModel(config.hand.detector?.modelPath);\n const inputs = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) models[1] = null;\n if (!models[1]) {\n models[1] = await loadModel(config.hand.skeleton?.modelPath);\n const inputs = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models[0]) await loadDetect(config);\n if (!models[1]) await loadSkeleton(config);\n return models;\n}\n\nasync function detectHands(input: Tensor4D, config: Config): Promise {\n const hands: HandDetectResult[] = [];\n if (!input || !models[0]) return hands;\n const t: Record = {};\n const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n const width = Math.round(height * ratio / 8) * 8;\n t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n t.cast = tf.cast(t.resize, 'int32');\n [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n t.scores = tf.squeeze(t.rawScores, [0]);\n const classScores: Tensor[] = tf.unstack(t.scores, 1); // unstack scores based on classes\n tf.dispose(classScores[faceIndex]);\n classScores.splice(faceIndex, 1); // remove faces\n t.filtered = tf.stack(classScores, 1); // restack\n tf.dispose(classScores);\n // t.filtered = t.scores;\n t.max = tf.max(t.filtered, 1); // max overall score\n t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n let id = 0;\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.max as Tensor1D, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n const nms = await t.nms.data();\n const scores = await t.max.data();\n const classNum = await t.argmax.data();\n for (const nmsIndex of Array.from(nms)) { // generates results for each class\n const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n const boxYX = await boxSlice.data();\n tf.dispose(boxSlice);\n const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n const score = scores[nmsIndex];\n const label = classes[classNum[nmsIndex]] as HandType;\n const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n hands.push(hand);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n hands.sort((a, b) => b.score - a.score);\n if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n return hands;\n}\n\nasync function detectFingers(input: Tensor4D, h: HandDetectResult, config: Config): Promise {\n const hand: HandResult = { // initial values inherited from hand detect\n id: h.id,\n score: Math.round(100 * h.score) / 100,\n boxScore: Math.round(100 * h.score) / 100,\n fingerScore: 0,\n box: h.box,\n boxRaw: h.boxRaw,\n label: h.label,\n keypoints: [],\n landmarks: {} as HandResult['landmarks'],\n annotations: {} as HandResult['annotations'],\n };\n if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n const t: Record = {};\n const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n t.div = tf.div(t.crop, constants.tf255);\n [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n const rawScore = (await t.score.data())[0];\n const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n if (score >= (config.hand.minConfidence || 0)) {\n hand.fingerScore = score;\n t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n const coordsData: Point[] = await t.reshaped.array() as Point[];\n const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n return hand;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!models[0]?.['executor'] || !models[1]?.['executor'] || !models[0].inputs[0].shape || !models[1].inputs[0].shape) return []; // something is wrong with the model\n outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n skipped++; // increment skip frames\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.hands; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else { // finally rerun detector\n cache.boxes = await detectHands(input, config);\n lastTime = now();\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n skipped = 0;\n }\n\n const oldCache = [...cache.boxes];\n cache.boxes.length = 0; // reset cache\n if (config.cacheSensitivity > 0) {\n for (let i = 0; i < cache.hands.length; i++) {\n const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n const boxScale = box.scale(boxKpt.box, boxExpandFact);\n const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n // const boxCrop = box.crop(boxScaleRaw);\n cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n }\n }\n }\n for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n cache.hands[i].box = bbox.box;\n cache.hands[i].boxRaw = bbox.boxRaw;\n }\n resolve(cache.hands);\n });\n}\n", "/**\n * Type definitions for Human result object\n */\n\nimport type { Tensor } from './tfjs/types';\nimport type { FaceGesture, BodyGesture, HandGesture, IrisGesture } from './gesture/gesture';\nimport type { AnyCanvas } from './exports';\n\n/** generic box as [x, y, width, height] */\nexport type Box = [number, number, number, number];\n/** generic point as [x, y, z?] */\nexport type Point = [number, number, number?];\n\nexport type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';\nexport type Gender = 'male' | 'female' | 'unknown';\nexport type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';\nexport type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette'\n | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner'\n | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris'\n | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris'\n | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';\n\n/** Face results\n * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models\n * - Some values may be null if specific model is not enabled\n */\nexport interface FaceResult {\n /** face id */\n id: number\n /** overall face score */\n score: number,\n /** detection score */\n boxScore: number,\n /** mesh score */\n faceScore: number,\n /** detected face box */\n box: Box,\n /** detected face box normalized to 0..1 */\n boxRaw: Box,\n /** detected face box size */\n size: [number, number],\n /** detected face mesh */\n mesh: Point[]\n /** detected face mesh normalized to 0..1 */\n meshRaw: Point[],\n /** face contours as array of 2d points normalized to 0..1 */\n // contoursRaw: Array<[number, number]>,\n /** face contours as array of 2d points */\n // contours: Array<[number, number]>,\n /** mesh keypoints combined into annotated results */\n annotations: Record,\n /** detected age */\n age?: number,\n /** detected gender */\n gender?: Gender,\n /** gender detection score */\n genderScore?: number,\n /** detected emotions */\n emotion?: { score: number, emotion: Emotion }[],\n /** detected race */\n race?: { score: number, race: Race }[],\n /** face descriptor */\n embedding?: number[],\n /** face distance from camera */\n distance?: number,\n /** face anti-spoofing result confidence */\n real?: number,\n /** face liveness result confidence */\n live?: number,\n /** face rotation details */\n rotation?: {\n angle: { roll: number, yaw: number, pitch: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n } | null,\n /** detected face as tensor that can be used in further pipelines */\n tensor?: Tensor,\n}\n\nexport type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';\nexport type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder'\n | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle'\n | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';\nexport type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;\nexport type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';\nexport type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';\nexport type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;\n\n/** Body Result keypoints */\nexport interface BodyKeypoint {\n /** body part name */\n part: BodyLandmark,\n /** body part position */\n position: Point,\n /** body part position normalized to 0..1 */\n positionRaw: Point,\n /** body part position relative to body center in meters */\n distance?: Point,\n /** body part detection score */\n score: number,\n}\n\n/** Body results */\nexport interface BodyResult {\n /** body id */\n id: number,\n /** body detection score */\n score: number,\n /** detected body box */\n box: Box,\n /** detected body box normalized to 0..1 */\n boxRaw: Box,\n /** detected body keypoints */\n keypoints: BodyKeypoint[]\n /** detected body keypoints combined into annotated parts */\n annotations: Record,\n}\n\nexport type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';\nexport type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';\nexport type FingerCurl = 'none' | 'half' | 'full';\nexport type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';\n\n/** Hand results */\nexport interface HandResult {\n /** hand id */\n id: number,\n /** hand overal score */\n score: number,\n /** hand detection score */\n boxScore: number,\n /** hand skelton score */\n fingerScore: number,\n /** detected hand box */\n box: Box,\n /** detected hand box normalized to 0..1 */\n boxRaw: Box,\n /** detected hand keypoints */\n keypoints: Point[],\n /** detected hand class */\n label: HandType,\n /** detected hand keypoints combined into annotated parts */\n annotations: Record,\n /** detected hand parts annotated with part gestures */\n landmarks: Record,\n}\n\nexport type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter'\n | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee'\n | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork'\n | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant'\n | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book'\n | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';\n\n/** Object results */\nexport interface ObjectResult {\n /** object id */\n id: number,\n /** object detection score */\n score: number,\n /** detected object class id */\n class: number,\n /** detected object class name */\n label: ObjectType,\n /** detected object box */\n box: Box,\n /** detected object box normalized to 0..1 */\n boxRaw: Box,\n}\n\n/** Gesture combined results\n * Each result has:\n * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`\n * - gesture: gesture detected\n */\nexport type GestureResult =\n { 'face': number, gesture: FaceGesture }\n | { 'iris': number, gesture: IrisGesture }\n | { 'body': number, gesture: BodyGesture }\n | { 'hand': number, gesture: HandGesture }\n\n/** Person getter\n* - Triggers combining all individual results into a virtual person object\n*/\nexport interface PersonResult {\n /** person id */\n id: number,\n /** face result that belongs to this person */\n face: FaceResult,\n /** body result that belongs to this person */\n body: BodyResult | null,\n /** left and right hand results that belong to this person */\n hands: { left: HandResult | null, right: HandResult | null },\n /** detected gestures specific to this person */\n gestures: GestureResult[],\n /** box that defines the person */\n box: Box,\n /** box that defines the person normalized to 0..1 */\n boxRaw?: Box,\n}\n\n/**\n * Result interface definition for **Human** library\n *\n * Contains all possible detection results\n */\nexport interface Result {\n /** {@link FaceResult}: detection & analysis results */\n face: FaceResult[],\n /** {@link BodyResult}: detection & analysis results */\n body: BodyResult[],\n /** {@link HandResult}: detection & analysis results */\n hand: HandResult[],\n /** {@link GestureResult}: detection & analysis results */\n gesture: GestureResult[],\n /** {@link ObjectResult}: detection & analysis results */\n object: ObjectResult[]\n /** global performance object with timing values for each operation */\n performance: Record,\n /** optional processed canvas that can be used to draw input on screen */\n canvas?: AnyCanvas | null,\n /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */\n readonly timestamp: number,\n /** getter property that returns unified persons object */\n persons: PersonResult[],\n /** Last known error message */\n error: string | null;\n /** Resolution width */\n width: number,\n /** Resolution height */\n height: number,\n}\n\nexport const empty = (error: string | null = null): Result => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });\n", "export const kpt: string[] = [ // used to create part labels\n 'nose',\n 'leftEye',\n 'rightEye',\n 'leftEar',\n 'rightEar',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n];\n\nexport const horizontal: string[][] = [ // used to fix left vs right\n ['leftEye', 'rightEye'],\n ['leftEar', 'rightEar'],\n ['leftShoulder', 'rightShoulder'],\n ['leftElbow', 'rightElbow'],\n ['leftWrist', 'rightWrist'],\n ['leftHip', 'rightHip'],\n ['leftKnee', 'rightKnee'],\n ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: string[][] = [ // used to remove unlikely keypoint positions\n ['leftKnee', 'leftShoulder'],\n ['rightKnee', 'rightShoulder'],\n ['leftAnkle', 'leftKnee'],\n ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: string[][][] = [ // used to match relative body parts\n [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record = { // used to create body outline in annotations\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation, empty, FaceLandmark } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = empty();\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n const t0 = now();\n if (!newResult) return empty();\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n\n /* curve fitted: buffer = 8 - ln(delay)\n interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n - at 50ms delay buffer = ~4.1 => 28% towards live data\n - at 250ms delay buffer = ~2.5 => 40% towards live data\n - at 500ms delay buffer = ~1.8 => 55% towards live data\n - at 750ms delay buffer = ~1.4 => 71% towards live data\n - at 1sec delay buffer = 1 which means live data is used\n */\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n if (newResult.error) bufferedResult.error = newResult.error;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)) as BodyResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((newKpt, j) => ({\n score: newKpt.score,\n part: newKpt.part,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n ],\n distance: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n ],\n }))) as { score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }[];\n\n const annotations: Record = {} as Record; // recreate annotations\n let coords = { connected: {} };\n if (config.body.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n else if (config.body.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n else if (config.body.modelPath?.includes('movenet')) coords = moveNetCoords;\n for (const [name, indexes] of Object.entries(coords.connected as Record)) {\n const pt: Point[][] = [];\n for (let j = 0; j < indexes.length - 1; j++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n : [];\n let annotations = {};\n if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.hand[i].annotations;\n } else if (newResult.hand[i].annotations) {\n for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n annotations[key] = newResult.hand[i]?.annotations?.[key]?.[0]\n ? newResult.hand[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)) as FaceResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n let annotations: Record = newResult.face[i].annotations;\n if (Object.keys(bufferedResult.face[i].annotations).length !== Object.keys(newResult.face[i].annotations).length) {\n bufferedResult.face[i].annotations = newResult.face[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.face[i].annotations;\n } else if (newResult.face[i].annotations) {\n for (const key of Object.keys(newResult.face[i].annotations)) { // update annotations\n annotations[key] = newResult.face[i]?.annotations?.[key]?.[0]\n ? newResult.face[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n if (newResult.face[i].rotation) {\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.bearing || 0) + (newResult.face[i].rotation?.gaze.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.strength || 0) + (newResult.face[i].rotation?.gaze.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw, annotations }; // shallow clone plus updated values\n } else {\n bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw, annotations }; // shallow clone plus updated values\n }\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)) as ObjectResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)) as PersonResult[];\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n }\n }\n }\n\n // copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n // copy resolution info\n bufferedResult.width = newResult.width;\n bufferedResult.height = newResult.height;\n\n // append interpolation performance data\n const t1 = now();\n interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n return bufferedResult;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]);\n // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);\n // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel\n t.fg = tf.softmax(t.fgRaw);\n t.mul = tf.mul(t.fg, constants.tf255);\n t.expand = tf.expandDims(t.mul, 2);\n t.output = tf.image.resizeBilinear(t.expand as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.output], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.output, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = number[]\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n // general minkowski distance, euclidean distance is limited case where order is 2\n if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\n let sum = 0;\n for (let i = 0; i < descriptor1.length; i++) {\n const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n }\n const dist = Math.round(100 * (options.multiplier || 20) * sum) / 100;\n return dist;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n if (dist === 0) return 1; // short circuit for identical inputs\n const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n const clamp = Math.round(100 * Math.max(Math.min(norm, 1), 0)) / 100; // clamp to 0..1\n return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n * default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n const dist = distance(descriptor1, descriptor2, options);\n return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function find(descriptor: Descriptor, descriptors: Descriptor[], options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { // validate input\n return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n }\n let lowestDistance = Number.MAX_SAFE_INTEGER;\n let index = -1;\n for (let i = 0; i < descriptors.length; i++) {\n const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\n if (res < lowestDistance) {\n lowestDistance = res;\n index = i;\n }\n if (lowestDistance < (options.threshold || 0)) break;\n }\n const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gear from './gear/gear';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as insightface from './face/insightface';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as meet from './segmentation/meet';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(instance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n if (!model) return null;\n if (!instance?.config?.validateModels) return null;\n const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];\n const ops: string[] = [];\n const missing: string[] = [];\n interface Op { name: string, category: string, op: string }\n const url = model['modelUrl'] as string;\n const executor = model['executor'];\n if (executor?.graph?.nodes) {\n for (const kernel of Object.values(executor.graph.nodes)) {\n const op = (kernel as Op).op.toLowerCase();\n if (!ops.includes(op)) ops.push(op);\n }\n } else {\n if (!executor && instance.config.debug) {\n log('model not loaded', name);\n }\n }\n for (const op of ops) {\n if (!simpleOps.includes(op) // exclude simple ops\n && !ignoreOps.includes(op) // exclude specific ops\n && !instance.env.kernels.includes(op) // check actual kernel ops\n && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n missing.push(op);\n }\n }\n if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n numLoadedModels: number,\n numDefinedModels: number,\n percentageLoaded: number,\n totalSizeFromManifest: number,\n totalSizeWeights: number,\n totalSizeLoading: number,\n modelStats: ModelInfo[],\n}\n\n/** Models class used by Human\n * - models: record of all GraphModels\n * - list: returns list of configured models with their stats\n * - loaded: returns array of loaded models\n * - reset: unloads all models\n * - validate: checks loaded models for valid kernel ops vs current backend\n * - stats: live detailed model stats that can be checked during model load phase\n */\nexport class Models {\n private instance: Human;\n models: Record = {};\n\n constructor(currentInstance: Human) {\n this.models = {};\n this.instance = currentInstance;\n }\n\n stats(): ModelStats {\n let totalSizeFromManifest = 0;\n let totalSizeWeights = 0;\n let totalSizeLoading = 0;\n for (const m of Object.values(modelStats)) {\n totalSizeFromManifest += Number.isNaN(+m.sizeFromManifest) ? 0 : m.sizeFromManifest;\n totalSizeWeights += Number.isNaN(+m.sizeLoadedWeights) ? 0 : m.sizeLoadedWeights;\n totalSizeLoading += Number.isNaN(+m.sizeDesired) ? 0 : m.sizeDesired;\n }\n const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n return {\n numLoadedModels: Object.values(modelStats).filter((m) => m?.loaded).length,\n numDefinedModels: Object.keys(this.models).length,\n percentageLoaded,\n totalSizeFromManifest,\n totalSizeWeights,\n totalSizeLoading,\n modelStats: Object.values(modelStats),\n };\n }\n\n reset(): void {\n for (const model of Object.keys(this.models)) this.models[model] = null;\n }\n\n async load(instance?: Human): Promise {\n if (env.initial) this.reset();\n if (instance) this.instance = instance;\n const m: Record> = {};\n // face main models\n m.blazeface = (this.instance.config.face.enabled && !this.models.blazeface) ? blazeface.load(this.instance.config) : null;\n m.antispoof = (this.instance.config.face.enabled && this.instance.config.face.antispoof?.enabled && !this.models.antispoof) ? antispoof.load(this.instance.config) : null;\n m.liveness = (this.instance.config.face.enabled && this.instance.config.face.liveness?.enabled && !this.models.liveness) ? liveness.load(this.instance.config) : null;\n m.faceres = (this.instance.config.face.enabled && this.instance.config.face.description?.enabled && !this.models.faceres) ? faceres.load(this.instance.config) : null;\n m.emotion = (this.instance.config.face.enabled && this.instance.config.face.emotion?.enabled && !this.models.emotion) ? emotion.load(this.instance.config) : null;\n m.iris = (this.instance.config.face.enabled && this.instance.config.face.iris?.enabled && !this.instance.config.face.attention?.enabled && !this.models.iris) ? iris.load(this.instance.config) : null;\n m.facemesh = (this.instance.config.face.enabled && this.instance.config.face.mesh?.enabled && (!this.models.facemesh)) ? facemesh.load(this.instance.config) : null;\n // face alternatives\n m.gear = (this.instance.config.face.enabled && this.instance.config.face['gear']?.enabled && !this.models.gear) ? gear.load(this.instance.config) : null;\n m.ssrnetage = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetage) ? ssrnetAge.load(this.instance.config) : null;\n m.ssrnetgender = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetgender) ? ssrnetGender.load(this.instance.config) : null;\n m.mobilefacenet = (this.instance.config.face.enabled && this.instance.config.face['mobilefacenet']?.enabled && !this.models.mobilefacenet) ? mobilefacenet.load(this.instance.config) : null;\n m.insightface = (this.instance.config.face.enabled && this.instance.config.face['insightface']?.enabled && !this.models.insightface) ? insightface.load(this.instance.config) : null;\n // body alterinatives\n m.blazepose = (this.instance.config.body.enabled && !this.models.blazepose && this.instance.config.body.modelPath?.includes('blazepose')) ? blazepose.loadPose(this.instance.config) : null;\n m.blazeposedetect = (this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body['detector'] && this.instance.config.body['detector'].modelPath) ? blazepose.loadDetect(this.instance.config) : null;\n m.efficientpose = (this.instance.config.body.enabled && !this.models.efficientpose && this.instance.config.body.modelPath?.includes('efficientpose')) ? efficientpose.load(this.instance.config) : null;\n m.movenet = (this.instance.config.body.enabled && !this.models.movenet && this.instance.config.body.modelPath?.includes('movenet')) ? movenet.load(this.instance.config) : null;\n m.posenet = (this.instance.config.body.enabled && !this.models.posenet && this.instance.config.body.modelPath?.includes('posenet')) ? posenet.load(this.instance.config) : null;\n // hand alternatives\n m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null;\n m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null;\n // if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null];\n if (this.instance.config.hand.enabled && !this.models.handdetect && this.instance.config.hand.detector?.modelPath?.includes('handdetect')) {\n m.handdetect = handpose.loadDetect(this.instance.config);\n m.handskeleton = handpose.loadSkeleton(this.instance.config);\n }\n // object detection alternatives\n m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null;\n m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null;\n // segmentation alternatives\n m.selfie = (this.instance.config.segmentation.enabled && !this.models.selfie && this.instance.config.segmentation.modelPath?.includes('selfie')) ? selfie.load(this.instance.config) : null;\n m.meet = (this.instance.config.segmentation.enabled && !this.models.meet && this.instance.config.segmentation.modelPath?.includes('meet')) ? meet.load(this.instance.config) : null;\n m.rvm = (this.instance.config.segmentation.enabled && !this.models.rvm && this.instance.config.segmentation.modelPath?.includes('rvm')) ? rvm.load(this.instance.config) : null;\n\n // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n for (const [model, promise] of Object.entries(m)) {\n if (promise?.['then']) promise['then']((val) => this.models[model] = val);\n }\n await Promise.all(Object.values(m)); // wait so this function does not resolve prematurely\n }\n\n list() {\n const models = Object.keys(this.models).map((model) => ({ name: model, loaded: (this.models[model] !== null), size: 0, url: this.models[model] ? this.models[model]?.['modelUrl'] : null }));\n for (const m of models) {\n const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));\n if (!stats) continue;\n m.size = modelStats[stats].sizeLoadedWeights;\n m.url = modelStats[stats].url;\n }\n return models;\n }\n\n loaded(): string[] {\n const list = this.list();\n const loaded = list.filter((model) => model.loaded).map((model) => model.name);\n return loaded;\n }\n\n validate(): { name: string, missing: string[] }[] {\n const missing: KernelOps[] = [];\n for (const defined of Object.keys(this.models)) {\n const model: GraphModel | null = this.models[defined as keyof Models];\n if (!model) continue;\n const res = validateModel(this.instance, model, defined);\n if (res) missing.push(res);\n }\n return missing;\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport type { Tensor, Tensor3D } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n keypoints: BodyKeypoint[],\n padding: [number, number][];\n} = {\n keypoints: [],\n padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n for (const pair of coords.horizontal) { // fix body parts left vs right\n const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n if (body.keypoints[left] && body.keypoints[right]) {\n if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n }\n for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n if (body.keypoints[lower] && body.keypoints[higher]) {\n if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n body.keypoints.splice(lower, 1);\n }\n }\n }\n for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n const distanceLeft = body.keypoints[left] ? [\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n ] : [0, 0];\n const distanceRight = body.keypoints[right] ? [\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n ] : [0, 0];\n if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n}\n\nexport function jitter(keypoints: BodyKeypoint[]): BodyKeypoint[] {\n for (let i = 0; i < keypoints.length; i++) {\n if (keypoints[i] && cache.keypoints[i]) {\n const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n if (diff[0] < maxJitter && diff[1] < maxJitter) {\n keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n } else {\n cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n }\n } else {\n cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n }\n }\n return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n cache.padding = [\n [0, 0], // dont touch batch\n [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(input, cache.padding);\n t.resize = tf.image.resizeBilinear(t.pad as Tensor3D, [inputSize, inputSize]);\n const final = tf.cast(t.resize, 'int32');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n body.keypoints = body.keypoints.filter((kpt) => kpt?.position); // filter invalid keypoints\n for (const kpt of body.keypoints) {\n kpt.position = [\n kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n ];\n kpt.positionRaw = [\n kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n ];\n }\n const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n body.box = rescaledBoxes.box;\n body.boxRaw = rescaledBoxes.boxRaw;\n return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n boxes: Box[], // unused\n bodies: BodyResult[];\n last: number,\n} = {\n boxes: [],\n bodies: [],\n last: 0,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n fakeOps(['size'], config);\n model = await loadModel(config.body.modelPath);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize < 64) inputSize = 256;\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', false); // default=false \n return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n const kpt = res[0][0];\n const keypoints: BodyKeypoint[] = [];\n let score = 0;\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw,\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * positionRaw[0]),\n Math.round((image.shape[1] || 0) * positionRaw[1]),\n ],\n });\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const bodies: BodyResult[] = [];\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n const annotations: Record = {};\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n fix.bodyParts(body);\n bodies.push(body);\n return bodies;\n}\n\nfunction parseMultiPose(res, config, image) {\n const bodies: BodyResult[] = [];\n for (let id = 0; id < res[0].length; id++) {\n const kpt = res[0][id];\n const boxScore = Math.round(100 * kpt[51 + 4]) / 100;\n if (boxScore > config.body.minConfidence) {\n const keypoints: BodyKeypoint[] = [];\n for (let i = 0; i < 17; i++) {\n const score = kpt[3 * i + 2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n keypoints.push({\n part: coords.kpt[i] as BodyLandmark,\n score: Math.round(100 * score) / 100,\n positionRaw,\n position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n });\n }\n }\n // const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n // movenet-multipose has built-in box details\n const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n const boxNorm: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n // const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n const body: BodyResult = { id, score: boxScore, box: boxNorm, boxRaw, keypoints: [...keypoints], annotations };\n fix.bodyParts(body);\n bodies.push(body);\n }\n }\n bodies.sort((a, b) => b.score - a.score);\n if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return []; // something is wrong with the model\n if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n skipped++; // increment skip frames\n const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.bodies; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const t: Record = {};\n skipped = 0;\n // run detection on squared input and no cached boxes\n t.input = fix.padInput(input, inputSize);\n t.res = model?.execute(t.input) as Tensor;\n cache.last = now();\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17)\n ? parseSinglePose(res, config, input)\n : parseMultiPose(res, config, input);\n for (const body of cache.bodies) {\n fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n fix.jitter(body.keypoints);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n resolve(cache.bodies);\n });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) {\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n let id = 0;\n let results: ObjectResult[] = [];\n const size = inputSize;\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)) as Tensor2D);\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n const featuresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)) as Tensor2D);\n const boxesMaxT = tf.reshape(featuresT, [-1, 4, (featuresT.shape?.[1] || 0) / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdxT = tf.argMax(boxesMaxT, 2); // what we need is indexes of features with highest scores, not values itself\n const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < (scoresT.shape?.[1] || 0); j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > (config.object.minConfidence || 0) && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / (size))); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label as ObjectType,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: box.map((a) => Math.trunc(a)) as Box,\n boxRaw,\n };\n results.push(result);\n }\n }\n }\n tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n }\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: number[] = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected || 0, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = Array.from(await nms.data());\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n const resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n const normT = tf.div(resizeT, constants.tf255);\n const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n let objectT;\n if (config.object.enabled) objectT = model.execute(transposeT);\n lastTime = now();\n\n const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n last = obj;\n tf.dispose([resizeT, normT, transposeT, ...objectT]);\n resolve(obj);\n });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): BodyResult[] {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i): BodyResult => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score: score as number,\n part: part as BodyLandmark,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n })),\n annotations: {} as Record,\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: unknown[]; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n // @ts-ignore getter is of unknown type\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n\nexport function getOffsetPoint(y, x, keypoint: number, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + count),\n };\n}\n\nexport function getImageCoords(part, outputStride: number, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a: { x: number, y: number }, b: { x: number, y: number }) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId: number, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: utils.partNames[root.part.id] as BodyLandmark,\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score: number, heatmapY: number, heatmapX: number, scores) {\n const [height, width]: [number, number] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: { keypoints, box: Box, score: number }[] = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n /** posenet is mostly obsolete\n * caching is not implemented\n */\n if (!model?.['executor']) return [];\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Tensor[] = model.execute(normalized, poseNetOutputs) as Tensor[];\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\n// internal state varaibles\nconst outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];\nconst t: Record = {}; // contains input tensor and recurrent states\nlet ratio = 0;\n\nfunction init(config: Config) {\n tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);\n t.r1i = tf.tensor(0.0);\n t.r2i = tf.tensor(0.0);\n t.r3i = tf.tensor(0.0);\n t.r4i = tf.tensor(0.0);\n ratio = config.segmentation.ratio || 0.5;\n t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n init(config);\n return model;\n}\n\nconst normalize = (r: Tensor): Tensor => tf.tidy(() => {\n const squeeze = tf.squeeze(r, ([0]));\n const mul = tf.mul(squeeze, constants.tf255);\n const cast = tf.cast(mul, 'int32');\n return cast;\n});\n\nfunction getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present\n const rgb = fgr\n ? normalize(fgr) // normalize and use value\n : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const a = pha\n ? normalize(pha) // normalize and use value\n : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const rgba = tf.concat([rgb, a], -1);\n tf.dispose([rgb, a]);\n return rgba;\n}\n\nfunction getState(state: Tensor): Tensor { // gets internal recurrent states\n return tf.tidy(() => {\n const r: Record = {};\n r.unstack = tf.unstack(state, -1);\n r.concat = tf.concat(r.unstack, 1);\n r.split = tf.split(r.concat, 4, 1);\n r.stack = tf.concat(r.split, 2);\n r.squeeze = tf.squeeze(r.stack, [0]);\n r.expand = tf.expandDims(r.squeeze, -1);\n r.add = tf.add(r.expand, 1);\n r.mul = tf.mul(r.add, 127.5);\n r.cast = tf.cast(r.mul, 'int32');\n r.tile = tf.tile(r.cast, [1, 1, 3]);\n r.alpha = tf.fill([(r.tile as Tensor).shape[0] || 0, (r.tile as Tensor).shape[1] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-unnecessary-type-assertion\n return tf.concat([r.tile, r.alpha], -1);\n });\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor']) return null;\n // const expand = tf.expandDims(input, 0);\n t.src = tf.div(input, 255);\n if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed\n const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n rgba = getRGBA(fgr, pha);\n break;\n case 'alpha':\n rgba = getRGBA(null, pha);\n break;\n case 'foreground':\n rgba = getRGBA(fgr, null);\n break;\n case 'state':\n rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o\n break;\n default:\n rgba = tf.tensor(0);\n }\n tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);\n [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states\n return rgba;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n t.alpha = tf.image.resizeBilinear(t.squeeze as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]); // model selfie has a single channel that we can use directly\n t.mul = tf.mul(t.alpha, constants.tf255);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.mul], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n let id = 0;\n const persons: PersonResult[] = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.left?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.right?.id) person.gestures.push(gesture);\n }\n\n // create new overarching box from all boxes belonging to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face.box);\n extractXY(person.body?.box);\n extractXY(person.hands.left?.box);\n extractXY(person.hands.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape?.[1] && shape?.[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face: string = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport { empty, Result } from './result';\nimport type { Config } from './config';\nimport type { Human } from './human';\nimport type { Tensor, DataType } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise {\n const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob: Blob | null;\n let res: Result | undefined;\n switch (instance.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'body':\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await instance.detect(bitmap, instance.config);\n bitmap.close();\n }\n return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise {\n return new Promise((resolve) => {\n let src: string;\n // let size = 0;\n switch (instance.config.warmup) {\n case 'face':\n // size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n // size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = '';\n }\n // src = encodeURI('../assets/human-sample-upper.jpg');\n let img: HTMLImageElement;\n if (typeof Image !== 'undefined') img = new Image();\n // @ts-ignore env.image is an external monkey-patch\n else if (env.Image) img = new env.Image();\n else {\n resolve(undefined);\n return;\n }\n img.onload = async () => {\n const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n if (!canvas) {\n log('Warmup: Canvas not found');\n resolve(undefined);\n } else {\n const ctx = canvas.getContext('2d') as CanvasRenderingContext2D;\n if (ctx) ctx.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const tensor = await instance.image(canvas, true);\n const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\n resolve(res);\n }\n };\n if (src) img.src = src;\n else resolve(undefined);\n });\n}\n\nasync function warmupNode(instance: Human): Promise {\n const atob = (str: string) => Buffer.from(str, 'base64');\n let img;\n if (instance.config.warmup === 'face') img = atob(sample.face);\n else img = atob(sample.body);\n let res: Result;\n if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n // @ts-ignore\n const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n const expanded: Tensor = tf.expandDims(data, 0);\n instance.tf.dispose(data);\n // log('Input:', expanded);\n res = await instance.detect(expanded, instance.config);\n instance.tf.dispose(expanded);\n } else {\n if (instance.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await instance.detect(input, instance.config);\n */\n }\n // @ts-ignore\n return res;\n}\n\nasync function runInference(instance: Human) {\n let res: Result | undefined;\n if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n else if ((typeof Image !== 'undefined') || (env.Canvas !== undefined)) res = await warmupCanvas(instance);\n else res = await warmupNode(instance);\n return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n // @ts-ignore private property\n if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n const backendType = tf.getBackend();\n const webGLBackend = tf.backend();\n if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.['checkCompileCompletion']) {\n // log('compile pass: skip');\n return;\n }\n tf.env().set('ENGINE_COMPILE_ONLY', true);\n const numTensorsStart = tf.engine().state.numTensors;\n const compiledModels: string[] = [];\n for (const [modelName, model] of Object.entries(instance.models.models)) {\n if (!model) continue;\n const shape = (model?.modelSignature && model?.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n const dtype: DataType = (model?.modelSignature && model?.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n for (let dim = 0; dim < shape.length; dim++) {\n if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n }\n const tensor = tf.zeros(shape, dtype);\n try {\n const res = model.execute(tensor);\n compiledModels.push(modelName);\n if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n else tf.dispose(res);\n } catch {\n if (instance.config.debug) log('compile fail model:', modelName);\n }\n tf.dispose(tensor);\n }\n const kernels = await webGLBackend['checkCompileCompletionAsync']();\n webGLBackend['getUniformLocations']();\n if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n tf.env().set('ENGINE_COMPILE_ONLY', false);\n const numTensorsEnd = tf.engine().state.numTensors;\n if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial): Promise {\n await backend.check(instance, false);\n const t0 = now();\n instance.state = 'warmup';\n if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n return empty();\n }\n return new Promise(async (resolve) => {\n await instance.models.load();\n await tf.ready();\n await runCompile(instance);\n const res = await runInference(instance);\n const t1 = now();\n if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n instance.emit('warmup');\n resolve(res);\n });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\n// module imports\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as draw from './draw/draw';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as meet from './segmentation/meet';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as warmups from './warmup';\n\n// type definitions\nimport { Input, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, AnyCanvas, empty } from './exports';\nimport type { Tensor, Tensor4D } from './tfjs/types';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n\n /** Current configuration\n * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\n config: Config;\n\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n\n /** currenty processed image tensor and canvas */\n process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n /** Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n * [TFJS API](https://js.tensorflow.org/api/latest/)\n */\n tf;\n\n /** Object containing environment information used for diagnostics */\n env: Env = env;\n\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - canvas: draws input to canvas\n * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n */\n // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n draw: typeof draw = draw;\n\n /** Face Matching\n * - similarity: compare two face descriptors and return similarity index\n * - distance: compare two face descriptors and return raw calculated differences\n * - find: compare face descriptor to array of face descriptors and return best match\n */\n match: typeof match = match;\n\n /** Currently loaded models\n * @internal\n * {@link models#Models}\n */\n models: models.Models;\n\n /** Container for events dispatched by Human\n * Possible events:\n * - `create`: triggered when Human object is instantiated\n * - `load`: triggered when models are loaded (explicitly or on-demand)\n * - `image`: triggered when input image is processed\n * - `result`: triggered when detection is complete\n * - `warmup`: triggered when warmup is complete\n * - `error`: triggered on some errors\n */\n events: EventTarget | undefined;\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: number[];\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: [number, number][];\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n // definition end\n\n /** Constructor for **Human** library that is futher used for all operations\n * @param userConfig - user configuration object {@link Config}\n */\n constructor(userConfig?: Partial) {\n /*\n defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n ? 'https://vladmandic.github.io/tfjs/dist/'\n : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n */\n const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n this.version = app.version; // expose version property on instance of class\n Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n this.config = JSON.parse(JSON.stringify(defaults));\n Object.seal(this.config);\n this.config.cacheModels = typeof indexedDB !== 'undefined';\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n setModelLoadOptions(this.config);\n this.tf = tf;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.performance = {};\n this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n // object that contains all initialized models\n this.models = new models.Models(this);\n // reexport draw methods\n draw.init();\n this.result = empty();\n // export access to image processing\n this.process = { tensor: null, canvas: null };\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // init model validation\n models.validateModel(this, null, '');\n // include platform info\n this.emit('create');\n if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n const envTemp = JSON.parse(JSON.stringify(this.env));\n delete envTemp.kernels;\n delete envTemp.initial;\n delete envTemp.perfadd;\n if (this.config.debug) log('environment:', envTemp);\n }\n\n /** internal function to measure tensor leaks */\n analyze = (...msg: string[]) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n };\n\n /** internal function for quick sanity check on inputs @hidden */\n #sanity = (input: Input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n };\n\n /** Reset configuration to default values */\n reset(): void {\n const currentBackend = this.config.backend; // save backend;\n this.config = JSON.parse(JSON.stringify(defaults));\n this.config.backend = currentBackend;\n image.reset();\n env.initial = true;\n }\n\n /** Validate current configuration schema */\n validate(userConfig?: Partial) {\n const msgs = validate(defaults, userConfig || this.config);\n if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n return msgs;\n }\n\n /** Utility wrapper for performance.now() */\n now(): number { // eslint-disable-line class-methods-use-this\n return now();\n }\n\n /** Process input as return canvas and tensor\n *\n * @param input - any input {@link Input}\n * @param getTensor - should image processing also return tensor or just canvas\n * Returns object with `tensor` and `canvas`\n */\n image(input: Input, getTensor: boolean = false) {\n return image.process(input, this.config, getTensor);\n }\n\n /** Segmentation method takes any input and returns RGBA tensor\n * Note: Segmentation is not triggered as part of detect process\n *\n * @param input - {@link Input}\n * Returns tensor which contains image data in RGBA format\n */\n async segmentation(input: Input, userConfig?: Partial): Promise {\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (!this.config.segmentation.enabled) return null;\n const processed = await image.process(input, this.config);\n if (!processed.tensor) return null;\n let tensor: Tensor | null = null;\n if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);\n tf.dispose(processed.tensor);\n return tensor;\n }\n\n /** Compare two input tensors for pixel similarity\n * - use `human.image` to process any valid input and get a tensor that can be used for compare\n * - when passing manually generated tensors:\n * - both input tensors must be in format [1, height, width, 3]\n * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n * - return value is pixel similarity score normalized by input resolution and rgb channels\n */\n compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise {\n return image.compare(this.config, firstImageTensor, secondImageTensor);\n }\n\n /** Explicit backend initialization\n * - Normally done implicitly during initial load phase\n * - Call to explictly register and initialize TFJS backend without any other operations\n * - Use when changing backend during runtime\n */\n async init(): Promise {\n await backend.check(this, true);\n await this.tf.ready();\n image.reset();\n }\n\n /** WebCam helper methods\n *\n */\n public webcam = new WebCam();\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n *\n * @param userConfig - {@link Config}\n */\n async load(userConfig?: Partial): Promise {\n this.state = 'load';\n const timeStamp = now();\n const count = Object.values(this.models.models).filter((model) => model).length;\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (this.env.initial) { // print version info on first run and check for correct backend setup\n if (!await backend.check(this, false)) log('error: backend check failed');\n await tf.ready();\n if (this.env.browser) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n\n await this.models.load(this); // actually loads models\n if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n this.env.initial = false;\n\n const loaded = Object.values(this.models.models).filter((model) => model).length;\n if (loaded !== count) { // number of loaded models changed\n this.models.validate(); // validate kernel ops used by model against current backend\n this.emit('load');\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n }\n\n /** emit event */\n emit = (event: string) => {\n if (this.events?.dispatchEvent) this.events.dispatchEvent(new Event(event));\n };\n\n /** Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result - {@link Result} optional use specific result set to run interpolation on\n * @returns result - {@link Result}\n */\n next(result: Result = this.result): Result {\n return interpolate.calc(result, this.config);\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async warmup(userConfig?: Partial) {\n const t0 = now();\n const res = await warmups.warmup(this, userConfig);\n const t1 = now();\n this.performance.warmup = Math.trunc(t1 - t0);\n return res;\n }\n\n /** Run detect with tensorflow profiling\n * - result object will contain total exeuction time information for top-20 kernels\n * - actual detection object can be accessed via `human.result`\n */\n async profile(input: Input, userConfig?: Partial): Promise<{ kernel: string, time: number, perc: number }[]> {\n // @ts-ignore profile wraps method return values\n const profile = await this.tf.profile(() => this.detect(input, userConfig));\n const kernels: Record = {};\n let total = 0;\n for (const kernel of profile.kernels) { // sum kernel time values per kernel\n const ms = Number(kernel.kernelTimeMs) || 0;\n if (kernels[kernel.name]) kernels[kernel.name] += ms;\n else kernels[kernel.name] = ms;\n total += ms;\n }\n const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n for (const kernel of kernelArr) {\n kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n kernel.time = Math.round(1000 * kernel.time) / 1000;\n }\n kernelArr.sort((a, b) => b.time - a.time); // sort\n kernelArr.length = 20; // crop\n return kernelArr;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input - {@link Input}\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async detect(input: Input, userConfig?: Partial): Promise {\n // detection happens inside a promise\n this.state = 'detect';\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n // sanity checks\n this.state = 'check';\n const error = this.#sanity(input);\n if (error) {\n log(error, input);\n this.emit('error');\n resolve(empty(error));\n }\n\n const timeStart = now();\n\n // load models if enabled\n await this.load();\n\n timeStamp = now();\n this.state = 'image';\n const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor4D };\n this.process = img;\n this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n if (!img.tensor) {\n if (this.config.debug) log('could not convert input to tensor');\n this.emit('error');\n resolve(empty('could not convert input to tensor'));\n return;\n }\n this.emit('image');\n\n timeStamp = now();\n this.config.skipAllowed = await image.skip(this.config, img.tensor);\n this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; // disable autoBrightness on scene change\n if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n (this.performance.totalFrames)++;\n if (this.config.skipAllowed) this.performance.cachedFrames++;\n this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes: FaceResult[] | Promise | never[] = [];\n let bodyRes: BodyResult[] | Promise | never[] = [];\n let handRes: HandResult[] | Promise | never[] = [];\n let objectRes: ObjectResult[] | Promise | never[] = [];\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n this.state = 'detect:face';\n if (this.config.async) {\n faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n\n if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n this.state = 'detect:body';\n const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n if (this.config.async) {\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n timeStamp = now();\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n this.state = 'detect:hand';\n const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n if (this.config.async) {\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n timeStamp = now();\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // run object detection\n this.analyze('Start Object:');\n this.state = 'detect:object';\n if (this.config.async) {\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n timeStamp = now();\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Object:');\n\n // if async wait for results\n this.state = 'detect:await';\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n this.state = 'detect:gesture';\n let gestureRes: GestureResult[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n const shape = this.process.tensor?.shape || [0, 0, 0, 0];\n this.result = {\n face: faceRes as FaceResult[],\n body: bodyRes as BodyResult[],\n hand: handRes as HandResult[],\n gesture: gestureRes,\n object: objectRes as ObjectResult[],\n performance: this.performance,\n canvas: this.process.canvas,\n timestamp: Date.now(),\n error: null,\n width: shape[2],\n height: shape[1],\n get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(img.tensor);\n\n // log('Result:', result);\n this.emit('detect');\n this.state = 'idle';\n resolve(this.result);\n });\n }\n\n /** Helper function\n * @param ms - sleep time in miliseconds\n */\n async sleep(ms: number): Promise { // eslint-disable-line class-methods-use-this\n return new Promise((resolve) => { setTimeout(resolve, ms); });\n }\n\n /** internal structure that keeps track of processed videos @hidden */\n #loops: Record = {};\n /** Continously detect video frames\n * @param element - HTMLVideoElement input\n * @param run - boolean run continously or stop if already running, default true\n * @param delay - number delay detection between frames for number of miliseconds, default 0\n */\n async video(element: HTMLVideoElement, run: boolean = true, delay: number = 0) {\n if (run) {\n if (!this.#loops[element.id]) {\n if (this.config.debug) log('video start', element.id);\n this.#loops[element.id] = true;\n }\n if (!element.paused && this.#loops[element.id] && (element.readyState >= 2)) await this.detect(element);\n if (delay > 0) await this.sleep(delay);\n if (this.#loops[element.id]) requestAnimationFrame(() => this.video(element, run, delay));\n } else {\n if (this.config.debug) log('video stop', element.id);\n this.#loops[element.id] = false;\n }\n }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"], + "mappings": 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className(){return"Adadelta"}constructor(e,t,a=null){super(),this.learningRate=e,this.rho=t,this.epsilon=a,this.accumulatedGrads=[],this.accumulatedUpdates=[],a==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t],r=!1;this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accum_grad`,variable:De(()=>Qa(n).variable(r))}),this.accumulatedUpdates[a]==null&&(this.accumulatedUpdates[a]={originalName:`${t}/accum_var`,variable:De(()=>Qa(n).variable(r))});let s=Array.isArray(e)?e[a].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[a].variable,o=this.accumulatedUpdates[a].variable;De(()=>{let l=we(te(i,this.rho),te(Tn(s),1-this.rho)),u=te(ve(ar(we(o,this.epsilon)),ar(we(i,this.epsilon))),s),p=we(te(o,this.rho),te(Tn(u),1-this.rho));i.assign(l),o.assign(p);let c=we(te(u,-this.learningRate),n);n.assign(c)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(J(this.accumulatedGrads.map(e=>e.variable)),J(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,a=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}},Jg=class extends hs{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t];this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accumulator`,variable:De(()=>or(n.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[a].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[a].variable;De(()=>{let i=we(s,Tn(r));s.assign(i);let o=we(te(ve(r,ar(we(i,L.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&J(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(a=>({originalName:a.name,variable:a.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},Qg=class extends hs{static get className(){return"Adam"}constructor(e,t,a,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=a,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=Ge(t).variable(),this.accBeta2=Ge(a).variable()}),n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);De(()=>{let a=xe(1,this.accBeta1),n=xe(1,this.accBeta2);t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:De(()=>Qa(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:De(()=>Qa(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedSecondMoment[s].variable,c=we(te(u,this.beta1),te(l,1-this.beta1)),d=we(te(p,this.beta2),te(Tn(l),1-this.beta2)),h=ve(c,a),m=ve(d,n);u.assign(c),p.assign(d);let f=we(te(ve(h,we(ar(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(te(this.accBeta1,this.beta1)),this.accBeta2.assign(te(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&J(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&J(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),De(()=>{this.accBeta1.assign(Yl(this.beta1,this.iterations_+1)),this.accBeta2.assign(Yl(this.beta2,this.iterations_+1))});let t=e.length/2,a=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}},e3=class extends hs{static get className(){return"Adamax"}constructor(e,t,a,n=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=a,this.epsilon=n,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=Ge(0).variable(),this.accBeta1=Ge(t).variable()}),n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);De(()=>{let a=xe(1,this.accBeta1),n=ve(-this.learningRate,we(te(this.iteration,this.decay),1));t.forEach((r,s)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}},Qh=class extends hs{static get className(){return"SGD"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=Array.isArray(e)?e[a].tensor:e[t];if(n==null)return;let r=L.registeredVariables[t];De(()=>{let s=we(te(this.c,n),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ln(Ge(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer 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this.constructor.className}static fromConfig(e,t){return new e(t)}},r7=class kl{constructor(){this.classNameMap={}}static getMap(){return kl.instance==null&&(kl.instance=new kl),kl.instance}static register(t){kl.getMap().classNameMap[t.className]=[t,t.fromConfig]}};function s7(e,t,a){F(e.className!=null,()=>"Class being registered does not have the static className property defined."),F(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),F(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),typeof t=="undefined"&&(t="Custom"),typeof a=="undefined"&&(a=e.className);let n=a,r=t+">"+n;return r7.register(e),q_.set(r,e),Y2.set(e,r),e}function X_(e){return Y2.has(e)?Y2.get(e):e.className}var cs=class extends n7{minimize(e,t=!1,a){let{value:n,grads:r}=this.computeGradients(e,a);if(a!=null){let s=a.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(s)}else 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className(){return"Adadelta"}constructor(e,t,a=null){super(),this.learningRate=e,this.rho=t,this.epsilon=a,this.accumulatedGrads=[],this.accumulatedUpdates=[],a==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t],r=!1;this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accum_grad`,variable:De(()=>Qa(n).variable(r))}),this.accumulatedUpdates[a]==null&&(this.accumulatedUpdates[a]={originalName:`${t}/accum_var`,variable:De(()=>Qa(n).variable(r))});let s=Array.isArray(e)?e[a].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[a].variable,o=this.accumulatedUpdates[a].variable;De(()=>{let l=we(te(i,this.rho),te(Tn(s),1-this.rho)),u=te(ve(ar(we(o,this.epsilon)),ar(we(i,this.epsilon))),s),p=we(te(o,this.rho),te(Tn(u),1-this.rho));i.assign(l),o.assign(p);let c=we(te(u,-this.learningRate),n);n.assign(c)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(J(this.accumulatedGrads.map(e=>e.variable)),J(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,a=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}},$g=class extends cs{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=L.registeredVariables[t];this.accumulatedGrads[a]==null&&(this.accumulatedGrads[a]={originalName:`${t}/accumulator`,variable:De(()=>or(n.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[a].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[a].variable;De(()=>{let i=we(s,Tn(r));s.assign(i);let o=we(te(ve(r,ar(we(i,L.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&J(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(a=>({originalName:a.name,variable:a.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},Pg=class extends cs{static get className(){return"Adam"}constructor(e,t,a,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=a,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=Ge(t).variable(),this.accBeta2=Ge(a).variable()}),n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);De(()=>{let a=xe(1,this.accBeta1),n=xe(1,this.accBeta2);t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:De(()=>Qa(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:De(()=>Qa(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedSecondMoment[s].variable,c=we(te(u,this.beta1),te(l,1-this.beta1)),d=we(te(p,this.beta2),te(Tn(l),1-this.beta2)),h=ve(c,a),m=ve(d,n);u.assign(c),p.assign(d);let f=we(te(ve(h,we(ar(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(te(this.accBeta1,this.beta1)),this.accBeta2.assign(te(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&J(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&J(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),De(()=>{this.accBeta1.assign(Pl(this.beta1,this.iterations_+1)),this.accBeta2.assign(Pl(this.beta2,this.iterations_+1))});let t=e.length/2,a=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}},_g=class extends cs{static get className(){return"Adamax"}constructor(e,t,a,n=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=a,this.epsilon=n,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=Ge(0).variable(),this.accBeta1=Ge(t).variable()}),n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);De(()=>{let a=xe(1,this.accBeta1),n=ve(-this.learningRate,we(te(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=L.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Qa(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:Qa(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedWeightedInfNorm[s].variable,c=we(te(u,this.beta1),te(l,1-this.beta1)),d=te(p,this.beta2),h=Za(l),m=pg(d,h);u.assign(c),p.assign(m);let f=we(te(ve(n,a),ve(c,we(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(we(this.iteration,1)),this.accBeta1.assign(te(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&J(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&J(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}},Ph=class extends cs{static get className(){return"SGD"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=Array.isArray(e)?e[a].tensor:e[t];if(n==null)return;let r=L.registeredVariables[t];De(()=>{let s=we(te(this.c,n),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ln(Ge(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer 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implemented`)}},kO=(e,t,a,n=ta)=>{switch(e.op){case"Equal":return[n.equal(k("a",e,t,a),k("b",e,t,a))];case"NotEqual":return[n.notEqual(k("a",e,t,a),k("b",e,t,a))];case"Greater":return[n.greater(k("a",e,t,a),k("b",e,t,a))];case"GreaterEqual":return[n.greaterEqual(k("a",e,t,a),k("b",e,t,a))];case"Less":return[n.less(k("a",e,t,a),k("b",e,t,a))];case"LessEqual":return[n.lessEqual(k("a",e,t,a),k("b",e,t,a))];case"LogicalAnd":return[n.logicalAnd(k("a",e,t,a),k("b",e,t,a))];case"LogicalNot":return[n.logicalNot(k("a",e,t,a))];case"LogicalOr":return[n.logicalOr(k("a",e,t,a),k("b",e,t,a))];case"Select":case"SelectV2":return[n.where(k("condition",e,t,a),k("a",e,t,a),k("b",e,t,a))];case"BitwiseAnd":return[n.bitwiseAnd(k("a",e,t,a),k("b",e,t,a))];default:throw TypeError(`Node type ${e.op} is not 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implemented`)}},RO=(e,t,a,n=ta)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=n.sparse.sparseFillEmptyRows(k("indices",e,t,a),k("values",e,t,a),k("denseShape",e,t,a),k("defaultValue",e,t,a));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=n.sparse.sparseReshape(k("inputIndices",e,t,a),k("inputShape",e,t,a),k("newShape",e,t,a));return[r,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(k("data",e,t,a),k("indices",e,t,a),k("segmentIds",e,t,a))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(k("data",e,t,a),k("indices",e,t,a),k("segmentIds",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},EO=(e,t,a,n=ta)=>{switch(e.op){case"FFT":return[n.fft(k("x",e,t,a))];case"IFFT":return[n.ifft(k("x",e,t,a))];case"RFFT":return[n.rfft(k("x",e,t,a))];case"IRFFT":return[n.irfft(k("x",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},MO=(e,t,a,n=ta)=>{switch(e.op){case"StaticRegexReplace":return[n.string.staticRegexReplace(k("input",e,t,a),k("pattern",e,t,a),k("rewrite",e,t,a),k("replaceGlobal",e,t,a))];case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=n.string.stringNGrams(k("data",e,t,a),k("dataSplits",e,t,a),k("separator",e,t,a),k("nGramWidths",e,t,a),k("leftPad",e,t,a),k("rightPad",e,t,a),k("padWidth",e,t,a),k("preserveShortSequences",e,t,a));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=n.string.stringSplit(k("input",e,t,a),k("delimiter",e,t,a),k("skipEmpty",e,t,a));return[r,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(k("input",e,t,a),k("numBuckets",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},$O=(e,t,a,n=ta)=>{switch(e.op){case"Cast":return[n.cast(k("x",e,t,a),k("dtype",e,t,a))];case"ExpandDims":{let r=k("axis",e,t,a);return[n.expandDims(k("x",e,t,a),r)]}case"Squeeze":{let r=k("axis",e,t,a);return[n.squeeze(k("x",e,t,a),r)]}case"Reshape":return[n.reshape(k("x",e,t,a),k("shape",e,t,a))];case"EnsureShape":return[n.ensureShape(k("x",e,t,a),k("shape",e,t,a))];case"MirrorPad":return[n.mirrorPad(k("x",e,t,a),k("padding",e,t,a),k("mode",e,t,a))];case"PadV2":case"Pad":return[n.pad(k("x",e,t,a),k("padding",e,t,a),k("constantValue",e,t,a))];case"SpaceToBatchND":{let r=k("blockShape",e,t,a),s=k("paddings",e,t,a);return[n.spaceToBatchND(k("x",e,t,a),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,a),s=k("crops",e,t,a);return[n.batchToSpaceND(k("x",e,t,a),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,a),s=k("dataFormat",e,t,a).toUpperCase();return[n.depthToSpace(k("x",e,t,a),r,s)]}case"BroadcastTo":return[n.broadcastTo(k("x",e,t,a),k("shape",e,t,a))];case"BroadcastArgs":return[n.broadcastArgs(k("s0",e,t,a),k("s1",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function f5(e,t,a,n,r=De){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>oO(i,o,l));case"basic_math":return r(()=>lO(i,o,l));case"control":return mO(i,o,l);case"convolution":return r(()=>fO(i,o,l));case"creation":return r(()=>gO(i,o,l));case"dynamic":return yO(i,o,l);case"evaluation":return r(()=>xO(i,o,l));case"image":return r(()=>wO(i,o,l));case"graph":return r(()=>AO(i,o,l));case"logical":return r(()=>kO(i,o,l));case"matrices":return r(()=>IO(i,o,l));case"normalization":return r(()=>SO(i,o,l));case"ragged":return r(()=>CO(i,o,l));case"reduction":return r(()=>TO(i,o,l));case"slice_join":return r(()=>NO(i,o,l));case"sparse":return r(()=>RO(i,o,l));case"spectral":return r(()=>EO(i,o,l));case"string":return r(()=>MO(i,o,l));case"transformation":return r(()=>$O(i,o,l));case"hash_table":return vO(i,o,l,n);case"custom":let u=j7(i.op);if(u&&u.customExecutor)return u.customExecutor(new iO(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,a);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var g5=class{constructor(e={},t={},a={},n={},r){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=a,this.functionMap=n,this.parseNodeNameCache=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function y5(e,t,a,n){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(d=>Ya(d)[0]));n=n||[];let p=new Set(n.map(d=>Ya(d.name)[0])),c=[...t];for(;c.length>0;){let d=c.pop();if((Us(d)||WO(d)||BO(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),a[d.name]==null&&!u.has(d.name)&&!p.has(d.name)){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),c.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function PO(e,t){let{usedNodes:a,inputs:n}=t,r=Object.keys(n).map(g=>Ya(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>a.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(y=>[y.name,y])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),c={};for(let g of u){c[g.name]=c[g.name]||0;for(let y of g.children)i(y)||(c[y.name]=Number.POSITIVE_INFINITY),c[y.name]=(c[y.name]||0)+1}let d=Object.entries(c).filter(([,g])=>g===0).map(([g])=>g),h=[...d];for(;d.length>0;){let g=d.pop(),y=p.get(g);for(let x of y.children.filter(i))--c[x.name]===0&&(h.push(x.name),d.push(x.name))}let m=h.map(g=>p.get(g)),f=_O(m,l);return FO(f,l),f}function _O(e,t){let a=new Map(e.map(s=>[s.name,s])),n=t.map(s=>s.name),r=new Set(n);for(;n.length>0;){let s=n.pop(),i=a.get(s);for(let o of i.children)!a.has(o.name)||r.has(o.name)||(r.add(o.name),n.push(o.name))}return e.filter(s=>r.has(s.name))}var Kc=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function FO(e,t){let a=new Map(e.map((o,l)=>[o.name,l])),n=new Set(t.map(o=>o.name)),r=o=>n.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!a.has(l.name))throw new Kc(`Child ${l.name} of node ${o.name} is unreachable.`);if(a.get(o.name)>a.get(l.name))throw new Kc(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!a.has(l.name))throw new Kc(`Input ${l.name} of node ${o.name} is unreachable.`);if(a.get(l.name)>a.get(o.name))throw new Kc(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function DO(e){let t=new Map(e.map((o,l)=>[o.name,l])),a=Number.MAX_SAFE_INTEGER,n=e.map((o,l)=>Us(o)?a:l),r=o=>{let l=n[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),n[l])),i=new Map;for(let o=0;ot[n].map(r=>r.id));this._weightIds=[].concat(...a),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let a=t.signatureKey||t.name;return t.defaultOutput?`${a}:${t.defaultOutput}`:a})}get functions(){return Object.keys(this._functions).reduce((t,a)=>(t[a]=this._functions[a].signature,t),{})}constructor(t,a){this.graph=t,this.parent=a,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new m6(t.functions[n],this)})}getCompilationKey(t,a){let n=t.map(s=>s.name).sort(),r=a.map(s=>s.name).sort();return n.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(t,a){let n=y5(t,a,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(r.length>0){let u=a.map(c=>c.name),p=Object.keys(t);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${p}]. Missing the following inputs: [${r}]`)}let o=PO(this.graph,n),l=DO(o);return{orderedNodes:o,nodeLiveUntilMap:l}}cloneAndKeepTensor(t){if(t==null)return null;let a=t.clone();return Ln(a),a}cloneTensorList(t){return t?t.map(a=>this.cloneAndKeepTensor(a)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([a,n])=>[a,this.cloneTensorList(n)]))}execute(t,a){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),a=this.mapOutputs(a),this.checkOutputs(a);let r=n.map(d=>this.graph.nodes[Ya(d)[0]]),s=a.map(d=>Ya(d)[0]),i=new Set(s),o=s.map(d=>this.graph.nodes[d]);o.length===0&&(o=this._outputs);let l=this.getCompilationKey(r,o),u=this.compiledMap.get(l);u==null&&(u=this.compile(t,o),this.compiledMap.set(l,u));try{this.keepIntermediateTensors=B().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let p={},c={};return De(()=>{let d=new g5(this.weightMap,p,c,this.functionExecutorMap,this.parseNodeNameCache),h=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(y=>{let[x,A]=Ya(y,d),b=[];b[A]=t[y],h[x]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x]=this.cloneTensorList(b))});let m=this.getFrozenTensorIds(h),{orderedNodes:f,nodeLiveUntilMap:g}=u;for(let y of f){if(h[y.name])continue;let x=f5(y,h,d,this._resourceManager);if(v.isPromise(x))throw new Error(`The execution of the op '${y.op}' returned a promise. Please use model.executeAsync() instead.`);h[y.name]=x,this.keepIntermediateTensors&&(this.clonedTensorsMap[y.name]=this.cloneTensorList(x)),this.checkTensorForDisposalWithNodeLiveUntilInfo(y,h,d,m,i,g.get(y.name))}return this.parent==null&&d.dispose(m),a.map(y=>da(y,h,d))})}getFrozenTensorIds(t){let a=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(r=>r.id)));return new Set(a)}checkTensorForDisposal(t,a,n,r,s,i,o){if(!(Us(a)||i.has(t))){for(let l of n[t])l!=null&&(o[l.id]=(o[l.id]||0)+a.children.length);for(let l of a.inputs){if(Us(l))continue;let u=d5(l.name,n,r);if(u!=null)for(let p of u){if(!p||p.kept||s.has(p.id))continue;let c=o[p.id];c===1?(p.dispose(),delete o[p.id]):c!=null&&o[p.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,a,n,r,s,i){function o(l){return Us(l)||s.has(l.name)}if(!(Us(t)||i==null))for(let l of i){if(o(l))continue;let u=d5(l.name,a,n);for(let p of u)!p||p.kept||r.has(p.id)||p.dispose()}}async executeAsync(t,a){return this._executeAsync(t,a)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let a of t)a&&!a.isDisposed&&a.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,a,n=!1,r={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),a=this.mapOutputs(a),this.checkOutputs(a));try{this.keepIntermediateTensors=B().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let i=new g5(this.weightMap,r,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let o=await this.executeWithControlFlow(t,i,a,n),l=a.map(d=>da(d,o,i)),u=l.map(d=>d.id),p=Object.keys(t).map(d=>t[d].id),c=new Set([...u,...p,...this.weightIds]);return Object.values(o).forEach(d=>{d.forEach(h=>{h&&!h.isDisposed&&!c.has(h.id)&&h.dispose()})}),this.parent==null&&i.dispose(c),l}async executeFunctionAsync(t,a,n){let r=t.reduce((s,i,o)=>(s[this.inputs[o].name]=i,s),{});return this._executeAsync(r,this.outputNodes,!0,a,n)}async executeWithControlFlow(t,a,n,r){let s=Object.keys(t),i=s.map(b=>this.graph.nodes[Ya(b)[0]]),o=n.map(b=>Ya(b)[0]),l=new Set(o),u=o.map(b=>this.graph.nodes[b]);u.length===0&&(u=this._outputs);let{usedNodes:p,missingInputs:c,dynamicNode:d,syncInputs:h}=y5(t,u,this.weightMap,this._initNodes),m=[...i,...this.graph.weights,...this._initNodes||[]].map(b=>({node:b,contexts:a.currentContext})),f=Object.assign({},this.weightMap);Object.keys(t).forEach(b=>{let[w,I]=Ya(b),T=[];T[I]=t[b],f[w]=T});let g={},y=this.getFrozenTensorIds(f),x={};for(;m.length>0;){let b=this.processStack(i,m,a,f,x,y,l,g,p);await Promise.all(b)}d==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let A=u.filter(b=>!Us(b)&&!da(b.name,f,a)).map(b=>b.name);if(A.length>0){let b="";throw d!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${A}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${b}`)}return f}processStack(t,a,n,r,s,i,o,l,u){let p=[];for(;a.length>0;){let c=a.pop();n.currentContext=c.contexts;let d="";if(c.node.op==="Enter"&&k("isConstant",c.node,r,n)&&([d]=vr(c.node.name,n)),r[c.node.name]==null){let h=f5(c.node,r,n,this._resourceManager);d||([d]=vr(c.node.name,n));let m=n.currentContext;v.isPromise(h)?p.push(h.then(f=>(r[d]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(f)),n.currentContext=m,this.checkTensorForDisposal(d,c.node,r,n,i,o,l),this.processChildNodes(c.node,a,n,r,s,u),f))):(r[d]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(h)),this.checkTensorForDisposal(d,c.node,r,n,i,o,l),this.processChildNodes(c.node,a,n,r,s,u))}else this.processChildNodes(c.node,a,n,r,s,u)}return p}processChildNodes(t,a,n,r,s,i){t.children.forEach(o=>{let[l]=vr(o.name,n);s[l]||!i.has(o.name)||(o.op==="Merge"?o.inputNames.some(u=>!!da(u,r,n))&&(s[l]=!0,a.push({contexts:n.currentContext,node:o})):o.inputNames.every(u=>!!da(u,r,n))&&(s[l]=!0,a.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(a=>a.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(a=>{let n=t[a],[r]=Ya(a),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,o=i.length===n.shape.length&&n.shape.every((l,u)=>i[u]===-1||i[u]===l);v.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&v.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var a,n;let r={};for(let s in t){let i=(n=(a=this._signature)===null||a===void 0?void 0:a.inputs)===null||n===void 0?void 0:n[s];i!=null?r[i.name]=t[s]:r[s]=t[s]}return r}checkInputs(t){let a=Object.keys(t).filter(n=>{let[r]=Ya(n);return this.graph.nodes[r]==null});if(a.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${a}] that are not part of graph`)}mapOutputs(t){return t.map(a=>{var n,r;let s=(r=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||r===void 0?void 0:r[a];return s!=null?s.name:a},{})}checkOutputs(t){t.forEach(a=>{let[n]=Ya(a);if(!this.graph.nodes[n])throw new Error(`The output '${a}' is not found in the graph`)})}},VO=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},UO="?tfjs-format=file",GO="model.json",Xp=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},a=Zn){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=a,t==null&&(this.loadOptions={}),this.resourceManager=new VO}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>t.getWeightStream==null?this.loadSync(t):this.loadStreaming(t)):this.loadSync(e)}loadSync(e){let t=this.io.decodeWeights(e.weightData,e.weightSpecs);return this.loadWithWeightMap(e,t)}async loadStreaming(e){if(e.getWeightStream==null)throw new Error("Model artifacts missing streamWeights function");let t=await jA(e.getWeightStream(),e.weightSpecs);return this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let a=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(n=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}if(this.signature=n,this.version=`${a.versions.producer}.${a.versions.minConsumer}`,this.executor=new x5(p5.Instance.transformGraph(a,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=p5.Instance.transformGraph(e.modelInitializer);this.initializer=new x5(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let a=this.io.getSaveHandlers(e);if(a.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(a.length>1)throw new Error(`Found more than one (${a.length}) save handlers for URL '${e}'`);e=a[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof yt?[e]:e,a={};return t.forEach((n,r)=>a[this.structuredOutputKeys[r]]=n),a}return e}predict(e,t){let a=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(a)}async predictAsync(e,t){let a=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(a)}normalizeInputs(e){var t;if(!(e instanceof yt)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let 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this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,a=Object.keys(t);for(let n=0;n1?a:a[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let a=await this.executor.executeAsync(e,t);return a.length>1?a:a[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return 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p=u;u=await a.functionMap[n].executeFunctionAsync(u,a.tensorArrayMap,a.tensorListMap);let c=u.map(h=>h.id);p.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&c.indexOf(h.id)===-1&&h.dispose()});let d=await a.functionMap[r].executeFunctionAsync(u,a.tensorArrayMap,a.tensorListMap);l=await d[0].data(),d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&c.indexOf(h.id)===-1&&h.dispose()})}return u}case"LoopCond":{let n=k("pred",e,t,a);return[wr(n)]}case"Switch":{let n=k("pred",e,t,a),r=k("data",e,t,a);return r.kept||(r=wr(r)),(await n.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let n=e.inputNames.find(r=>da(r,t,a)!==void 0);if(n){let r=da(n,t,a);return[wr(r)]}return}case"Enter":{let n=k("frameName",e,t,a),r=k("tensor",e,t,a);return a.enterFrame(n),[wr(r)]}case"Exit":{let n=k("tensor",e,t,a);return a.exitFrame(),[wr(n)]}case"NextIteration":{let n=k("tensor",e,t,a);return a.nextIteration(),[wr(n)]}case"TensorArrayV3":{let n=k("size",e,t,a),r=k("dtype",e,t,a),s=k("elementShape",e,t,a),i=k("dynamicSize",e,t,a),o=k("clearAfterRead",e,t,a),l=k("identicalElementShapes",e,t,a),u=k("name",e,t,a),p=new uO(u,r,n,s,l,i,o);return a.addTensorArray(p),[p.idTensor,Ge(1)]}case"TensorArrayWriteV3":{let n=k("tensorArrayId",e,t,a),r=k("index",e,t,a),s=k("tensor",e,t,a),i=a.getTensorArray(n.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let n=k("tensorArrayId",e,t,a),r=k("index",e,t,a);return[a.getTensorArray(n.id).read(r)]}case"TensorArrayGatherV3":{let n=k("tensorArrayId",e,t,a),r=k("indices",e,t,a),s=k("dtype",e,t,a);return[a.getTensorArray(n.id).gather(r,s)]}case"TensorArrayScatterV3":{let n=k("tensorArrayId",e,t,a),r=k("indices",e,t,a),s=k("tensor",e,t,a),i=a.getTensorArray(n.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let n=k("tensorArrayId",e,t,a),r=a.getTensorArray(n.id),s=k("dtype",e,t,a);return[r.concat(s)]}case"TensorArraySplitV3":{let n=k("tensorArrayId",e,t,a),r=k("tensor",e,t,a),s=k("lengths",e,t,a),i=a.getTensorArray(n.id);return i.split(s,r),[i.idTensor]}case"TensorArraySizeV3":{let n=k("tensorArrayId",e,t,a),r=a.getTensorArray(n.id);return[Ge(r.size(),"int32")]}case"TensorArrayCloseV3":{let n=k("tensorArrayId",e,t,a),r=a.getTensorArray(n.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let n=k("tensorListId",e,t,a),r=k("index",e,t,a),s=k("tensor",e,t,a),i=a.getTensorList(n.id);return i.setItem(r,s),[i.idTensor]}case"TensorListGetItem":{let n=k("tensorListId",e,t,a),r=k("index",e,t,a),s=k("elementShape",e,t,a),i=k("elementDType",e,t,a);return[a.getTensorList(n.id).getItem(r,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let n=k("indices",e,t,a),r=k("tensor",e,t,a),s=k("elementShape",e,t,a),i=k("numElements",e,t,a),o=cO(r,n,s,i);return a.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let n=k("elementShape",e,t,a),r=k("elementDType",e,t,a),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=k(s,e,t,a),o=e.op==="TensorListReserve"?-1:i,l=pO(n,r,i,o);return a.addTensorList(l),[l.idTensor]}case"TensorListGather":{let n=k("tensorListId",e,t,a),r=k("indices",e,t,a),s=k("elementShape",e,t,a),i=k("elementDType",e,t,a);return[a.getTensorList(n.id).gather(r,i,s)]}case"TensorListStack":{let n=k("tensorListId",e,t,a),r=k("elementShape",e,t,a),s=k("elementDType",e,t,a),i=k("numElements",e,t,a);return[a.getTensorList(n.id).stack(r,s,i)]}case"TensorListFromTensor":{let n=k("tensor",e,t,a),r=k("elementShape",e,t,a),s=k("elementDType",e,t,a),i=dO(n,r,s);return a.addTensorList(i),[i.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let n=k("tensorListId",e,t,a),r=a.getTensorList(n.id),s=k("dtype",e,t,a),i=k("elementShape",e,t,a);return[r.concat(s,i)]}case"TensorListPushBack":{let n=k("tensorListId",e,t,a),r=k("tensor",e,t,a),s=a.getTensorList(n.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let n=k("tensorListId",e,t,a),r=k("elementShape",e,t,a),s=k("elementDType",e,t,a);return[a.getTensorList(n.id).popBack(r,s)]}case"TensorListSplit":{let n=k("tensor",e,t,a),r=k("elementShape",e,t,a),s=k("lengths",e,t,a),i=hO(n,s,r);return a.addTensorList(i),[i.idTensor]}case"TensorListLength":{let n=k("tensorListId",e,t,a),r=a.getTensorList(n.id);return[Ge(r.size(),"int32")]}case"TensorListResize":{let n=k("tensorListId",e,t,a),r=k("size",e,t,a),s=a.getTensorList(n.id).resize(r);return a.addTensorList(s),[s.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Xx(e,t,a){let[n,r]=k("fusedOps",e,t,a),s=n==="biasadd",i=!s,o=r==="prelu",l=n==="fusedbatchnorm",u=k("numArgs",e,t,a);if(s){if(o&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one 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r=k("strides",e,t,a),s=Dc(e,t,a),i=k("dataFormat",e,t,a).toUpperCase(),o=k("dilations",e,t,a);return[n.conv2d(k("x",e,t,a),k("filter",e,t,a),[r[1],r[2]],s,i,[o[1],o[2]])]}case"_FusedConv2D":{let{stride:r,pad:s,dataFormat:i,dilations:o,biasArg:l,preluArg:u,activationFunc:p,leakyreluAlpha:c}=Xx(e,t,a);return[n.fused.conv2d({x:k("x",e,t,a),filter:k("filter",e,t,a),strides:[r[1],r[2]],pad:s,dataFormat:i,dilations:[o[1],o[2]],bias:l,activation:p,preluActivationWeights:u,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:s,dataFormat:i,dilations:o,biasArg:l,preluArg:u,activationFunc:p,leakyreluAlpha:c}=Xx(e,t,a);return[n.fused.depthwiseConv2d({x:k("x",e,t,a),filter:k("filter",e,t,a),strides:[r[1],r[2]],pad:s,dataFormat:i,dilations:[o[1],o[2]],bias:l,activation:p,preluActivationWeights:u,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let r=k("outputShape",e,t,a),s=k("strides",e,t,a),i=Dc(e,t,a);return[n.conv2dTranspose(k("x",e,t,a),k("filter",e,t,a),r,[s[1],s[2]],i)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=k("strides",e,t,a),s=Dc(e,t,a),i=k("dilations",e,t,a),o=k("dataFormat",e,t,a).toUpperCase();return[n.depthwiseConv2d(k("input",e,t,a),k("filter",e,t,a),[r[1],r[2]],s,o,[i[1],i[2]])]}case"Conv3D":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("dataFormat",e,t,a).toUpperCase(),o=k("dilations",e,t,a);return[n.conv3d(k("x",e,t,a),k("filter",e,t,a),[r[1],r[2],r[3]],s,i,[o[1],o[2],o[3]])]}case"AvgPool":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("kernelSize",e,t,a);return[n.avgPool(k("x",e,t,a),[i[1],i[2]],[r[1],r[2]],s)]}case"MaxPool":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("kernelSize",e,t,a);return[n.maxPool(k("x",e,t,a),[i[1],i[2]],[r[1],r[2]],s)]}case"MaxPoolWithArgmax":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("kernelSize",e,t,a),o=k("includeBatchInIndex",e,t,a),{result:l,indexes:u}=n.maxPoolWithArgmax(k("x",e,t,a),[i[1],i[2]],[r[1],r[2]],s,o);return[l,u]}case"AvgPool3D":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("kernelSize",e,t,a);return[n.avgPool3d(k("x",e,t,a),[i[1],i[2],i[3]],[r[1],r[2],r[3]],s)]}case"MaxPool3D":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("kernelSize",e,t,a);return[n.maxPool3d(k("x",e,t,a),[i[1],i[2],i[3]],[r[1],r[2],r[3]],s)]}case"Dilation2D":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("dilations",e,t,a),o=r[1],l=r[2],u=i[1],p=i[2];return[n.dilation2d(k("x",e,t,a),k("filter",e,t,a),[o,l],s,[u,p],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},gO=(e,t,a,n=ta)=>{switch(e.op){case"Fill":{let r=k("shape",e,t,a),s=k("dtype",e,t,a),i=k("value",e,t,a);return[n.fill(r,i,s)]}case"LinSpace":{let r=k("start",e,t,a),s=k("stop",e,t,a),i=k("num",e,t,a);return[n.linspace(r,s,i)]}case"Multinomial":{let 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r=k("shape",e,t,a),s=k("mean",e,t,a),i=k("stdDev",e,t,a),o=k("seed",e,t,a);return[n.truncatedNormal(r,s,i,k("dtype",e,t,a),o)]}case"Zeros":return[n.zeros(k("shape",e,t,a),k("dtype",e,t,a))];case"ZerosLike":return[n.zerosLike(k("x",e,t,a))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function M2(e,t,a){let n=k("boxes",e,t,a),r=k("scores",e,t,a),s=k("maxOutputSize",e,t,a),i=k("iouThreshold",e,t,a),o=k("scoreThreshold",e,t,a),l=k("softNmsSigma",e,t,a);return{boxes:n,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var yO=async(e,t,a,n,r=ta)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:i,maxOutputSize:o,iouThreshold:l,scoreThreshold:u,softNmsSigma:p}=M2(e,t,a),c=await r.image.nonMaxSuppressionWithScoreAsync(s,i,o,l,u,p);return[c.selectedIndices,c.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:s,scores:i,maxOutputSize:o,iouThreshold:l,scoreThreshold:u}=M2(e,t,a),p=k("padToMaxOutputSize",e,t,a),c=await 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s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>oO(i,o,l));case"basic_math":return r(()=>lO(i,o,l));case"control":return mO(i,o,l);case"convolution":return r(()=>fO(i,o,l));case"creation":return r(()=>gO(i,o,l));case"dynamic":return yO(i,o,l);case"evaluation":return r(()=>xO(i,o,l));case"image":return r(()=>wO(i,o,l));case"graph":return r(()=>AO(i,o,l));case"logical":return r(()=>kO(i,o,l));case"matrices":return r(()=>IO(i,o,l));case"normalization":return r(()=>SO(i,o,l));case"ragged":return r(()=>CO(i,o,l));case"reduction":return r(()=>TO(i,o,l));case"slice_join":return r(()=>NO(i,o,l));case"sparse":return r(()=>RO(i,o,l));case"spectral":return r(()=>EO(i,o,l));case"string":return r(()=>MO(i,o,l));case"transformation":return r(()=>$O(i,o,l));case"hash_table":return vO(i,o,l,n);case"custom":let u=C7(i.op);if(u&&u.customExecutor)return u.customExecutor(new iO(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. 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t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Zx(e,t,a,n){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(d=>Ya(d)[0]));n=n||[];let p=new Set(n.map(d=>Ya(d.name)[0])),c=[...t];for(;c.length>0;){let d=c.pop();if((Vs(d)||WO(d)||BO(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),a[d.name]==null&&!u.has(d.name)&&!p.has(d.name)){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),c.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function PO(e,t){let{usedNodes:a,inputs:n}=t,r=Object.keys(n).map(g=>Ya(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>a.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(y=>[y.name,y])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),c={};for(let g of u){c[g.name]=c[g.name]||0;for(let y of g.children)i(y)||(c[y.name]=Number.POSITIVE_INFINITY),c[y.name]=(c[y.name]||0)+1}let d=Object.entries(c).filter(([,g])=>g===0).map(([g])=>g),h=[...d];for(;d.length>0;){let g=d.pop(),y=p.get(g);for(let x of y.children.filter(i))--c[x.name]===0&&(h.push(x.name),d.push(x.name))}let m=h.map(g=>p.get(g)),f=_O(m,l);return FO(f,l),f}function _O(e,t){let a=new Map(e.map(s=>[s.name,s])),n=t.map(s=>s.name),r=new Set(n);for(;n.length>0;){let s=n.pop(),i=a.get(s);for(let o of i.children)!a.has(o.name)||r.has(o.name)||(r.add(o.name),n.push(o.name))}return e.filter(s=>r.has(s.name))}var Rc=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function FO(e,t){let a=new Map(e.map((o,l)=>[o.name,l])),n=new Set(t.map(o=>o.name)),r=o=>n.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!a.has(l.name))throw new Rc(`Child ${l.name} of node ${o.name} is unreachable.`);if(a.get(o.name)>a.get(l.name))throw new Rc(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!a.has(l.name))throw new Rc(`Input ${l.name} of node ${o.name} is unreachable.`);if(a.get(l.name)>a.get(o.name))throw new Rc(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function DO(e){let t=new Map(e.map((o,l)=>[o.name,l])),a=Number.MAX_SAFE_INTEGER,n=e.map((o,l)=>Vs(o)?a:l),r=o=>{let l=n[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),n[l])),i=new Map;for(let o=0;ot[n].map(r=>r.id));this._weightIds=[].concat(...a),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let a=t.signatureKey||t.name;return t.defaultOutput?`${a}:${t.defaultOutput}`:a})}get functions(){return Object.keys(this._functions).reduce((t,a)=>(t[a]=this._functions[a].signature,t),{})}constructor(t,a){this.graph=t,this.parent=a,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new X7(t.functions[n],this)})}getCompilationKey(t,a){let n=t.map(s=>s.name).sort(),r=a.map(s=>s.name).sort();return n.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(t,a){let n=Zx(t,a,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(r.length>0){let u=a.map(c=>c.name),p=Object.keys(t);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${p}]. Missing the following inputs: [${r}]`)}let o=PO(this.graph,n),l=DO(o);return{orderedNodes:o,nodeLiveUntilMap:l}}cloneAndKeepTensor(t){if(t==null)return null;let a=t.clone();return Ln(a),a}cloneTensorList(t){return t?t.map(a=>this.cloneAndKeepTensor(a)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([a,n])=>[a,this.cloneTensorList(n)]))}execute(t,a){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),a=this.mapOutputs(a),this.checkOutputs(a);let r=n.map(d=>this.graph.nodes[Ya(d)[0]]),s=a.map(d=>Ya(d)[0]),i=new Set(s),o=s.map(d=>this.graph.nodes[d]);o.length===0&&(o=this._outputs);let l=this.getCompilationKey(r,o),u=this.compiledMap.get(l);u==null&&(u=this.compile(t,o),this.compiledMap.set(l,u));try{this.keepIntermediateTensors=B().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let p={},c={};return De(()=>{let d=new Yx(this.weightMap,p,c,this.functionExecutorMap,this.parseNodeNameCache),h=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(y=>{let[x,A]=Ya(y,d),b=[];b[A]=t[y],h[x]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x]=this.cloneTensorList(b))});let m=this.getFrozenTensorIds(h),{orderedNodes:f,nodeLiveUntilMap:g}=u;for(let y of f){if(h[y.name])continue;let x=Kx(y,h,d,this._resourceManager);if(v.isPromise(x))throw new Error(`The execution of the op '${y.op}' returned a promise. Please use model.executeAsync() instead.`);h[y.name]=x,this.keepIntermediateTensors&&(this.clonedTensorsMap[y.name]=this.cloneTensorList(x)),this.checkTensorForDisposalWithNodeLiveUntilInfo(y,h,d,m,i,g.get(y.name))}return this.parent==null&&d.dispose(m),a.map(y=>da(y,h,d))})}getFrozenTensorIds(t){let a=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(r=>r.id)));return new Set(a)}checkTensorForDisposal(t,a,n,r,s,i,o){if(!(Vs(a)||i.has(t))){for(let l of n[t])l!=null&&(o[l.id]=(o[l.id]||0)+a.children.length);for(let l of a.inputs){if(Vs(l))continue;let u=Gx(l.name,n,r);if(u!=null)for(let p of u){if(!p||p.kept||s.has(p.id))continue;let c=o[p.id];c===1?(p.dispose(),delete o[p.id]):c!=null&&o[p.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,a,n,r,s,i){function o(l){return Vs(l)||s.has(l.name)}if(!(Vs(t)||i==null))for(let l of i){if(o(l))continue;let u=Gx(l.name,a,n);for(let p of u)!p||p.kept||r.has(p.id)||p.dispose()}}async executeAsync(t,a){return this._executeAsync(t,a)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let a of t)a&&!a.isDisposed&&a.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,a,n=!1,r={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),a=this.mapOutputs(a),this.checkOutputs(a));try{this.keepIntermediateTensors=B().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let i=new Yx(this.weightMap,r,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let o=await this.executeWithControlFlow(t,i,a,n),l=a.map(d=>da(d,o,i)),u=l.map(d=>d.id),p=Object.keys(t).map(d=>t[d].id),c=new Set([...u,...p,...this.weightIds]);return Object.values(o).forEach(d=>{d.forEach(h=>{h&&!h.isDisposed&&!c.has(h.id)&&h.dispose()})}),this.parent==null&&i.dispose(c),l}async executeFunctionAsync(t,a,n){let r=t.reduce((s,i,o)=>(s[this.inputs[o].name]=i,s),{});return this._executeAsync(r,this.outputNodes,!0,a,n)}async executeWithControlFlow(t,a,n,r){let s=Object.keys(t),i=s.map(b=>this.graph.nodes[Ya(b)[0]]),o=n.map(b=>Ya(b)[0]),l=new Set(o),u=o.map(b=>this.graph.nodes[b]);u.length===0&&(u=this._outputs);let{usedNodes:p,missingInputs:c,dynamicNode:d,syncInputs:h}=Zx(t,u,this.weightMap,this._initNodes),m=[...i,...this.graph.weights,...this._initNodes||[]].map(b=>({node:b,contexts:a.currentContext})),f=Object.assign({},this.weightMap);Object.keys(t).forEach(b=>{let[w,I]=Ya(b),T=[];T[I]=t[b],f[w]=T});let g={},y=this.getFrozenTensorIds(f),x={};for(;m.length>0;){let b=this.processStack(i,m,a,f,x,y,l,g,p);await Promise.all(b)}d==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let A=u.filter(b=>!Vs(b)&&!da(b.name,f,a)).map(b=>b.name);if(A.length>0){let b="";throw d!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${A}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${b}`)}return f}processStack(t,a,n,r,s,i,o,l,u){let p=[];for(;a.length>0;){let c=a.pop();n.currentContext=c.contexts;let d="";if(c.node.op==="Enter"&&k("isConstant",c.node,r,n)&&([d]=vr(c.node.name,n)),r[c.node.name]==null){let h=Kx(c.node,r,n,this._resourceManager);d||([d]=vr(c.node.name,n));let m=n.currentContext;v.isPromise(h)?p.push(h.then(f=>(r[d]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(f)),n.currentContext=m,this.checkTensorForDisposal(d,c.node,r,n,i,o,l),this.processChildNodes(c.node,a,n,r,s,u),f))):(r[d]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(h)),this.checkTensorForDisposal(d,c.node,r,n,i,o,l),this.processChildNodes(c.node,a,n,r,s,u))}else this.processChildNodes(c.node,a,n,r,s,u)}return p}processChildNodes(t,a,n,r,s,i){t.children.forEach(o=>{let[l]=vr(o.name,n);s[l]||!i.has(o.name)||(o.op==="Merge"?o.inputNames.some(u=>!!da(u,r,n))&&(s[l]=!0,a.push({contexts:n.currentContext,node:o})):o.inputNames.every(u=>!!da(u,r,n))&&(s[l]=!0,a.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(a=>a.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(a=>{let n=t[a],[r]=Ya(a),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,o=i.length===n.shape.length&&n.shape.every((l,u)=>i[u]===-1||i[u]===l);v.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&v.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var a,n;let r={};for(let s in t){let i=(n=(a=this._signature)===null||a===void 0?void 0:a.inputs)===null||n===void 0?void 0:n[s];i!=null?r[i.name]=t[s]:r[s]=t[s]}return r}checkInputs(t){let a=Object.keys(t).filter(n=>{let[r]=Ya(n);return this.graph.nodes[r]==null});if(a.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${a}] that are not part of graph`)}mapOutputs(t){return t.map(a=>{var n,r;let s=(r=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||r===void 0?void 0:r[a];return s!=null?s.name:a},{})}checkOutputs(t){t.forEach(a=>{let[n]=Ya(a);if(!this.graph.nodes[n])throw new Error(`The output '${a}' is not found in the graph`)})}},VO=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},UO="?tfjs-format=file",GO="model.json",Np=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},a=Zn){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=a,t==null&&(this.loadOptions={}),this.resourceManager=new VO}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>t.getWeightStream==null?this.loadSync(t):this.loadStreaming(t)):this.loadSync(e)}loadSync(e){let t=this.io.decodeWeights(e.weightData,e.weightSpecs);return this.loadWithWeightMap(e,t)}async loadStreaming(e){if(e.getWeightStream==null)throw new Error("Model artifacts missing streamWeights function");let t=await CA(e.getWeightStream(),e.weightSpecs);return this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let a=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(n=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}if(this.signature=n,this.version=`${a.versions.producer}.${a.versions.minConsumer}`,this.executor=new Jx(Hx.Instance.transformGraph(a,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Hx.Instance.transformGraph(e.modelInitializer);this.initializer=new Jx(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let a=this.io.getSaveHandlers(e);if(a.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(a.length>1)throw new Error(`Found more than one (${a.length}) save handlers for URL '${e}'`);e=a[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof yt?[e]:e,a={};return t.forEach((n,r)=>a[this.structuredOutputKeys[r]]=n),a}return e}predict(e,t){let a=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(a)}async predictAsync(e,t){let a=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(a)}normalizeInputs(e){var t;if(!(e instanceof yt)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let a=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+a!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-a} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[n++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return 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Object.keys(e).reduce((t,a)=>(t[a]=[e[a]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&J(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function Gg(e,t={},a=Zn){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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i}calculateOutputIndexValueRowID(t,a,n,r){let s=t.length,i=[];if(s===0)return[];let o=0,l=t[0];if(l>=a.length)throw new Error(`Got currentValueRowId=${l}, which is not less than ${a.length}`);let u=a[l];i.push(u);for(let p=1;p=0&&(++o,o=a.length)throw new Error(`Got nextValueRowId=${c} which is not less than ${a.length}`);u=a[c]}i.push(u)}if(i.length!==t.length)throw new Error("Invalid row ids.");return i}calculateOutputIndex(t,a,n,r){let s=this.getRowPartitionTensor(t),i=this.getRowPartitionTypeByDimension(t);switch(i){case Sn.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(s,a,n,r);case Sn.ROW_SPLITS:if(s.length-1>a.length)throw new Error(`Row partition size is greater than output size: ${s.length-1} > ${a.length}`);return this.calculateOutputIndexRowSplit(s,a,n,r);default:throw new Error(`Unsupported partition type: ${Sn[i]}`)}}getFirstDimensionSize(){let t=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error("No row_partition_types given.");let 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Tensor requires at least one element.");let t=this.getFirstDimensionSize(),a=this.calculateOutputSize(t),n=new Array(this.raggedRank+1);n[n.length-1]=1;for(let i=n.length-2;i>=0;--i)n[i]=n[i+1]*a[i+1];let r=w5(a,!1),s=v.getArrayFromDType(this.valuesDType,v.sizeFromShape(r));if(n[0]*a[0]>0){let i=this.calculateFirstParentOutputIndex(t,n[0],a[0]);for(let o=1;o<=this.raggedRank;++o)i=this.calculateOutputIndex(o-1,i,n[o],a[o]);this.setOutput(this.raggedRank,i,s,r)}return[r,s]}setOutput(t,a,n,r){if(n.length===0)return;let s=this.values,i=n,o=r.slice();o=o.slice(t+1);let l=v.sizeFromShape(o),u=a.length,p=this.defaultValue;if(p.length!==l&&p.length!==1){let m=this.defaultValueShape;De(()=>{let f=Q(p,m);p=Gl(f,o).dataSync()})}let c=0,d=0,h=0;for(let m=0;m<=u;++m){let f=m=u){let g=n.length;f=Math.floor(g/l)}if(f>h)if(this.defaultValue.length===1)i.subarray(h*l,f*l).fill(this.defaultValue[0]),h=f;else for(;f>h;){let g=i.slice(h*l);v5(g,p,l),++h}f<0?(c=m+1,d=h):(c=m,d=h,h=d+1)}}};function v5(e,t,a){for(let n=0;n= 0`);if(n<-1)throw new Error(`Dimension ${n} must be >= -1`);n=-1}a.push(n)}return a}function H6(e,t,a,n,r,s,i,o,l,u){return new Gz(e,t,a,n,r,s,i,o,l,u).compute()}function y3(e,t,a,n){let r=e===t,s=e1;if(r||s||i)return v.makeZerosTypedArray(0,n);let o=Math.abs(Math.ceil((t-e)/a)),l=v.makeZerosTypedArray(o,n);t1/Math.sqrt(e)),Hz=ms(Po,j6),jz={kernelName:Po,backendName:"cpu",kernelFunc:Hz};function qs(e,t,a,n,r,s,i,o,l,u){let p=[n/r,r],c=e.values,d=t.values;if(n===0)return _e(a,t.dtype);let h=l instanceof Vt?l:_e(p,t.dtype);typeof l=="string"||typeof l=="number"?h.values.fill(l):typeof l=="boolean"&&h.values.fill(+l);for(let m=0;m=n/r)throw new Error(`Invalid indices: ${f} does not index into ${a}`);for(let y=0;y1/(1+Math.exp(-e))),q6=ct(Bo,e=>1/(1+Math.exp(-e))),Xz={kernelName:Bo,backendName:"cpu",kernelFunc:q6};function Ah(e,t,a,n,r){let s=Nt.isSliceContinous(n,t,a),i=v.sizeFromShape(a),o=v.computeStrides(n);if(s){let c=Nt.computeFlatOffset(t,o);return r==="string"?e.slice(c,c+i):e.subarray(c,c+i)}let l=r==="string"?C.fromUint8ToStringArray(e):e,u=_e(n,r,l),p=_e(a,r);for(let c=0;cm+t[f]);p.set(u.get(...h),...d)}return r==="string"?C.fromStringArrayToUint8(p.values):p.values}function ti(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n;Ie(r,"slice");let[o,l]=Nt.parseSliceParams(r,s,i);Nt.assertParamsValid(r,o,l);let u=a.data.get(r.dataId).values,p=Ah(u,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,p)}var Kz={kernelName:_u,backendName:"cpu",kernelFunc:ti};function X6(e,t,a,n,r,s,i){let o=t[0],l=s[0],u=new Array(l),p=new Array(o),c=t[1];if(l===0){if(o!==0)throw new Error(C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(o));let g=v.getArrayFromDType(a,0),y=v.getArrayFromDType(r,0);return[g,[0,c],y,u,p]}let d=!0,h=0,m=new Array(l).fill(0);for(let g=0;g=l)throw new Error(C.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g,y,l));++m[y],d=d&&y>=h,h=y}let f=!0;for(let g=0;g0&&(m[g]+=m[g-1])}if(f&&d){let g=e,y=n;for(let x=0;x0){d[c-1]=1;for(let f=c-2;f>=0;--f)d[f]=d[f+1]*n[f+1]}let h=[];if(o>0){h[o-1]=1;for(let f=o-2;f>=0;--f)h[f]=h[f+1]*l[f+1]}let m=v.getArrayFromDType(a,i*o);for(let f=0;f0?r[o-1]+1:0;if(p<0)throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let c=t.slice();c[0]=p;let d=c.reduce((x,A)=>x*A,1),h=v.getArrayFromDType(a,d);if(o===0)return p>0&&h.fill(i),[h,c];if(p<=0)throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let m=0,f=1,g=0,y=r[m];for(;;){let x=0;if(f=x)throw new Error(C.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(y<0||y>=p)throw new Error(C.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(y,p));y>g&&h.fill(i,g*u,y*u);for(let A=m;A=l[0])throw new Error(C.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(A,n[A],l[0]));for(let w=0;wo)break}return gMath.sqrt(e)),Zz=ct(Uo,e=>Math.sqrt(e)),Jz={kernelName:Uo,backendName:"cpu",kernelFunc:Zz},Y6=_t((e,t)=>{let a=e-t;return a*a}),Qz=Kt(qo,Y6),eL={kernelName:qo,backendName:"cpu",kernelFunc:Qz},Z6=lr((e,t)=>{let{pattern:a,replaceGlobal:n,rewrite:r}=t;return e.replace(new RegExp(a,n?"g":""),r)}),tL=ms(Tp,Z6),aL={kernelName:Tp,backendName:"cpu",kernelFunc:tL};function J6(e,t,a,n){let r=_e(e,t.dtype);for(let s=0;s0?0:i-o),d=0;d+=l*this.leftPad.length;for(let y=0;yy.forEach(x=>m[f++]=x);for(let y=0;y0){g(e[c+p-1]);for(let y=0;y0){let o=t[0];if(o!==0)throw new Error(`First split value must be 0, got ${o}`);for(let l=1;l=o;if(u=u&&t[l]<=a,!u)throw new Error(`Invalid split value ${t[l]}, must be in [${o}, ${a}]`);o=t[l]}if(o!==a)throw new Error(`Last split value must be data size. 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A=i?[g,p,d]:[g,d,p],b=o?[y,h,c]:[y,c,h],w=bt({inputs:{x:r},backend:a,attrs:{shape:A}}),I=bt({inputs:{x:s},backend:a,attrs:{shape:b}}),T=i?w.shape[1]:w.shape[2],N=i?w.shape[2]:w.shape[1],M=o?I.shape[1]:I.shape[2],$=Math.max(g,y),E=a.data.get(w.dataId).values,S=a.data.get(I.dataId).values,_=v.computeStrides(w.shape),O=v.computeStrides(I.shape),[W,P,U]=i?[_[0],1,_[1]]:[_[0],_[1],1],[G,q,H]=o?[1,O[1],O[0]]:[O[1],1,O[0]],V=N*M,Z=_e([$,N,M],w.dtype),X=Z.values,re=a.blockSize;for(let ee=0;ee<$;ee++){let ge=ee%g,ie=ee%y;for(let be=0;beMath.acos(e)),AL={kernelName:oi,backendName:"cpu",kernelFunc:xL},bL=ct(li,e=>Math.acosh(e)),vL={kernelName:li,backendName:"cpu",kernelFunc:bL};function wL(e){let{inputs:t,backend:a}=e,n=t;Ie(t,"addN");let r=n.map(o=>a.data.get(o.dataId).values),s=_e(n[0].shape,n[0].dtype),i=s.values;for(let o=0;ox&&(x=w,A=b)}h[g]=A}return u.forEach(g=>a.disposeIntermediateTensorInfo(g)),a.makeTensorInfo(p,"int32",h)}var RL={kernelName:lu,backendName:"cpu",kernelFunc:NL};function EL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n;Ie(r,"argMin");let i=v.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Va({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[p,c]=C.computeOutAndReduceShapes(l.shape,i),d=v.sizeFromShape(p),h=v.makeZerosTypedArray(d,"int32"),m=v.sizeFromShape(c),f=a.data.get(l.dataId).values;for(let g=0;ga.disposeIntermediateTensorInfo(g)),a.makeTensorInfo(p,"int32",h)}var ML={kernelName:uu,backendName:"cpu",kernelFunc:EL},$L=ct(ci,e=>Math.asin(e)),PL={kernelName:ci,backendName:"cpu",kernelFunc:$L},_L=ct(hi,e=>Math.asinh(e)),FL={kernelName:hi,backendName:"cpu",kernelFunc:_L},DL=ct(mi,e=>Math.atan(e)),OL={kernelName:mi,backendName:"cpu",kernelFunc:DL},zL=_t((e,t)=>Math.atan2(e,t)),LL=Kt(gi,zL),WL={kernelName:gi,backendName:"cpu",kernelFunc:LL},BL=ct(fi,e=>Math.atanh(e)),VL={kernelName:fi,backendName:"cpu",kernelFunc:BL};function I3(e,t,a,n,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,p=r.effectiveFilterHeight,c=r.effectiveFilterWidth,d=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=_e(r.outShape,a),g=f.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],x=r.outShape[2]*r.outShape[3],A=r.outShape[3];for(let b=0;bU?U=ee:s==="avg"&&(G+=ee,q++)}if(isNaN(U))break}let H=S+_*A+T;g[H]=s==="avg"?G/q:U}}}return f}function uv(e,t,a,n,r=!1,s=!1){let i=_e(n.outShape,"int32"),o=n.strideHeight,l=n.strideWidth,u=n.dilationHeight,p=n.dilationWidth,c=n.effectiveFilterHeight,d=n.effectiveFilterWidth,h=n.padInfo.top,m=n.padInfo.left,f=_e(t,a,e);for(let g=0;g$&&($=P,r?E=s?((g*n.inHeight+S)*n.inWidth+O)*n.inChannels+y:(S*n.inWidth+O)*n.inChannels+y:E=_*d+W)}}i.set(E,g,x,I,y)}}return i}function dv(e,t,a,n,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,p=r.dilationHeight,c=r.dilationWidth,d=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,y=r.padInfo.left,x=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,A=_e(r.outShape,a),b=A.values,w=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],I=r.outShape[2]*r.outShape[3]*r.outShape[4],T=r.outShape[3]*r.outShape[4],N=r.outShape[4];for(let M=0;Mbe?be=xt:s==="avg"&&(Ce+=xt,Re++),isNaN(be))break}if(isNaN(be))break}if(isNaN(be))break}let Le=ie+S;b[Le]=s==="avg"?Ce/Math.max(Re,1):be}}}}return A}function UL(e,t){let a=_e(t.outShape,"int32"),n=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,p=t.effectiveFilterHeight,c=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f=_&&(_=V,O=P*p*c+G*p+H)}}}a.set(O,f,y,w,M,g)}}}return a}function GL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;Ie(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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p=C.computePool3DInfo(s.shape,i,o,1,l,u),c=p.strideDepth,d=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,y=p.dilationDepth,x=p.dilationHeight,A=p.dilationWidth,b=p.effectiveFilterDepth,w=p.effectiveFilterHeight,I=p.effectiveFilterWidth,T=b-1-p.padInfo.front,N=I-1-p.padInfo.left,M=w-1-p.padInfo.top,$=_e(s.shape,"float32"),E=1/(m*f*g),S=a.bufferSync(r);for(let _=0;_=p.outDepth||Math.floor(X)!==X))for(let re=0;re=p.outHeight||Math.floor(ee)!==ee))for(let ge=0;ge=p.outWidth||Math.floor(ie)!==ie)continue;let be=S.get(_,X,ee,ie,O);V+=be}}}$.set(V*E,_,W,P,U,O)}return a.makeTensorInfo($.shape,$.dtype,$.values)}var KL={kernelName:pp,backendName:"cpu",kernelFunc:XL};function YL(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Ie([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=C.computePool2DInfo(i.shape,o,l,1,u),c=p.strideHeight,d=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.effectiveFilterHeight,x=p.effectiveFilterWidth,A=x-1-p.padInfo.left,b=y-1-p.padInfo.top,w=_e(i.shape,"float32"),I=1/(h*m),T=a.data.get(r.dataId).values,N=_e(r.shape,"float32",T);for(let M=0;M=p.outHeight||Math.floor(U)!==U))for(let G=0;G=p.outWidth||Math.floor(q)!==q)continue;let H=N.get(M,U,q,$);W+=H}}w.set(W*I,M,E,S,$)}return a.makeTensorInfo(w.shape,w.dtype,w.values)}var ZL={kernelName:dp,backendName:"cpu",kernelFunc:YL};function JL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Ie([r,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=n;u==null&&(u=.001);let p=a.data.get(r.dataId).values,c=a.data.get(o.dataId).values,d=a.data.get(l.dataId).values,h=s?a.data.get(s.dataId).values:new Float32Array([1]),m=i?a.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(p.length),g=m.length,y=h.length,x=d.length,A=c.length,b=0,w=0,I=0,T=0;for(let N=0;N=g&&(b=0),w>=A&&(w=0),I>=y&&(I=0),T>=x&&(T=0);return a.makeTensorInfo(r.shape,r.dtype,f)}var QL={kernelName:Ui,backendName:"cpu",kernelFunc:JL};function eW(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;Ie([r],"batchToSpaceND");let o=s.reduce((y,x)=>y*x),l=C.getReshaped(r.shape,s,o),u=C.getPermuted(l.length,s.length),p=C.getReshapedPermuted(r.shape,s,o),c=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(p,i,s.length),h=bt({inputs:{x:r},backend:a,attrs:{shape:l}}),m=Va({inputs:{x:h},backend:a,attrs:{perm:u}}),f=bt({inputs:{x:m},backend:a,attrs:{shape:p}}),g=ti({inputs:{x:f},backend:a,attrs:{begin:c,size:d}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),g}var tW={kernelName:pu,backendName:"cpu",kernelFunc:eW};function aW(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.data.get(r.dataId).values,l=a.data.get(s.dataId).values,u=h3(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var nW={kernelName:Ai,backendName:"cpu",kernelFunc:aW};function rW(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.data.get(n.dataId).values,i=a.data.get(r.dataId).values,o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var sW={kernelName:hu,backendName:"cpu",kernelFunc:rW},iW=ct(us,(e,t)=>{let a=t;return e>a.clipValueMax?a.clipValueMax:e{let{x:t}=e.inputs,a=e.backend,n=new Float32Array(v.sizeFromShape(t.shape)),r=a.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=a.data.get(s.dataId).values,l=a.data.get(i.dataId).values;for(let u=0;uf.shape);C.assertParamsConsistent(i,s);let o=C.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>v.sizeFromShape(f.shape)>0);if(l.length===1)return rr({inputs:{x:l[0]},backend:a});if(l[0].dtype==="complex64"){let f=l.map(b=>ei({inputs:{input:b},backend:a})),g=l.map(b=>tu({inputs:{input:b},backend:a})),y=au({inputs:f,backend:a,attrs:{axis:s}}),x=au({inputs:g,backend:a,attrs:{axis:s}}),A=Ja({inputs:{real:y,imag:x},backend:a});return f.forEach(b=>a.disposeIntermediateTensorInfo(b)),g.forEach(b=>a.disposeIntermediateTensorInfo(b)),a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(x),A}let u=l.map(f=>{let g=[-1,v.sizeFromShape(f.shape.slice(s))];return bt({inputs:{x:f},backend:a,attrs:{shape:g}})}),p=u.map(f=>({vals:a.data.get(f.dataId).values,shape:f.shape}));o=C.computeOutShape(u.map(f=>f.shape),1);let c=u[0].shape[0]===1,d=m3(p,o,t[0].dtype,c),h=C.computeOutShape(l.map(f=>f.shape),s),m=a.makeTensorInfo(h,t[0].dtype,d);return u.forEach(f=>a.disposeIntermediateTensorInfo(f)),m}var pW={kernelName:mu,backendName:"cpu",kernelFunc:au};function pv(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n;Ie([r,s],"conv2d");let c=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.padInfo.left,x=d.padInfo.top,A=d.dataFormat==="channelsLast",b=new Vt(d.outShape,r.dtype),w=v.computeStrides(r.shape),I=v.computeStrides(s.shape),T=w[0],N=A?w[1]:w[2],M=A?w[2]:1,$=A?1:w[1],E=b.strides[0],S=A?b.strides[1]:b.strides[2],_=A?b.strides[2]:1,O=A?1:b.strides[1],W=a.data.get(r.dataId).values,P=a.data.get(s.dataId).values,U=b.values;for(let G=0;G=d.inHeight)continue;let ge=re*I[0],ie=q+ee*N;for(let be=0;be=d.inWidth)continue;let gt=ge+Le*I[1],dt=ie+qe*M,st=gt;for(let it=0;it=u.inDepth)continue;let G=P*M[0],q=E+U*N[1];for(let H=0;H=u.inHeight)continue;let ee=G+X*M[1],ge=q+re*N[2];for(let ie=0;ie=u.inWidth)continue;let qe=ee+Re*M[2],gt=ge+Le*u.inChannels,dt=qe;for(let st=0;stMath.cos(e)),IW={kernelName:Ci,backendName:"cpu",kernelFunc:kW},SW=ct(Ti,e=>Math.cosh(e)),CW={kernelName:Ti,backendName:"cpu",kernelFunc:SW};function TW(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[p,c,d,h]=r.shape,m=s.shape[0],[f,g]=o,y=_e([m,f,g,h],"float32"),x=a.data.get(s.dataId).values,A=a.data.get(i.dataId).values,b=a.data.get(r.dataId).values,w=v.computeStrides(r.shape),I=v.computeStrides(y.shape);for(let T=0;T=p)continue;let O=f>1?(E-M)*(c-1)/(f-1):0,W=g>1?(S-$)*(d-1)/(g-1):0;for(let P=0;P1?M*(c-1)+P*O:.5*(M+E)*(c-1);if(U<0||U>c-1){for(let G=0;G1?$*(d-1)+V*W:.5*($+S)*(d-1);if(Z<0||Z>d-1){for(let ge=0;ge1?$*(d-1)+G*W:.5*($+S)*(d-1);if(q<0||q>d-1){for(let Z=0;Zy+m-x-1:(y,x)=>y+x;for(let y=0;yy+m-x-1:(y,x)=>y+x;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],c=l*s,d=u*s,h=p/(s*s),m=a.data.get(r.dataId).values,f=new Float32Array(o*c*d*h),g=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=C.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:x}=h,A=x.left,b=x.top,w=h.outChannels/h.inChannels,I=new Vt(h.outShape,r.dtype),T=a.data.get(r.dataId).values,N=a.data.get(s.dataId).values,M=I.values;for(let $=0;$=h.inHeight)continue;let G=P*c[0],q=E+U*p[1];for(let H=0;H=h.inWidth)continue;let ee=G+X*c[1],ge=q+re*h.inChannels,ie=V,be=ee;for(let Ce=0;Ce{let{x:n,filter:r}=e,{strides:s,pad:i,dilations:o}=a,l=t,u=l.data.get(n.dataId).values,p=n.shape.length,c=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:x,padInfo:A,strideHeight:b,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:N,dilationWidth:M,outShape:$}=C.computeDilation2DInfo(n.shape,r.shape,s,i,"NHWC",o),E=v.sizeFromShape($),S=$.length,_=v.getArrayFromDType(n.dtype,E);for(let O=0;O=0&&X=0&&eeH&&(H=be)}}}let V=v.locToIndex([O,W,U,q],S,v.computeStrides($));_[V]=H}}}return{dataId:l.write(v.toTypedArray(_,n.dtype),$,n.dtype),shape:$,dtype:n.dtype}}},HW={kernelName:Xl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:x,strideHeight:A,strideWidth:b,filterHeight:w,filterWidth:I,dilationHeight:T,dilationWidth:N,outShape:M}=C.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===M.length,()=>`Error in ${Xl}, dy must have the same rank as output ${M.length}, but got ${s.rank}`);let $=v.toNestedArray(M,u.data.get(s.dataId).values),E=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&Z=0&&reG&&(G=ee,q=V,H=X)}}}E[q][H][U]+=$[S][_][W][U]}}}return{dataId:u.write(v.toTypedArray(E,n.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},jW={kernelName:ql,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:x,strideHeight:A,strideWidth:b,filterHeight:w,filterWidth:I,dilationHeight:T,dilationWidth:N,outShape:M}=C.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===M.length,()=>`Error in ${ql}, dy must have the same rank as output ${M.length}, but got ${s.rank}`);let $=v.toNestedArray(M,u.data.get(s.dataId).values),E=v.makeZerosNestedTypedArray(n.shape,n.dtype);for(let S=0;S=0&&Z=0&&reG&&(G=ee,q=Z,H=re)}}}E[S][q][H][U]+=$[S][_][W][U]}}}return{dataId:u.write(v.toTypedArray(E,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function qW(e){let{inputs:t,backend:a,attrs:n}=e,{image:r}=t,{canvas:s,options:i}=n,{contextOptions:o,imageOptions:l}=i||{},u=(l==null?void 0:l.alpha)||1,p=(o==null?void 0:o.contextType)||"2d";if(p!=="2d")throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let c=s.getContext(p,(o==null?void 0:o.contextAttributes)||{});if(c==null)throw new Error(`Could not get the context with ${p} type.`);let[d,h]=r.shape.slice(0,2),m=r.shape.length===2?1:r.shape[2],f=a.data.get(r.dataId).values,g=r.dtype==="float32"?255:1,y=new Uint8ClampedArray(h*d*4);for(let A=0;A1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${T}.`)}else if(r.dtype==="int32"&&(T<0||T>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${T}.`);m===1?(b[0]=T*g,b[1]=T*g,b[2]=T*g):b[I]=T*g}let w=A*4;y[w+0]=Math.round(b[0]),y[w+1]=Math.round(b[1]),y[w+2]=Math.round(b[2]),y[w+3]=Math.round(b[3])}s.width=h,s.height=d;let x=new ImageData(y,h,d);return c.putImageData(x,0,0),r}var XW={kernelName:yp,backendName:"cpu",kernelFunc:qW};function Kp(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;Ie(r,"sum");let o;r.dtype==="bool"?o=is({inputs:{x:r},backend:a,attrs:{dtype:"int32"}}):o=rr({inputs:{x:r},backend:a});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=C.getAxesPermutation(u,l),c=u,d=o;p!=null&&(d=Va({inputs:{x:o},backend:a,attrs:{perm:p}}),c=C.getInnerMostAxes(c.length,l)),C.assertAxesAreInnerMostDims("sum",c,d.shape.length);let[h,m]=C.computeOutAndReduceShapes(d.shape,c),f=C.upcastType(d.dtype,"int32"),g=xh(a,h,f),y=v.sizeFromShape(m),x=a.data.get(g.dataId).values,A=a.data.get(d.dataId).values;for(let b=0;b=0&&(d=Kp({inputs:{x:d},backend:a,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(d)),h--)}for(let f of m)f!==d&&a.disposeIntermediateTensorInfo(f);return d}var ZW={kernelName:xp,backendName:"cpu",kernelFunc:YW};function JW(e){let{inputs:t,backend:a}=e,{dy:n,y:r}=t;Ie([n,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=a.data.get(r.dataId).values,o=a.data.get(n.dataId).values;for(let l=0;l=0?s[l]=o[l]:s[l]=o[l]*(u+1)}return a.makeTensorInfo(r.shape,"float32",s)}var QW={kernelName:xu,backendName:"cpu",kernelFunc:JW},eB=C.ERF_P,tB=C.ERF_A1,aB=C.ERF_A2,nB=C.ERF_A3,rB=C.ERF_A4,sB=C.ERF_A5,iB=ct(Di,e=>{let t=Math.sign(e),a=Math.abs(e),n=1/(1+eB*a);return t*(1-((((sB*n+rB)*n+nB)*n+aB)*n+tB)*n*Math.exp(-a*a))}),oB={kernelName:Di,backendName:"cpu",kernelFunc:iB};function vh(e){let{inputs:t,backend:a,attrs:n}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),bt({inputs:{x:r},backend:a,attrs:{shape:o}})}var lB={kernelName:Au,backendName:"cpu",kernelFunc:vh},uB=_t((e,t)=>e/t),S3=Kt(_i,uB),P1={kernelName:_i,backendName:"cpu",kernelFunc:S3};function hv(e,t,a){let n=e.shape,r=n[0],s=n[1],i=a.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),c=v.getTypedArrayFromDType("float32",p),d=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:n}=e,r=a,s=v.getTypedArrayFromDType(n.dtype,v.sizeFromShape(n.shape)),[i,o,l,u]=n.shape,p=r.data.get(n.dataId).values;for(let c=0;c=0&&x=0,()=>`GatherV2: the index value ${w} is not in [0, ${p-1}]`)}let c=o;o==null&&(c=0);let d=v.sizeFromShape(s.shape),h=C.segment_util.collectGatherOpShapeInfo(r,s,l,c),m=bt({inputs:{x:r},backend:a,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=bt({inputs:{x:s},backend:a,attrs:{shape:[h.batchSize,d/h.batchSize]}}),g=[h.batchSize,h.outerSize,d/h.batchSize,h.sliceSize],y=a.bufferSync(f),x=a.bufferSync(m),A=E6(x,y,g);return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),a.makeTensorInfo(h.outputShape,A.dtype,A.values)}var SB={kernelName:vu,backendName:"cpu",kernelFunc:IB};function CB(e){let{inputs:t,backend:a}=e,{input:n}=t,r=v.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=r/s,o=bt({inputs:{x:n},backend:a,attrs:{shape:[i,s]}}),l=hv(o,!0,a),u=bt({inputs:{x:l},backend:a,attrs:{shape:n.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(l),u}var TB={kernelName:bp,backendName:"cpu",kernelFunc:CB},NB=ct(Xi,e=>Number.isFinite(e)?1:0,"bool"),RB={kernelName:Xi,backendName:"cpu",kernelFunc:NB},EB=ct(Ki,e=>Math.abs(e)===1/0?1:0,"bool"),MB={kernelName:Ki,backendName:"cpu",kernelFunc:EB},$B=ct(Yi,e=>Number.isNaN(e)?1:0,"bool"),PB={kernelName:Yi,backendName:"cpu",kernelFunc:$B};function _B(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=F6(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var FB={kernelName:eo,backendName:"cpu",kernelFunc:_B},DB=ct(ao,e=>Math.log1p(e)),OB={kernelName:ao,backendName:"cpu",kernelFunc:DB},zB=_t((e,t)=>e&&t),LB=Kt(no,zB,null,"bool"),WB={kernelName:no,backendName:"cpu",kernelFunc:LB},BB=ct(ro,e=>e?0:1,"bool"),VB={kernelName:ro,backendName:"cpu",kernelFunc:BB},UB=_t((e,t)=>e||t),GB=Kt(so,UB,null,"bool"),HB={kernelName:so,backendName:"cpu",kernelFunc:GB};function jB(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;Ie(r,"LRN");let u=r.shape[3],p=u-1,c=a.data.get(r.dataId).values,d=v.sizeFromShape(r.shape),h=new Float32Array(d);function m(f){let g=f%u,y=f-g+Math.max(0,g-s),x=f-g+Math.min(g+s,p),A=0;for(;y<=x;y++){let b=c[y];A+=b*b}return A}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,u,o,l),c;if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))c=rr({inputs:{x:r},backend:a});else{let d=a.data.get(r.dataId).values,h=v.computeStrides(r.shape),m=I3(d,r.shape,r.dtype,h,p,"max");c=a.makeTensorInfo(p.outShape,r.dtype,m.values)}return c}var JB={kernelName:uo,backendName:"cpu",kernelFunc:ZB};function QB(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n;Ie(r,"maxPool3d");let p=C.computePool3DInfo(r.shape,s,i,1,o,l,u),c=a.data.get(r.dataId).values,d=dv(c,r.shape,r.dtype,v.computeStrides(r.shape),p,"max");return a.makeTensorInfo(d.shape,"float32",d.values)}var eV={kernelName:ku,backendName:"cpu",kernelFunc:QB};function tV(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n;Ie([r,s],"maxPool3DGrad");let p=C.computePool3DInfo(s.shape,i,o,1,l,u),c=a.bufferSync(s),d=UL(c,p),h=p.strideDepth,m=p.strideHeight,f=p.strideWidth,g=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,A=p.effectiveFilterDepth,b=p.effectiveFilterHeight,w=p.effectiveFilterWidth,I=A-1-p.padInfo.front,T=w-1-p.padInfo.left,N=b-1-p.padInfo.top,M=_e(s.shape,"float32"),$=a.bufferSync(r);for(let E=0;E=p.outDepth||Math.floor(V)!==V))for(let Z=0;Z=p.outHeight||Math.floor(X)!==X))for(let re=0;re=p.outWidth||Math.floor(ee)!==ee)continue;let ge=A*b*w-1-d.get(E,V,X,ee,S),ie=H*b*w+Z*w+re,be=ge===ie?1:0;if(be===0)continue;let Ce=$.get(E,V,X,ee,S);q+=Ce*be}}}M.set(q,E,_,O,W,S)}return a.makeTensorInfo(M.shape,M.dtype,M.values)}var aV={kernelName:kp,backendName:"cpu",kernelFunc:tV};function nV(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;Ie([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=C.computePool2DInfo(o.shape,l,u,1,p,c),h=a.data.get(o.dataId).values,m=_e(d.outShape,o.dtype,uv(h,o.shape,o.dtype,d).values),f=d.strideHeight,g=d.strideWidth,y=d.dilationHeight,x=d.dilationWidth,A=d.effectiveFilterHeight,b=d.effectiveFilterWidth,w=b-1-d.padInfo.left,I=A-1-d.padInfo.top,T=_e(o.shape,"float32"),N=a.data.get(r.dataId).values,M=_e(r.shape,"float32",N);for(let $=0;$=d.outHeight||Math.floor(G)!==G))for(let q=0;q=d.outWidth||Math.floor(H)!==H)continue;let V=A*b-1-m.get($,G,H,E),Z=U*b+q,X=V===Z?1:0;if(X===0)continue;let re=M.get($,G,H,E);P+=re*X}}T.set(P,$,S,_,E)}return a.makeTensorInfo(T.shape,T.dtype,T.values)}var rV={kernelName:wp,backendName:"cpu",kernelFunc:nV};function sV(e,t,a,n,r){let s=v.computeStrides(t),i=I3(e,t,a,s,r,"max"),o=uv(e,t,a,r,!0,n);return[i.values,o.values]}var iV={kernelName:Iu,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=a;Ie(n,"MaxPoolWithArgmax");let u=l.data.get(n.dataId).values,p=C.computePool2DInfo(n.shape,r,s,[1,1],i),[c,d]=sV(u,n.shape,n.dtype,o,p),h=l.write(c,p.outShape,n.dtype),m=l.write(d,p.outShape,n.dtype);return[{dataId:h,shape:p.outShape,dtype:n.dtype},{dataId:m,shape:p.outShape,dtype:"int32"}]}};function oV(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=v.parseAxisParam(s,r.shape),l=C.computeOutAndReduceShapes(r.shape,o)[1],u=v.sizeFromShape(l),p=[],c=a.makeTensorInfo([],"float32",new Float32Array([u]));p.push(c);let d=is({inputs:{x:r},backend:a,attrs:{dtype:"float32"}});p.push(d);let h=S3({inputs:{a:d,b:c},backend:a});p.push(h);let m=Kp({inputs:{x:h},backend:a,attrs:{axis:s,keepDims:i}});return p.forEach(f=>a.disposeIntermediateTensorInfo(f)),m}var lV={kernelName:po,backendName:"cpu",kernelFunc:oV};function uV(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;Ie(r,"min");let o=v.parseAxisParam(s,r.shape),l=o,u=C.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=Va({inputs:{x:r},backend:a,attrs:{perm:u}}),l=C.getInnerMostAxes(l.length,r.shape.length)),C.assertAxesAreInnerMostDims("min",l,p.shape.length);let[c,d]=C.computeOutAndReduceShapes(p.shape,l),h=v.sizeFromShape(d),m=v.makeZerosTypedArray(v.sizeFromShape(c),p.dtype),f=a.data.get(p.dataId).values;for(let y=0;yx[0]+r.shape[A]+x[1]),l=s.map(x=>x[0]),u=s.map((x,A)=>x[0]+r.shape[A]),p=i==="reflect"?0:1,c=a.data.get(r.dataId).values,d=r.shape.length,h=v.computeStrides(r.shape),m=v.sizeFromShape(o),f=o.length,g=v.computeStrides(o),y=v.getTypedArrayFromDType(r.dtype,m);for(let x=0;x=u[w]&&(A[w]=(u[w]-1)*2-A[w]+p);A=A.map((w,I)=>w-l[I]);let b=v.locToIndex(A,d,h);y[x]=c[b]}return{dataId:a.write(y,o,r.dtype),shape:o,dtype:r.dtype}}var cV={kernelName:mo,backendName:"cpu",kernelFunc:pV},hV=_t((e,t)=>{let a=e%t;return e<0&&t<0||e>=0&&t>=0?a:(a+t)%t}),mV=Kt(fo,hV),fV={kernelName:fo,backendName:"cpu",kernelFunc:mV},gV=ru(mA());function fv(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=r.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);let l=v.parseAxisParam([o],r.shape),u=mv({inputs:{x:r},backend:a,attrs:{reductionIndices:l,keepDims:!1}}),p=C.expandShapeToKeepDim(u.shape,l),c=bt({inputs:{x:u},backend:a,attrs:{shape:p}}),d=w3({inputs:{a:r,b:c},backend:a}),h=S6({inputs:{x:d},backend:a}),m=Kp({inputs:{x:h},backend:a,attrs:{axis:l,keepDims:!1}}),f=bt({inputs:{x:m},backend:a,attrs:{shape:p}}),g=S3({inputs:{a:h,b:f},backend:a});return a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),g}var yV={kernelName:Ho,backendName:"cpu",kernelFunc:fv};function xV(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n;Ie(r,"multinomial");let l=o?r:fv({inputs:{logits:r},backend:a,attrs:{dim:-1}}),u=l.shape[0],p=l.shape[1],c=a.data.get(l.dataId).values,d=[u,s],h=v.makeZerosTypedArray(v.sizeFromShape(d),"int32");for(let m=0;m=0&&c[d]{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=vh({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=au({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeIntermediateTensorInfo(p)),u}var PV={kernelName:Nu,backendName:"cpu",kernelFunc:yv};function _V(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;Ie(r,"pad");let o=s.map((y,x)=>y[0]+r.shape[x]+y[1]),l=s.map(y=>y[0]),u=a.data.get(r.dataId).values,p=v.sizeFromShape(r.shape),c=r.shape.length,d=v.computeStrides(r.shape),h=v.sizeFromShape(o),m=o.length,f=v.computeStrides(o),g=v.getTypedArrayFromDType(r.dtype,h);i!==0&&g.fill(i);for(let 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WV(e){let{inputs:t,backend:a}=e,{starts:n,limits:r,deltas:s}=t,i=a.data.get(n.dataId).values,o=a.data.get(r.dataId).values,l=a.data.get(s.dataId).values,[u,p]=G6(i,n.shape,n.dtype,o,r.shape,l,s.shape),c=a.makeTensorInfo([u.length],"int32",u),d=a.makeTensorInfo([p.length],n.dtype,p);return[c,d]}var BV={kernelName:Ph,backendName:"cpu",kernelFunc:WV};function VV(e){let{inputs:t,backend:a,attrs:n}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=n,u=a.data.get(r.dataId).values,p=a.data.get(s.dataId).values,c=a.data.get(i.dataId).values,d=o.map(g=>a.data.get(g.dataId).values),h=o.map(g=>g.shape),[m,f]=H6(u,r.shape,p,s.shape,s.dtype,c,i.shape,d,h,l);return a.makeTensorInfo(m,s.dtype,f)}var UV={kernelName:_h,backendName:"cpu",kernelFunc:VV};function GV(e){let{backend:t,attrs:a}=e,{start:n,stop:r,dtype:s,step:i}=a,o=y3(n,r,i,s);return t.makeTensorInfo([o.length],s,o)}var 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A=i?[g,p,d]:[g,d,p],b=o?[y,h,c]:[y,c,h],w=bt({inputs:{x:r},backend:a,attrs:{shape:A}}),I=bt({inputs:{x:s},backend:a,attrs:{shape:b}}),T=i?w.shape[1]:w.shape[2],N=i?w.shape[2]:w.shape[1],M=o?I.shape[1]:I.shape[2],$=Math.max(g,y),E=a.data.get(w.dataId).values,S=a.data.get(I.dataId).values,_=v.computeStrides(w.shape),O=v.computeStrides(I.shape),[W,P,U]=i?[_[0],1,_[1]]:[_[0],_[1],1],[G,q,H]=o?[1,O[1],O[0]]:[O[1],1,O[0]],V=N*M,Z=_e([$,N,M],w.dtype),X=Z.values,re=a.blockSize;for(let ee=0;ee<$;ee++){let ge=ee%g,ie=ee%y;for(let be=0;beMath.acos(e)),AL={kernelName:ii,backendName:"cpu",kernelFunc:xL},bL=ct(oi,e=>Math.acosh(e)),vL={kernelName:oi,backendName:"cpu",kernelFunc:bL};function wL(e){let{inputs:t,backend:a}=e,n=t;Ie(t,"addN");let r=n.map(o=>a.data.get(o.dataId).values),s=_e(n[0].shape,n[0].dtype),i=s.values;for(let o=0;ox&&(x=w,A=b)}h[g]=A}return u.forEach(g=>a.disposeIntermediateTensorInfo(g)),a.makeTensorInfo(p,"int32",h)}var RL={kernelName:Gl,backendName:"cpu",kernelFunc:NL};function EL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n;Ie(r,"argMin");let i=v.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Va({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[p,c]=C.computeOutAndReduceShapes(l.shape,i),d=v.sizeFromShape(p),h=v.makeZerosTypedArray(d,"int32"),m=v.sizeFromShape(c),f=a.data.get(l.dataId).values;for(let g=0;ga.disposeIntermediateTensorInfo(g)),a.makeTensorInfo(p,"int32",h)}var ML={kernelName:Hl,backendName:"cpu",kernelFunc:EL},$L=ct(ui,e=>Math.asin(e)),PL={kernelName:ui,backendName:"cpu",kernelFunc:$L},_L=ct(di,e=>Math.asinh(e)),FL={kernelName:di,backendName:"cpu",kernelFunc:_L},DL=ct(pi,e=>Math.atan(e)),OL={kernelName:pi,backendName:"cpu",kernelFunc:DL},zL=_t((e,t)=>Math.atan2(e,t)),LL=Kt(hi,zL),WL={kernelName:hi,backendName:"cpu",kernelFunc:LL},BL=ct(ci,e=>Math.atanh(e)),VL={kernelName:ci,backendName:"cpu",kernelFunc:BL};function r3(e,t,a,n,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,p=r.effectiveFilterHeight,c=r.effectiveFilterWidth,d=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=_e(r.outShape,a),g=f.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],x=r.outShape[2]*r.outShape[3],A=r.outShape[3];for(let b=0;bU?U=ee:s==="avg"&&(G+=ee,q++)}if(isNaN(U))break}let H=S+_*A+T;g[H]=s==="avg"?G/q:U}}}return f}function U6(e,t,a,n,r=!1,s=!1){let i=_e(n.outShape,"int32"),o=n.strideHeight,l=n.strideWidth,u=n.dilationHeight,p=n.dilationWidth,c=n.effectiveFilterHeight,d=n.effectiveFilterWidth,h=n.padInfo.top,m=n.padInfo.left,f=_e(t,a,e);for(let g=0;g$&&($=P,r?E=s?((g*n.inHeight+S)*n.inWidth+O)*n.inChannels+y:(S*n.inWidth+O)*n.inChannels+y:E=_*d+W)}}i.set(E,g,x,I,y)}}return i}function G6(e,t,a,n,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,p=r.dilationHeight,c=r.dilationWidth,d=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,y=r.padInfo.left,x=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,A=_e(r.outShape,a),b=A.values,w=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],I=r.outShape[2]*r.outShape[3]*r.outShape[4],T=r.outShape[3]*r.outShape[4],N=r.outShape[4];for(let M=0;Mbe?be=xt:s==="avg"&&(Ce+=xt,Re++),isNaN(be))break}if(isNaN(be))break}if(isNaN(be))break}let Le=ie+S;b[Le]=s==="avg"?Ce/Math.max(Re,1):be}}}}return A}function UL(e,t){let a=_e(t.outShape,"int32"),n=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,p=t.effectiveFilterHeight,c=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f=_&&(_=V,O=P*p*c+G*p+H)}}}a.set(O,f,y,w,M,g)}}}return a}function GL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;Ie(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,u,o,l),c;if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))c=rr({inputs:{x:r},backend:a});else{let d=a.data.get(r.dataId).values,h=v.computeStrides(r.shape),m=r3(d,r.shape,r.dtype,h,p,"avg");c=a.makeTensorInfo(p.outShape,r.dtype,m.values)}return c}var HL={kernelName:mi,backendName:"cpu",kernelFunc:GL};function jL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n;Ie(r,"avgPool3d");let p=C.computePool3DInfo(r.shape,s,i,1,o,l,u),c=a.data.get(r.dataId).values,d=G6(c,r.shape,r.dtype,v.computeStrides(r.shape),p,"avg");return a.makeTensorInfo(d.shape,"float32",d.values)}var qL={kernelName:jl,backendName:"cpu",kernelFunc:jL};function XL(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n;Ie([r,s],"avgPool3DGrad");let p=C.computePool3DInfo(s.shape,i,o,1,l,u),c=p.strideDepth,d=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,y=p.dilationDepth,x=p.dilationHeight,A=p.dilationWidth,b=p.effectiveFilterDepth,w=p.effectiveFilterHeight,I=p.effectiveFilterWidth,T=b-1-p.padInfo.front,N=I-1-p.padInfo.left,M=w-1-p.padInfo.top,$=_e(s.shape,"float32"),E=1/(m*f*g),S=a.bufferSync(r);for(let _=0;_=p.outDepth||Math.floor(X)!==X))for(let re=0;re=p.outHeight||Math.floor(ee)!==ee))for(let ge=0;ge=p.outWidth||Math.floor(ie)!==ie)continue;let be=S.get(_,X,ee,ie,O);V+=be}}}$.set(V*E,_,W,P,U,O)}return a.makeTensorInfo($.shape,$.dtype,$.values)}var KL={kernelName:jd,backendName:"cpu",kernelFunc:XL};function YL(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Ie([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=C.computePool2DInfo(i.shape,o,l,1,u),c=p.strideHeight,d=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.effectiveFilterHeight,x=p.effectiveFilterWidth,A=x-1-p.padInfo.left,b=y-1-p.padInfo.top,w=_e(i.shape,"float32"),I=1/(h*m),T=a.data.get(r.dataId).values,N=_e(r.shape,"float32",T);for(let M=0;M=p.outHeight||Math.floor(U)!==U))for(let G=0;G=p.outWidth||Math.floor(q)!==q)continue;let H=N.get(M,U,q,$);W+=H}}w.set(W*I,M,E,S,$)}return a.makeTensorInfo(w.shape,w.dtype,w.values)}var ZL={kernelName:Hd,backendName:"cpu",kernelFunc:YL};function JL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Ie([r,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=n;u==null&&(u=.001);let p=a.data.get(r.dataId).values,c=a.data.get(o.dataId).values,d=a.data.get(l.dataId).values,h=s?a.data.get(s.dataId).values:new Float32Array([1]),m=i?a.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(p.length),g=m.length,y=h.length,x=d.length,A=c.length,b=0,w=0,I=0,T=0;for(let N=0;N=g&&(b=0),w>=A&&(w=0),I>=y&&(I=0),T>=x&&(T=0);return a.makeTensorInfo(r.shape,r.dtype,f)}var QL={kernelName:Di,backendName:"cpu",kernelFunc:JL};function eW(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;Ie([r],"batchToSpaceND");let o=s.reduce((y,x)=>y*x),l=C.getReshaped(r.shape,s,o),u=C.getPermuted(l.length,s.length),p=C.getReshapedPermuted(r.shape,s,o),c=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(p,i,s.length),h=bt({inputs:{x:r},backend:a,attrs:{shape:l}}),m=Va({inputs:{x:h},backend:a,attrs:{perm:u}}),f=bt({inputs:{x:m},backend:a,attrs:{shape:p}}),g=ei({inputs:{x:f},backend:a,attrs:{begin:c,size:d}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),g}var tW={kernelName:ql,backendName:"cpu",kernelFunc:eW};function aW(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.data.get(r.dataId).values,l=a.data.get(s.dataId).values,u=qg(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var nW={kernelName:gi,backendName:"cpu",kernelFunc:aW};function rW(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.data.get(n.dataId).values,i=a.data.get(r.dataId).values,o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var sW={kernelName:Kl,backendName:"cpu",kernelFunc:rW},iW=ct(ls,(e,t)=>{let a=t;return e>a.clipValueMax?a.clipValueMax:e{let{x:t}=e.inputs,a=e.backend,n=new Float32Array(v.sizeFromShape(t.shape)),r=a.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=a.data.get(s.dataId).values,l=a.data.get(i.dataId).values;for(let u=0;uf.shape);C.assertParamsConsistent(i,s);let o=C.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>v.sizeFromShape(f.shape)>0);if(l.length===1)return rr({inputs:{x:l[0]},backend:a});if(l[0].dtype==="complex64"){let f=l.map(b=>Qs({inputs:{input:b},backend:a})),g=l.map(b=>zl({inputs:{input:b},backend:a})),y=Ll({inputs:f,backend:a,attrs:{axis:s}}),x=Ll({inputs:g,backend:a,attrs:{axis:s}}),A=Ja({inputs:{real:y,imag:x},backend:a});return f.forEach(b=>a.disposeIntermediateTensorInfo(b)),g.forEach(b=>a.disposeIntermediateTensorInfo(b)),a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(x),A}let u=l.map(f=>{let g=[-1,v.sizeFromShape(f.shape.slice(s))];return bt({inputs:{x:f},backend:a,attrs:{shape:g}})}),p=u.map(f=>({vals:a.data.get(f.dataId).values,shape:f.shape}));o=C.computeOutShape(u.map(f=>f.shape),1);let c=u[0].shape[0]===1,d=Xg(p,o,t[0].dtype,c),h=C.computeOutShape(l.map(f=>f.shape),s),m=a.makeTensorInfo(h,t[0].dtype,d);return u.forEach(f=>a.disposeIntermediateTensorInfo(f)),m}var pW={kernelName:Yl,backendName:"cpu",kernelFunc:Ll};function H6(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n;Ie([r,s],"conv2d");let c=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.padInfo.left,x=d.padInfo.top,A=d.dataFormat==="channelsLast",b=new Vt(d.outShape,r.dtype),w=v.computeStrides(r.shape),I=v.computeStrides(s.shape),T=w[0],N=A?w[1]:w[2],M=A?w[2]:1,$=A?1:w[1],E=b.strides[0],S=A?b.strides[1]:b.strides[2],_=A?b.strides[2]:1,O=A?1:b.strides[1],W=a.data.get(r.dataId).values,P=a.data.get(s.dataId).values,U=b.values;for(let G=0;G=d.inHeight)continue;let ge=re*I[0],ie=q+ee*N;for(let be=0;be=d.inWidth)continue;let gt=ge+Le*I[1],dt=ie+qe*M,st=gt;for(let it=0;it=u.inDepth)continue;let G=P*M[0],q=E+U*N[1];for(let H=0;H=u.inHeight)continue;let ee=G+X*M[1],ge=q+re*N[2];for(let ie=0;ie=u.inWidth)continue;let qe=ee+Re*M[2],gt=ge+Le*u.inChannels,dt=qe;for(let st=0;stMath.cos(e)),IW={kernelName:"Cos",backendName:"cpu",kernelFunc:kW},SW=ct(ki,e=>Math.cosh(e)),CW={kernelName:ki,backendName:"cpu",kernelFunc:SW};function TW(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[p,c,d,h]=r.shape,m=s.shape[0],[f,g]=o,y=_e([m,f,g,h],"float32"),x=a.data.get(s.dataId).values,A=a.data.get(i.dataId).values,b=a.data.get(r.dataId).values,w=v.computeStrides(r.shape),I=v.computeStrides(y.shape);for(let T=0;T=p)continue;let O=f>1?(E-M)*(c-1)/(f-1):0,W=g>1?(S-$)*(d-1)/(g-1):0;for(let P=0;P1?M*(c-1)+P*O:.5*(M+E)*(c-1);if(U<0||U>c-1){for(let G=0;G1?$*(d-1)+V*W:.5*($+S)*(d-1);if(Z<0||Z>d-1){for(let ge=0;ge1?$*(d-1)+G*W:.5*($+S)*(d-1);if(q<0||q>d-1){for(let Z=0;Zy+m-x-1:(y,x)=>y+x;for(let y=0;yy+m-x-1:(y,x)=>y+x;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],c=l*s,d=u*s,h=p/(s*s),m=a.data.get(r.dataId).values,f=new Float32Array(o*c*d*h),g=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=C.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:x}=h,A=x.left,b=x.top,w=h.outChannels/h.inChannels,I=new Vt(h.outShape,r.dtype),T=a.data.get(r.dataId).values,N=a.data.get(s.dataId).values,M=I.values;for(let $=0;$=h.inHeight)continue;let G=P*c[0],q=E+U*p[1];for(let H=0;H=h.inWidth)continue;let ee=G+X*c[1],ge=q+re*h.inChannels,ie=V,be=ee;for(let Ce=0;Ce{let{x:n,filter:r}=e,{strides:s,pad:i,dilations:o}=a,l=t,u=l.data.get(n.dataId).values,p=n.shape.length,c=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:x,padInfo:A,strideHeight:b,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:N,dilationWidth:M,outShape:$}=C.computeDilation2DInfo(n.shape,r.shape,s,i,"NHWC",o),E=v.sizeFromShape($),S=$.length,_=v.getArrayFromDType(n.dtype,E);for(let O=0;O=0&&X=0&&eeH&&(H=be)}}}let V=v.locToIndex([O,W,U,q],S,v.computeStrides($));_[V]=H}}}return{dataId:l.write(v.toTypedArray(_,n.dtype),$,n.dtype),shape:$,dtype:n.dtype}}},HW={kernelName:Ml,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:x,strideHeight:A,strideWidth:b,filterHeight:w,filterWidth:I,dilationHeight:T,dilationWidth:N,outShape:M}=C.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===M.length,()=>`Error in ${Ml}, dy must have the same rank as output ${M.length}, but got ${s.rank}`);let $=v.toNestedArray(M,u.data.get(s.dataId).values),E=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&Z=0&&reG&&(G=ee,q=V,H=X)}}}E[q][H][U]+=$[S][_][W][U]}}}return{dataId:u.write(v.toTypedArray(E,n.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},jW={kernelName:El,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,p=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:x,strideHeight:A,strideWidth:b,filterHeight:w,filterWidth:I,dilationHeight:T,dilationWidth:N,outShape:M}=C.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===M.length,()=>`Error in ${El}, dy must have the same rank as output ${M.length}, but got ${s.rank}`);let $=v.toNestedArray(M,u.data.get(s.dataId).values),E=v.makeZerosNestedTypedArray(n.shape,n.dtype);for(let S=0;S=0&&Z=0&&reG&&(G=ee,q=Z,H=re)}}}E[S][q][H][U]+=$[S][_][W][U]}}}return{dataId:u.write(v.toTypedArray(E,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function qW(e){let{inputs:t,backend:a,attrs:n}=e,{image:r}=t,{canvas:s,options:i}=n,{contextOptions:o,imageOptions:l}=i||{},u=(l==null?void 0:l.alpha)||1,p=(o==null?void 0:o.contextType)||"2d";if(p!=="2d")throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let c=s.getContext(p,(o==null?void 0:o.contextAttributes)||{});if(c==null)throw new Error(`Could not get the context with ${p} type.`);let[d,h]=r.shape.slice(0,2),m=r.shape.length===2?1:r.shape[2],f=a.data.get(r.dataId).values,g=r.dtype==="float32"?255:1,y=new Uint8ClampedArray(h*d*4);for(let A=0;A1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${T}.`)}else if(r.dtype==="int32"&&(T<0||T>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${T}.`);m===1?(b[0]=T*g,b[1]=T*g,b[2]=T*g):b[I]=T*g}let w=A*4;y[w+0]=Math.round(b[0]),y[w+1]=Math.round(b[1]),y[w+2]=Math.round(b[2]),y[w+3]=Math.round(b[3])}s.width=h,s.height=d;let x=new ImageData(y,h,d);return c.putImageData(x,0,0),r}var XW={kernelName:Jd,backendName:"cpu",kernelFunc:qW};function Rp(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;Ie(r,"sum");let o;r.dtype==="bool"?o=is({inputs:{x:r},backend:a,attrs:{dtype:"int32"}}):o=rr({inputs:{x:r},backend:a});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=C.getAxesPermutation(u,l),c=u,d=o;p!=null&&(d=Va({inputs:{x:o},backend:a,attrs:{perm:p}}),c=C.getInnerMostAxes(c.length,l)),C.assertAxesAreInnerMostDims("sum",c,d.shape.length);let[h,m]=C.computeOutAndReduceShapes(d.shape,c),f=C.upcastType(d.dtype,"int32"),g=Jc(a,h,f),y=v.sizeFromShape(m),x=a.data.get(g.dataId).values,A=a.data.get(d.dataId).values;for(let b=0;b=0&&(d=Rp({inputs:{x:d},backend:a,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(d)),h--)}for(let f of m)f!==d&&a.disposeIntermediateTensorInfo(f);return d}var ZW={kernelName:Qd,backendName:"cpu",kernelFunc:YW};function JW(e){let{inputs:t,backend:a}=e,{dy:n,y:r}=t;Ie([n,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=a.data.get(r.dataId).values,o=a.data.get(n.dataId).values;for(let l=0;l=0?s[l]=o[l]:s[l]=o[l]*(u+1)}return a.makeTensorInfo(r.shape,"float32",s)}var QW={kernelName:eu,backendName:"cpu",kernelFunc:JW},eB=C.ERF_P,tB=C.ERF_A1,aB=C.ERF_A2,nB=C.ERF_A3,rB=C.ERF_A4,sB=C.ERF_A5,iB=ct("Erf",e=>{let t=Math.sign(e),a=Math.abs(e),n=1/(1+eB*a);return t*(1-((((sB*n+rB)*n+nB)*n+aB)*n+tB)*n*Math.exp(-a*a))}),oB={kernelName:"Erf",backendName:"cpu",kernelFunc:iB};function th(e){let{inputs:t,backend:a,attrs:n}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),bt({inputs:{x:r},backend:a,attrs:{shape:o}})}var lB={kernelName:tu,backendName:"cpu",kernelFunc:th},uB=_t((e,t)=>e/t),s3=Kt(Ei,uB),h1={kernelName:Ei,backendName:"cpu",kernelFunc:s3};function q6(e,t,a){let n=e.shape,r=n[0],s=n[1],i=a.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),c=v.getTypedArrayFromDType("float32",p),d=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:n}=e,r=a,s=v.getTypedArrayFromDType(n.dtype,v.sizeFromShape(n.shape)),[i,o,l,u]=n.shape,p=r.data.get(n.dataId).values;for(let c=0;c=0&&x=0,()=>`GatherV2: the index value ${w} is not in [0, ${p-1}]`)}let c=o;o==null&&(c=0);let d=v.sizeFromShape(s.shape),h=C.segment_util.collectGatherOpShapeInfo(r,s,l,c),m=bt({inputs:{x:r},backend:a,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=bt({inputs:{x:s},backend:a,attrs:{shape:[h.batchSize,d/h.batchSize]}}),g=[h.batchSize,h.outerSize,d/h.batchSize,h.sliceSize],y=a.bufferSync(f),x=a.bufferSync(m),A=d6(x,y,g);return a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),a.makeTensorInfo(h.outputShape,A.dtype,A.values)}var SB={kernelName:nu,backendName:"cpu",kernelFunc:IB};function CB(e){let{inputs:t,backend:a}=e,{input:n}=t,r=v.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=r/s,o=bt({inputs:{x:n},backend:a,attrs:{shape:[i,s]}}),l=q6(o,!0,a),u=bt({inputs:{x:l},backend:a,attrs:{shape:n.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(l),u}var TB={kernelName:ep,backendName:"cpu",kernelFunc:CB},NB=ct(Bi,e=>Number.isFinite(e)?1:0,"bool"),RB={kernelName:Bi,backendName:"cpu",kernelFunc:NB},EB=ct(Vi,e=>Math.abs(e)===1/0?1:0,"bool"),MB={kernelName:Vi,backendName:"cpu",kernelFunc:EB},$B=ct(Ui,e=>Number.isNaN(e)?1:0,"bool"),PB={kernelName:Ui,backendName:"cpu",kernelFunc:$B};function _B(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=f6(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var FB={kernelName:qi,backendName:"cpu",kernelFunc:_B},DB=ct(Xi,e=>Math.log1p(e)),OB={kernelName:Xi,backendName:"cpu",kernelFunc:DB},zB=_t((e,t)=>e&&t),LB=Kt(Ki,zB,null,"bool"),WB={kernelName:Ki,backendName:"cpu",kernelFunc:LB},BB=ct(Yi,e=>e?0:1,"bool"),VB={kernelName:Yi,backendName:"cpu",kernelFunc:BB},UB=_t((e,t)=>e||t),GB=Kt(Zi,UB,null,"bool"),HB={kernelName:Zi,backendName:"cpu",kernelFunc:GB};function jB(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;Ie(r,"LRN");let u=r.shape[3],p=u-1,c=a.data.get(r.dataId).values,d=v.sizeFromShape(r.shape),h=new Float32Array(d);function m(f){let g=f%u,y=f-g+Math.max(0,g-s),x=f-g+Math.min(g+s,p),A=0;for(;y<=x;y++){let b=c[y];A+=b*b}return A}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,u,o,l),c;if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))c=rr({inputs:{x:r},backend:a});else{let d=a.data.get(r.dataId).values,h=v.computeStrides(r.shape),m=r3(d,r.shape,r.dtype,h,p,"max");c=a.makeTensorInfo(p.outShape,r.dtype,m.values)}return c}var JB={kernelName:Qi,backendName:"cpu",kernelFunc:ZB};function QB(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n;Ie(r,"maxPool3d");let p=C.computePool3DInfo(r.shape,s,i,1,o,l,u),c=a.data.get(r.dataId).values,d=G6(c,r.shape,r.dtype,v.computeStrides(r.shape),p,"max");return a.makeTensorInfo(d.shape,"float32",d.values)}var eV={kernelName:su,backendName:"cpu",kernelFunc:QB};function tV(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n;Ie([r,s],"maxPool3DGrad");let p=C.computePool3DInfo(s.shape,i,o,1,l,u),c=a.bufferSync(s),d=UL(c,p),h=p.strideDepth,m=p.strideHeight,f=p.strideWidth,g=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,A=p.effectiveFilterDepth,b=p.effectiveFilterHeight,w=p.effectiveFilterWidth,I=A-1-p.padInfo.front,T=w-1-p.padInfo.left,N=b-1-p.padInfo.top,M=_e(s.shape,"float32"),$=a.bufferSync(r);for(let E=0;E=p.outDepth||Math.floor(V)!==V))for(let Z=0;Z=p.outHeight||Math.floor(X)!==X))for(let re=0;re=p.outWidth||Math.floor(ee)!==ee)continue;let ge=A*b*w-1-d.get(E,V,X,ee,S),ie=H*b*w+Z*w+re,be=ge===ie?1:0;if(be===0)continue;let Ce=$.get(E,V,X,ee,S);q+=Ce*be}}}M.set(q,E,_,O,W,S)}return a.makeTensorInfo(M.shape,M.dtype,M.values)}var aV={kernelName:np,backendName:"cpu",kernelFunc:tV};function nV(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;Ie([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=C.computePool2DInfo(o.shape,l,u,1,p,c),h=a.data.get(o.dataId).values,m=_e(d.outShape,o.dtype,U6(h,o.shape,o.dtype,d).values),f=d.strideHeight,g=d.strideWidth,y=d.dilationHeight,x=d.dilationWidth,A=d.effectiveFilterHeight,b=d.effectiveFilterWidth,w=b-1-d.padInfo.left,I=A-1-d.padInfo.top,T=_e(o.shape,"float32"),N=a.data.get(r.dataId).values,M=_e(r.shape,"float32",N);for(let $=0;$=d.outHeight||Math.floor(G)!==G))for(let q=0;q=d.outWidth||Math.floor(H)!==H)continue;let V=A*b-1-m.get($,G,H,E),Z=U*b+q,X=V===Z?1:0;if(X===0)continue;let re=M.get($,G,H,E);P+=re*X}}T.set(P,$,S,_,E)}return a.makeTensorInfo(T.shape,T.dtype,T.values)}var rV={kernelName:ap,backendName:"cpu",kernelFunc:nV};function sV(e,t,a,n,r){let s=v.computeStrides(t),i=r3(e,t,a,s,r,"max"),o=U6(e,t,a,r,!0,n);return[i.values,o.values]}var iV={kernelName:iu,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=a;Ie(n,"MaxPoolWithArgmax");let u=l.data.get(n.dataId).values,p=C.computePool2DInfo(n.shape,r,s,[1,1],i),[c,d]=sV(u,n.shape,n.dtype,o,p),h=l.write(c,p.outShape,n.dtype),m=l.write(d,p.outShape,n.dtype);return[{dataId:h,shape:p.outShape,dtype:n.dtype},{dataId:m,shape:p.outShape,dtype:"int32"}]}};function oV(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=v.parseAxisParam(s,r.shape),l=C.computeOutAndReduceShapes(r.shape,o)[1],u=v.sizeFromShape(l),p=[],c=a.makeTensorInfo([],"float32",new Float32Array([u]));p.push(c);let d=is({inputs:{x:r},backend:a,attrs:{dtype:"float32"}});p.push(d);let h=s3({inputs:{a:d,b:c},backend:a});p.push(h);let m=Rp({inputs:{x:h},backend:a,attrs:{axis:s,keepDims:i}});return p.forEach(f=>a.disposeIntermediateTensorInfo(f)),m}var lV={kernelName:eo,backendName:"cpu",kernelFunc:oV};function uV(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;Ie(r,"min");let o=v.parseAxisParam(s,r.shape),l=o,u=C.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=Va({inputs:{x:r},backend:a,attrs:{perm:u}}),l=C.getInnerMostAxes(l.length,r.shape.length)),C.assertAxesAreInnerMostDims("min",l,p.shape.length);let[c,d]=C.computeOutAndReduceShapes(p.shape,l),h=v.sizeFromShape(d),m=v.makeZerosTypedArray(v.sizeFromShape(c),p.dtype),f=a.data.get(p.dataId).values;for(let y=0;yx[0]+r.shape[A]+x[1]),l=s.map(x=>x[0]),u=s.map((x,A)=>x[0]+r.shape[A]),p=i==="reflect"?0:1,c=a.data.get(r.dataId).values,d=r.shape.length,h=v.computeStrides(r.shape),m=v.sizeFromShape(o),f=o.length,g=v.computeStrides(o),y=v.getTypedArrayFromDType(r.dtype,m);for(let x=0;x=u[w]&&(A[w]=(u[w]-1)*2-A[w]+p);A=A.map((w,I)=>w-l[I]);let b=v.locToIndex(A,d,h);y[x]=c[b]}return{dataId:a.write(y,o,r.dtype),shape:o,dtype:r.dtype}}var cV={kernelName:ao,backendName:"cpu",kernelFunc:pV},hV=_t(((e,t)=>{let a=e%t;return e<0&&t<0||e>=0&&t>=0?a:(a+t)%t})),mV=Kt("Mod",hV),fV={kernelName:"Mod",backendName:"cpu",kernelFunc:mV},gV=Bl(X5());function K6(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=r.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);let l=v.parseAxisParam([o],r.shape),u=X6({inputs:{x:r},backend:a,attrs:{reductionIndices:l,keepDims:!1}}),p=C.expandShapeToKeepDim(u.shape,l),c=bt({inputs:{x:u},backend:a,attrs:{shape:p}}),d=a3({inputs:{a:r,b:c},backend:a}),h=s6({inputs:{x:d},backend:a}),m=Rp({inputs:{x:h},backend:a,attrs:{axis:l,keepDims:!1}}),f=bt({inputs:{x:m},backend:a,attrs:{shape:p}}),g=s3({inputs:{a:h,b:f},backend:a});return a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),g}var yV={kernelName:Eo,backendName:"cpu",kernelFunc:K6};function xV(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n;Ie(r,"multinomial");let l=o?r:K6({inputs:{logits:r},backend:a,attrs:{dim:-1}}),u=l.shape[0],p=l.shape[1],c=a.data.get(l.dataId).values,d=[u,s],h=v.makeZerosTypedArray(v.sizeFromShape(d),"int32");for(let m=0;m=0&&c[d]{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=th({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=Ll({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeIntermediateTensorInfo(p)),u}var PV={kernelName:uu,backendName:"cpu",kernelFunc:Z6};function _V(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;Ie(r,"pad");let o=s.map((y,x)=>y[0]+r.shape[x]+y[1]),l=s.map(y=>y[0]),u=a.data.get(r.dataId).values,p=v.sizeFromShape(r.shape),c=r.shape.length,d=v.computeStrides(r.shape),h=v.sizeFromShape(o),m=o.length,f=v.computeStrides(o),g=v.getTypedArrayFromDType(r.dtype,h);i!==0&&g.fill(i);for(let 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WV(e){let{inputs:t,backend:a}=e,{starts:n,limits:r,deltas:s}=t,i=a.data.get(n.dataId).values,o=a.data.get(r.dataId).values,l=a.data.get(s.dataId).values,[u,p]=I6(i,n.shape,n.dtype,o,r.shape,l,s.shape),c=a.makeTensorInfo([u.length],"int32",u),d=a.makeTensorInfo([p.length],n.dtype,p);return[c,d]}var BV={kernelName:hh,backendName:"cpu",kernelFunc:WV};function VV(e){let{inputs:t,backend:a,attrs:n}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=n,u=a.data.get(r.dataId).values,p=a.data.get(s.dataId).values,c=a.data.get(i.dataId).values,d=o.map(g=>a.data.get(g.dataId).values),h=o.map(g=>g.shape),[m,f]=S6(u,r.shape,p,s.shape,s.dtype,c,i.shape,d,h,l);return a.makeTensorInfo(m,s.dtype,f)}var UV={kernelName:mh,backendName:"cpu",kernelFunc:VV};function GV(e){let{backend:t,attrs:a}=e,{start:n,stop:r,dtype:s,step:i}=a,o=Zg(n,r,i,s);return t.makeTensorInfo([o.length],s,o)}var 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Se=B();Se.registerFlag("HAS_WEBGL",()=>Se.getNumber("WEBGL_VERSION")>0);Se.registerFlag("WEBGL_VERSION",()=>y1(2)?2:y1(1)?1:0);Se.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Se.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Se.get("WEBGL_VERSION")===2);Se.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Se.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Se.registerFlag("WEBGL_PACK",()=>Se.getBool("HAS_WEBGL"));Se.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_CLIP",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_REDUCE",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_LAZILY_UNPACK",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_CONV_IM2COL",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_PACK_CONV2DTRANSPOSE",()=>Se.getBool("WEBGL_PACK"));Se.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>xv(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>Av(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Se.getNumber("WEBGL_VERSION");return e===0?0:bv(e)});Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Se.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!fp.isMobile());Se.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>vv(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Se.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Se.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Se.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>wv(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_FENCE_API_ENABLED",()=>kv(Se.getNumber("WEBGL_VERSION")));Se.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Se.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Se.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(typeof e!="number")throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${e}.`);if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got 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es",t="in",a="out",n="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=B().getBool("WEBGL2_ISNAN_CUSTOM")?` bool isnan_custom(float val) { uint floatToUint = floatBitsToUint(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; @@ -100,15 +100,15 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `),{version:e,attribute:t,varyingVs:a,varyingFs:n,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function rl(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / ${r}`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function r0(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function LG(e,t){let a=e.length,n=e.map(s=>`${t}[${s}]`),r=new Array(a-1);r[a-2]=n[a-1];for(let s=a-3;s>=0;--s)r[s]=`(${r[s+1]} * ${n[s+1]})`;return r}function WG(e,t,a="index"){let n=e.map((s,i)=>i),r=LG(n,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${a} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${a} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function R3(e){let t=v.computeStrides(e).map(a=>a.toString());return` + `),{version:e,attribute:t,varyingVs:a,varyingFs:n,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function Bo(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / ${r}`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function zh(e,t,a="index"){let n=v.computeStrides(t);return n.map((r,s)=>{let i=`int ${e[s]} = ${a} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${a} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function LG(e,t){let a=e.length,n=e.map(s=>`${t}[${s}]`),r=new Array(a-1);r[a-2]=n[a-1];for(let s=a-3;s>=0;--s)r[s]=`(${r[s+1]} * ${n[s+1]})`;return r}function WG(e,t,a="index"){let n=e.map((s,i)=>i),r=LG(n,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${a} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${a} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function u3(e){let t=v.computeStrides(e).map(a=>a.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; } -`}function E3(){return` +`}function d3(){return` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } -`}var Gv=` +`}var Iv=` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -147,10 +147,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return c / 255.0; } -`,{getBroadcastDims:Hv}=C;function BG(e,t,a){let n=[];if(e.forEach(d=>{let h=v.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?n.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(n.push(`uniform sampler2D ${d.name};`),n.push(`uniform int offset${d.name};`)),a.enableShapeUniforms){let{uniformShape:m}=M3(a.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(m.length){case 1:n.push(`uniform int ${d.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${d.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${d.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${d.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${d.name}TexShape;`)}}),a.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}a.customUniforms&&a.customUniforms.forEach(d=>{n.push(`uniform ${d.type} ${d.name}${d.arrayIndex?`[${d.arrayIndex}]`:""};`)});let r=n.join(` +`,{getBroadcastDims:Sv}=C;function BG(e,t,a){let n=[];if(e.forEach(d=>{let h=v.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?n.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(n.push(`uniform sampler2D ${d.name};`),n.push(`uniform int offset${d.name};`)),a.enableShapeUniforms){let{uniformShape:m}=p3(a.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(m.length){case 1:n.push(`uniform int ${d.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${d.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${d.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${d.name}Shape;`);break;default:break}n.push(`uniform ivec2 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mH(e,t);case 3:return gH(e,t);case 4:return xH(e,t);case 5:return AH(e);case 6:return bH(e);default:throw new Error(`${a.length}-D input sampling is not yet supported`)}}function jv(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return uH(e);case 1:return pH(e,t);case 2:return hH(e,t);case 3:return fH(e,t);default:return yH(e,t)}}function VG(e,t,a=!1,n){let r="";a?r+=jv(e,n):r+=Xu(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(a?r+=vH(e,t):r+=wH(e,t)),r}function UG(e,t,a){switch(e.length){case 0:return qv();case 1:return QG(e,t,a);case 2:return oH(e,t,a);case 3:return tH(e,t,a);default:return nH(e,t,a)}}function GG(e,t,a){switch(e.length){case 0:return qv();case 1:return eH(e,t,a);case 2:return lH(e,t,a);case 3:return aH(e,t,a);case 4:return rH(e,t,a);case 5:return sH(e,t);case 6:return iH(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function HG(e){return` +`)}function Ru(e,t=!1){let a=e.shapeInfo.logicalShape;switch(a.length){case 0:return dH(e,t);case 1:return cH(e,t);case 2:return mH(e,t);case 3:return gH(e,t);case 4:return xH(e,t);case 5:return AH(e);case 6:return bH(e);default:throw new Error(`${a.length}-D input sampling is not yet supported`)}}function Cv(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return uH(e);case 1:return pH(e,t);case 2:return hH(e,t);case 3:return fH(e,t);default:return yH(e,t)}}function VG(e,t,a=!1,n){let r="";a?r+=Cv(e,n):r+=Ru(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(a?r+=vH(e,t):r+=wH(e,t)),r}function UG(e,t,a){switch(e.length){case 0:return Tv();case 1:return QG(e,t,a);case 2:return oH(e,t,a);case 3:return tH(e,t,a);default:return nH(e,t,a)}}function GG(e,t,a){switch(e.length){case 0:return Tv();case 1:return eH(e,t,a);case 2:return lH(e,t,a);case 3:return aH(e,t,a);case 4:return rH(e,t,a);case 5:return sH(e,t);case 6:return iH(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function HG(e){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${e.texture2D}(textureSampler, uv).r; } @@ -260,7 +260,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function qv(){return` +`;function Tv(){return` int getOutputCoords() { return 0; } @@ -357,10 +357,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${r0(["r","c","d"],e)} + ${zh(["r","c","d"],e)} return ivec3(r, c, d); } -`;let n=rl(["r","c","d"],e);return` +`;let n=Bo(["r","c","d"],e);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -414,10 +414,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${r0(["r","c","d","d2"],e)} + ${zh(["r","c","d","d2"],e)} return ivec4(r, c, d, d2); } - `;let n=rl(["r","c","d","d2"],e);return` + `;let n=Bo(["r","c","d","d2"],e);return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -425,7 +425,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n} return ivec4(r, c, d, d2); } - `}function sH(e,t){let a=rl(["r","c","d","d2","d3"],e);return` + `}function sH(e,t){let a=Bo(["r","c","d","d2","d3"],e);return` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -437,7 +437,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } - `}function iH(e,t){let a=rl(["r","c","d","d2","d3","d4"],e);return` + `}function iH(e,t){let a=Bo(["r","c","d","d2","d3","d4"],e);return` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -535,7 +535,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${e[1]}; return ivec2(r, c); } - `}function sl(e){return`offset${e}`}function uH(e){let t=e.name,a="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ra();return` + `}function Vo(e){return`offset${e}`}function uH(e){let t=e.name,a="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ra();return` vec4 ${a}() { return ${n.texture2D}(${t}, halfCR); } @@ -543,7 +543,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float ${n}() { return sampleTexture(${a}, halfCR); } - `;let i=sl(a);if(t)return` + `;let i=Vo(a);if(t)return` float ${n}() { vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], ${i}); return sampleTexture(${a}, uv); @@ -568,13 +568,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } `}function cH(e,t){let a=e.name,n="get"+a.charAt(0).toUpperCase()+a.slice(1);if(e.shapeInfo.isUniform)return` float ${n}(int index) { - ${Ku(e)} + ${Eu(e)} } `;let r=e.shapeInfo.texShape,s=r[0],i=r[1];if(i===1&&s===1)return` float ${n}(int index) { return sampleTexture(${a}, halfCR); } - `;let o=sl(a);return i===1?t?` + `;let o=Vo(a);return i===1?t?` float ${n}(int index) { vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${a}TexShape[0])); return sampleTexture(${a}, uv); @@ -638,17 +638,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0); return sampleTexture(${n}, uv); } - `}let{newShape:i,keptDims:o}=v.squeezeShape(a),l=i;if(l.length=1?p="coords = 0;":p=o.map(g=>`coords.${c[g+u]} = 0;`).join(` + `}function vH(e,t){let a=e.name,n=a.charAt(0).toUpperCase()+a.slice(1),r="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=Sv(e.shapeInfo.logicalShape,t.logicalShape),l=ft(i),u=i-s,p,c=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${c[g+u]} = 0;`).join(` `);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((g,y)=>`coords.${c[y+u]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,f=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=` return vec4(outputValue.xy, outputValue.xy); `;else if(m&&!f)i===1?h=` @@ -969,16 +969,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float ${r}() { return sampleTexture(${a}, resultUV); } - `;let u=ft(l),p=Hv(e.shapeInfo.logicalShape,t.logicalShape),c=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&p.length>=1?d="coords = 0;":d=p.map(f=>`coords.${h[f+c]} = 0;`).join(` + `;let u=ft(l),p=Sv(e.shapeInfo.logicalShape,t.logicalShape),c=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&p.length>=1?d="coords = 0;":d=p.map(f=>`coords.${h[f+c]} = 0;`).join(` `);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+c]}`).join(", "),` float ${r}() { ${u} coords = getOutputCoords(); ${d} return get${n}(${m}); } - `}function ft(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function M3(e,t,a){let{newShape:n,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!v.arraysEqual(t,a)&&n.lengthe[a]).join(", ")}function kH(e,t,a,n){let r=a.map((p,c)=>{let d={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(d.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[c],shapeInfo:d}}),s=r.map(p=>p.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=BG(r,i,t),l=kv(e.gl,o),u=e.createProgram(l);return B().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(e.buildVao(u),Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},Xv(e,t,u)))}function Xv(e,t,a){let n=[],r=[],s,i,o,l=null,u=null;u=e.getUniformLocation(a,"NAN",!1),B().getNumber("WEBGL_VERSION")===1&&(l=e.getUniformLocation(a,"INFINITY",!1));let p=!1;for(let c of t.variableNames){let d={name:c,uniform:e.getUniformLocation(a,c,p),offset:e.getUniformLocation(a,`offset${c}`,p)};t.enableShapeUniforms&&(d.shape=e.getUniformLocation(a,`${c}Shape`,p),d.texShape=e.getUniformLocation(a,`${c}TexShape`,p)),n.push(d)}if(t.enableShapeUniforms&&(s=e.getUniformLocation(a,"outShape",p),o=e.getUniformLocation(a,"outShapeStrides",p),i=e.getUniformLocation(a,"outTexShape",p)),t.customUniforms)for(let c of t.customUniforms)r.push(e.getUniformLocation(a,c.name,p));return{variablesLocations:n,customUniformLocations:r,infLoc:l,nanLoc:u,outShapeLocation:s,outShapeStridesLocation:o,outTexShapeLocation:i}}function S5(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((a,n)=>{let r=a.logicalShape,s=t[n],i=s.shape;if(!v.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(a.isUniform&&s.isUniform)return;let o=a.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function IH(e,t,a,n,r){t.program.enableShapeUniforms||(S5(t.inShapeInfos,a),S5([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),e.bindVertexArray(t.webGLProgram.vao),B().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN);for(let l=0;l{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:c}=M3(e.packedInputs,i.shape,l),d="",h="",m="";if(p.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];d=`${w[0]>1}_${w[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let w=v.computeStrides(p);m=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,x=C.getBroadcastDims(i.shape,a.shape),A=!e.packedInputs&&f===a.shape.length&&v.arraysEqual(l,a.texData.texShape),b=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${f}_${A}_${u?c:""}_${p.length}_${y}_${x}_${g}_${d}_${h}_${m}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+r+`${B().getNumber("WEBGL_VERSION")}`,s}function ya(e){return B().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var CH=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Qd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ra();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` + `}function ft(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function p3(e,t,a){let{newShape:n,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!v.arraysEqual(t,a)&&n.lengthe[a]).join(", ")}function kH(e,t,a,n){let r=a.map((p,c)=>{let d={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(d.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[c],shapeInfo:d}}),s=r.map(p=>p.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=BG(r,i,t),l=nv(e.gl,o),u=e.createProgram(l);return B().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(e.buildVao(u),Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},Nv(e,t,u)))}function Nv(e,t,a){let n=[],r=[],s,i,o,l=null,u=null;u=e.getUniformLocation(a,"NAN",!1),B().getNumber("WEBGL_VERSION")===1&&(l=e.getUniformLocation(a,"INFINITY",!1));let p=!1;for(let c of t.variableNames){let d={name:c,uniform:e.getUniformLocation(a,c,p),offset:e.getUniformLocation(a,`offset${c}`,p)};t.enableShapeUniforms&&(d.shape=e.getUniformLocation(a,`${c}Shape`,p),d.texShape=e.getUniformLocation(a,`${c}TexShape`,p)),n.push(d)}if(t.enableShapeUniforms&&(s=e.getUniformLocation(a,"outShape",p),o=e.getUniformLocation(a,"outShapeStrides",p),i=e.getUniformLocation(a,"outTexShape",p)),t.customUniforms)for(let c of t.customUniforms)r.push(e.getUniformLocation(a,c.name,p));return{variablesLocations:n,customUniformLocations:r,infLoc:l,nanLoc:u,outShapeLocation:s,outShapeStridesLocation:o,outTexShapeLocation:i}}function s5(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((a,n)=>{let r=a.logicalShape,s=t[n],i=s.shape;if(!v.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. 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Shape ${o} and ${l} must match`)})}function IH(e,t,a,n,r){t.program.enableShapeUniforms||(s5(t.inShapeInfos,a),s5([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),e.bindVertexArray(t.webGLProgram.vao),B().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN);for(let l=0;l{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:c}=p3(e.packedInputs,i.shape,l),d="",h="",m="";if(p.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];d=`${w[0]>1}_${w[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let w=v.computeStrides(p);m=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,x=C.getBroadcastDims(i.shape,a.shape),A=!e.packedInputs&&f===a.shape.length&&v.arraysEqual(l,a.texData.texShape),b=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${f}_${A}_${u?c:""}_${p.length}_${y}_${x}_${g}_${d}_${h}_${m}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+r+`${B().getNumber("WEBGL_VERSION")}`,s}function ya(e){return B().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var CH=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=_d.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ra();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?r0(["r","c","d"],e):rl(["r","c","d"],e)} + ${this.enableShapeUniforms?zh(["r","c","d"],e):Bo(["r","c","d"],e)} return ivec3(r, c, d); } @@ -996,9 +996,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},TH=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Qd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ra();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` + `}},TH=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=_d.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ra();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?r0(["r","c","d"],e):rl(["r","c","d"],e)} + ${this.enableShapeUniforms?zh(["r","c","d"],e):Bo(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1017,25 +1017,25 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } `}},NH=class{constructor(e){this.variableNames=["A"],this.outTexUsage=mn.DOWNLOAD;let t=Ra();this.outputShape=e,this.userCode=` - ${Gv} + ${Iv} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } `}},RH=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=mn.DOWNLOAD;let t=Ra();this.outputShape=e,this.userCode=` - ${Gv} + ${Iv} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } - `}},EH={R:0,G:1,B:2,A:3},C5=class{constructor(e,t=!1,a="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Ra();this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let s="";for(let i=0;ir8,createBufferFromOutputTexture:()=>o8,createFloat16MatrixTexture:()=>e8,createFloat16PackedMatrixTexture:()=>n8,createFloat32MatrixTexture:()=>Qv,createIndexBuffer:()=>Jv,createPackedMatrixTexture:()=>a8,createUnsignedBytesMatrixTexture:()=>t8,createVertexBuffer:()=>Zv,createVertexShader:()=>Yv,downloadByteEncodedFloatMatrixFromOutputTexture:()=>u8,downloadFloat32MatrixFromBuffer:()=>l8,downloadMatrixFromPackedOutputTexture:()=>p8,downloadPackedMatrixFromBuffer:()=>d8,getInternalFormatForFloat16MatrixTexture:()=>P3,getInternalFormatForFloat16PackedMatrixTexture:()=>D3,getInternalFormatForFloat32MatrixTexture:()=>$3,getInternalFormatForPackedMatrixTexture:()=>F3,getInternalFormatForUnsignedBytesMatrixTexture:()=>_3,uploadDenseMatrixToTexture:()=>s8,uploadPixelDataToTexture:()=>i8});function Yv(e){let t=Ra(),a=`${t.version} + `}},Rv={};Ze(Rv,{bindVertexProgramAttributeStreams:()=>zv,createBufferFromOutputTexture:()=>Bv,createFloat16MatrixTexture:()=>_v,createFloat16PackedMatrixTexture:()=>Ov,createFloat32MatrixTexture:()=>Pv,createIndexBuffer:()=>$v,createPackedMatrixTexture:()=>Dv,createUnsignedBytesMatrixTexture:()=>Fv,createVertexBuffer:()=>Mv,createVertexShader:()=>Ev,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Uv,downloadFloat32MatrixFromBuffer:()=>Vv,downloadMatrixFromPackedOutputTexture:()=>Hv,downloadPackedMatrixFromBuffer:()=>Gv,getInternalFormatForFloat16MatrixTexture:()=>h3,getInternalFormatForFloat16PackedMatrixTexture:()=>g3,getInternalFormatForFloat32MatrixTexture:()=>c3,getInternalFormatForPackedMatrixTexture:()=>f3,getInternalFormatForUnsignedBytesMatrixTexture:()=>m3,uploadDenseMatrixToTexture:()=>Lv,uploadPixelDataToTexture:()=>Wv});function Ev(e){let t=Ra(),a=`${t.version} precision highp float; ${t.attribute} vec3 clipSpacePos; ${t.attribute} vec2 uv; @@ -1107,7 +1107,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, void main() { gl_Position = vec4(clipSpacePos, 1); resultUV = uv; - }`;return wv(e,a)}function Zv(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return Cv(e,t)}function Jv(e){let t=new Uint16Array([0,1,2,2,1,3]);return Tv(e,t)}function Zp(e,t,a,n,r,s){Rv(t,a);let i=Nv(e),o=e.TEXTURE_2D;return ce(e,()=>e.bindTexture(o,i)),ce(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ce(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ce(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),ce(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),B().getNumber("WEBGL_VERSION")===1?ce(e,()=>e.texImage2D(o,0,n,t,a,0,r,s,null)):ce(e,()=>e.texStorage2D(o,1,n,t,a)),ce(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[a,t]}}function $3(e){return e.internalFormatFloat}function Qv(e,t,a,n){let[r,s]=Yp(t,a);return Zp(e,r,s,$3(n),n.textureFormatFloat,e.FLOAT)}function P3(e){return e.internalFormatHalfFloat}function e8(e,t,a,n){let[r,s]=Yp(t,a);return Zp(e,r,s,P3(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function _3(e){return e.downloadTextureFormat}function t8(e,t,a,n){let[r,s]=Yp(t,a);return Zp(e,r,s,_3(n),e.RGBA,e.UNSIGNED_BYTE)}function F3(e){return e.internalFormatPackedFloat}function a8(e,t,a,n){let[r,s]=ju(t,a);return Zp(e,r,s,F3(n),e.RGBA,e.FLOAT)}function D3(e){return e.internalFormatPackedHalfFloat}function n8(e,t,a,n){let[r,s]=ju(t,a);return Zp(e,r,s,D3(n),e.RGBA,n.textureTypeHalfFloat)}function r8(e,t,a){return ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,a)),F1(e,t,"clipSpacePos",a,3,20,0)&&F1(e,t,"uv",a,2,20,12)}function s8(e,t,a,n,r,s){ce(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(a*n*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(a*n*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),B().getNumber("WEBGL_VERSION")===2?ce(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,a,n,e.RGBA,o,i)):ce(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,a,n,0,e.RGBA,o,i)),ce(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function i8(e,t,a){ce(e,()=>e.bindTexture(e.TEXTURE_2D,t)),a.data instanceof Uint8Array?B().getNumber("WEBGL_VERSION")===2?ce(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,a.width,a.height,e.RGBA,e.UNSIGNED_BYTE,a.data)):ce(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,a.width,a.height,0,e.RGBA,e.UNSIGNED_BYTE,a.data)):B().getNumber("WEBGL_VERSION")===2?ce(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,a)):ce(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,a)),ce(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function o8(e,t,a,n){let r=e.createBuffer();ce(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*a;return ce(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),ce(e,()=>e.readPixels(0,0,a,t,e.RGBA,e.FLOAT,0)),ce(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function l8(e,t,a){let n=e,r=new Float32Array(a);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,r),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),r}function u8(e,t,a,n){let[r,s]=Yp(t,a),i=4,o=new Uint8Array(TG(t*a,i));return ce(e,()=>e.readPixels(0,0,r,s,n.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function d8(e,t,a,n,r,s,i,o){let l=e,u=new Float32Array(NG(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function p8(e,t,a){let n=new Float32Array(t*a*4);return ce(e,()=>e.readPixels(0,0,a,t,e.RGBA,e.FLOAT,n)),n}var Hl=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=B().getNumber("WEBGL_VERSION");if(e!=null?(this.gl=e,n0(t,e)):this.gl=Bn(t),e=this.gl,B().getNumber("WEBGL_VERSION")===2){let r=e;this.createVertexArray=()=>ce(r,()=>r.createVertexArray()),this.bindVertexArray=s=>ce(r,()=>r.bindVertexArray(s)),this.deleteVertexArray=s=>ce(r,()=>r.deleteVertexArray(s)),this.getVertexArray=()=>ce(r,()=>r.getParameter(r.VERTEX_ARRAY_BINDING))}else if(e!=null){let r=e.getExtension("OES_vertex_array_object");if(r==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ce(e,()=>r.createVertexArrayOES()),this.bindVertexArray=s=>ce(e,()=>r.bindVertexArrayOES(s)),this.deleteVertexArray=s=>ce(e,()=>r.deleteVertexArrayOES(s)),this.getVertexArray=()=>ce(e,()=>e.getParameter(r.VERTEX_ARRAY_BINDING_OES))}let a="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),B().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Rd(this.gl,r),fn(this.gl,s))this.textureHalfFloatExtension=Rd(this.gl,s);else if(B().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(a),fn(this.gl,n))this.colorBufferHalfFloatExtension=Rd(this.gl,n);else if(B().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(a="EXT_color_buffer_float",fn(this.gl,a))this.colorBufferFloatExtension=this.gl.getExtension(a);else if(fn(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Zv(this.gl),this.indexBuffer=Jv(this.gl),this.framebuffer=Ev(this.gl),this.textureConfig=T3(this.gl,this.textureHalfFloatExtension)}get debug(){return B().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;ce(e,()=>e.finish()),ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ce(e,()=>e.deleteFramebuffer(this.framebuffer)),ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ce(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Qv(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),e8(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),t8(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),i8(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,a,n){this.throwIfDisposed(),s8(this.gl,e,t,a,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),n8(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),a8(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(D1(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,a){return this.downloadMatrixDriver(e,()=>u8(this.gl,t,a,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,a,n,r,s){return d8(this.gl,e,t,a,n,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return l8(this.gl,e,t)}createBufferFromTexture(e,t,a){this.bindTextureToFrameBuffer(e);let n=o8(this.gl,t,a,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,a;if(B().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,r=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),a=()=>{let 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t=this.gl;ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),r8(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&nh(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,a=!0){return this.throwIfDisposed(),a?$v(this.gl,e,t):Pv(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,a){this.throwIfDisposed(),this.throwIfNoProgram(),_v(this.gl,e,t,a)}setOutputMatrixTexture(e,t,a){this.setOutputMatrixTextureDriver(e,a,t)}setOutputPackedMatrixTexture(e,t,a){this.throwIfDisposed();let[n,r]=ju(t,a);this.setOutputMatrixTextureDriver(e,n,r)}setOutputMatrixWriteRegion(e,t,a,n){this.setOutputMatrixWriteRegionDriver(a,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,a,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&nh(this.gl,this.program),Ed(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and 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t=this.gl,a=this.getQueryTimerExtensionWebGL2();t.endQuery(a.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let a=this.gl;return a.getQueryParameter(e,a.QUERY_RESULT)/1e6}else{let a=this.getQueryTimerExtensionWebGL1();return a.getQueryObjectEXT(e,a.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let a=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=a.getQueryParameter(e,a.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let a=this.getQueryTimerExtensionWebGL1(),n=a.getQueryObjectEXT(e,a.QUERY_RESULT_AVAILABLE_EXT);return 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this.unbindTextureToFrameBuffer(),a}setOutputMatrixTextureDriver(e,t,a){this.throwIfDisposed();let n=this.gl;rh(n,e,this.framebuffer),this.debug&&Ed(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,a)),ce(n,()=>n.scissor(0,0,t,a))}setOutputMatrixWriteRegionDriver(e,t,a,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,a,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function $H(e){let t=0;for(;t`${e}.${a}`)}function ka(e,t){return t===1?[e]:f8(e,t)}function Ij(e,t){if(e===1)return"rc";let a="";for(let n=0;ne.bindTexture(o,i)),ce(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ce(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ce(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),ce(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),B().getNumber("WEBGL_VERSION")===1?ce(e,()=>e.texImage2D(o,0,n,t,a,0,r,s,null)):ce(e,()=>e.texStorage2D(o,1,n,t,a)),ce(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[a,t]}}function c3(e){return e.internalFormatFloat}function Pv(e,t,a,n){let[r,s]=Ep(t,a);return Mp(e,r,s,c3(n),n.textureFormatFloat,e.FLOAT)}function h3(e){return e.internalFormatHalfFloat}function _v(e,t,a,n){let[r,s]=Ep(t,a);return Mp(e,r,s,h3(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function m3(e){return e.downloadTextureFormat}function Fv(e,t,a,n){let[r,s]=Ep(t,a);return Mp(e,r,s,m3(n),e.RGBA,e.UNSIGNED_BYTE)}function f3(e){return e.internalFormatPackedFloat}function Dv(e,t,a,n){let[r,s]=Tu(t,a);return Mp(e,r,s,f3(n),e.RGBA,e.FLOAT)}function g3(e){return e.internalFormatPackedHalfFloat}function Ov(e,t,a,n){let[r,s]=Tu(t,a);return Mp(e,r,s,g3(n),e.RGBA,n.textureTypeHalfFloat)}function zv(e,t,a){return ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,a)),f1(e,t,"clipSpacePos",a,3,20,0)&&f1(e,t,"uv",a,2,20,12)}function Lv(e,t,a,n,r,s){ce(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(a*n*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(a*n*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),B().getNumber("WEBGL_VERSION")===2?ce(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,a,n,e.RGBA,o,i)):ce(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,a,n,0,e.RGBA,o,i)),ce(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Wv(e,t,a){ce(e,()=>e.bindTexture(e.TEXTURE_2D,t)),a.data instanceof 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ce(e,()=>e.readPixels(0,0,r,s,n.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function Gv(e,t,a,n,r,s,i,o){let l=e,u=new Float32Array(NG(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function Hv(e,t,a){let n=new Float32Array(t*a*4);return ce(e,()=>e.readPixels(0,0,a,t,e.RGBA,e.FLOAT,n)),n}var Nl=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=B().getNumber("WEBGL_VERSION");if(e!=null?(this.gl=e,Oh(t,e)):this.gl=Bn(t),e=this.gl,B().getNumber("WEBGL_VERSION")===2){let r=e;this.createVertexArray=()=>ce(r,()=>r.createVertexArray()),this.bindVertexArray=s=>ce(r,()=>r.bindVertexArray(s)),this.deleteVertexArray=s=>ce(r,()=>r.deleteVertexArray(s)),this.getVertexArray=()=>ce(r,()=>r.getParameter(r.VERTEX_ARRAY_BINDING))}else if(e!=null){let r=e.getExtension("OES_vertex_array_object");if(r==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ce(e,()=>r.createVertexArrayOES()),this.bindVertexArray=s=>ce(e,()=>r.bindVertexArrayOES(s)),this.deleteVertexArray=s=>ce(e,()=>r.deleteVertexArrayOES(s)),this.getVertexArray=()=>ce(e,()=>e.getParameter(r.VERTEX_ARRAY_BINDING_OES))}let a="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),B().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=dd(this.gl,r),fn(this.gl,s))this.textureHalfFloatExtension=dd(this.gl,s);else if(B().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(a),fn(this.gl,n))this.colorBufferHalfFloatExtension=dd(this.gl,n);else if(B().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(a="EXT_color_buffer_float",fn(this.gl,a))this.colorBufferFloatExtension=this.gl.getExtension(a);else if(fn(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Mv(this.gl),this.indexBuffer=$v(this.gl),this.framebuffer=dv(this.gl),this.textureConfig=o3(this.gl,this.textureHalfFloatExtension)}get debug(){return B().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;ce(e,()=>e.finish()),ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ce(e,()=>e.deleteFramebuffer(this.framebuffer)),ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ce(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Pv(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),_v(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Fv(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Wv(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,a,n){this.throwIfDisposed(),Lv(this.gl,e,t,a,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Ov(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Dv(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(g1(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,a){return this.downloadMatrixDriver(e,()=>Uv(this.gl,t,a,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,a,n,r,s){return Gv(this.gl,e,t,a,n,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Vv(this.gl,e,t)}createBufferFromTexture(e,t,a){this.bindTextureToFrameBuffer(e);let n=Bv(this.gl,t,a,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,a;if(B().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,r=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),a=()=>{let s=n.clientWaitSync(r,0,0);return s===n.ALREADY_SIGNALED||s===n.CONDITION_SATISFIED},t=r}else B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),a=()=>this.isQueryAvailable(t,B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):a=()=>!0;return{query:t,isFencePassed:a}}downloadMatrixFromPackedTexture(e,t,a){return this.downloadMatrixDriver(e,()=>Hv(this.gl,t,a))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=Ev(t));let a=rv(t);ce(t,()=>t.attachShader(a,this.vertexShader)),ce(t,()=>t.attachShader(a,e)),sv(t,a);let n=Object.assign(a,{vao:this.createVertexArray()});return this.debug&&Oc(t,n),n}buildVao(e){this.setProgram(e),this.bindVertexArray(e.vao);let t=this.gl;ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),zv(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Oc(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,a=!0){return this.throwIfDisposed(),a?cv(this.gl,e,t):hv(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,a){this.throwIfDisposed(),this.throwIfNoProgram(),mv(this.gl,e,t,a)}setOutputMatrixTexture(e,t,a){this.setOutputMatrixTextureDriver(e,a,t)}setOutputPackedMatrixTexture(e,t,a){this.throwIfDisposed();let[n,r]=Tu(t,a);this.setOutputMatrixTextureDriver(e,n,r)}setOutputMatrixWriteRegion(e,t,a,n){this.setOutputMatrixWriteRegionDriver(a,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,a,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Oc(this.gl,this.program),pd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}ce(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ce(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=dd(this.gl,B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let a=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=a.createQuery();return a.beginQuery(n.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,a=this.getQueryTimerExtensionWebGL2();t.endQuery(a.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let a=this.gl;return a.getQueryParameter(e,a.QUERY_RESULT)/1e6}else{let a=this.getQueryTimerExtensionWebGL1();return a.getQueryObjectEXT(e,a.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let a=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=a.getQueryParameter(e,a.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let a=this.getQueryTimerExtensionWebGL1(),n=a.getQueryObjectEXT(e,a.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=$H(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:a}=this.itemsToPoll[t];a()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let a;"setTimeoutCustom"in B().platform&&(a=B().platform.setTimeoutCustom.bind(B().platform)),v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,a)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),zc(this.gl,e,this.framebuffer),this.debug&&pd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(zc(this.gl,this.outputTexture,this.framebuffer),this.debug&&pd(this.gl)):g1(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let a=t();return this.unbindTextureToFrameBuffer(),a}setOutputMatrixTextureDriver(e,t,a){this.throwIfDisposed();let n=this.gl;zc(n,e,this.framebuffer),this.debug&&pd(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,a)),ce(n,()=>n.scissor(0,0,t,a))}setOutputMatrixWriteRegionDriver(e,t,a,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,a,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function $H(e){let t=0;for(;t`${e}.${a}`)}function ka(e,t){return t===1?[e]:Kv(e,t)}function Ij(e,t){if(e===1)return"rc";let a="";for(let n=0;n= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), cEdge ? 0. : getA(${t[1]}), rEdge ? 0. : getA(${t[2]}), - rEdge || cEdge ? 0. : getA(${t[3]})`}},g8=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length);let a="";for(let n=0;n<4;n++){let r="thisRC = rc;";n%2===1&&(r+="thisRC.z += 1;"),n>1&&(r+="thisRC.y += 1;"),a+=` + rEdge || cEdge ? 0. : getA(${t[3]})`}},Yv=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length);let a="";for(let n=0;n<4;n++){let r="thisRC = rc;";n%2===1&&(r+="thisRC.z += 1;"),n>1&&(r+="thisRC.y += 1;"),a+=` ${r} ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); @@ -1147,7 +1147,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n>0?"}":""} `}this.userCode=` ${Cj(t,this.enableShapeUniforms)} - ${this.enableShapeUniforms?E3():R3(e)} + ${this.enableShapeUniforms?d3():u3(e)} void main() { ivec3 rc = getOutputCoords(); @@ -1164,10 +1164,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } `}};function Cj(e,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?WG(["r","c","d"],"inputShape"):rl(["r","c","d"],e)} + ${t?WG(["r","c","d"],"inputShape"):Bo(["r","c","d"],e)} return ivec3(r, c, d); } - `}var Tj=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,a){let n=N5(t,a),r=R5(e,n,a);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=T5(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,a);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return n===pa.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===pa.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===pa.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===pa.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===pa.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,a,n){if(this.freeTextures==null)return;let r=N5(a,n),s=R5(t,r,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=T5(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=B().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Nj(e,t){let a=e;if(t===a.R32F)return 4;if(t===a.R16F)return 2;if(t===a.RGBA32F||t===e.RGBA)return 16;if(t===a.RGBA16F)return 8;if(t===a.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function T5(e,t,a,n,r){let s=Rj(t,n),i;if(r){let[l,u]=ju(e[0],e[1]);i=l*u}else{let[l,u]=Yp(e[0],e[1]);i=l*u}let o=Nj(a,s);return i*o}function Rj(e,t){switch(e){case pa.PACKED_2X2_FLOAT32:return F3(t);case pa.PACKED_2X2_FLOAT16:return D3(t);case pa.UNPACKED_FLOAT32:return $3(t);case pa.UNPACKED_FLOAT16:return P3(t);case pa.PACKED_4X1_UNSIGNED_BYTE:return _3(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function Ej(e){return B().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?pa.PACKED_2X2_FLOAT32:pa.UNPACKED_FLOAT32:e?pa.PACKED_2X2_FLOAT16:pa.UNPACKED_FLOAT16}function N5(e,t){if(e===mn.UPLOAD)return pa.PACKED_2X2_FLOAT32;if(e===mn.RENDER||e==null)return Ej(t);if(e===mn.DOWNLOAD||e===mn.PIXELS)return pa.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function R5(e,t,a){return`${e[0]}_${e[1]}_${t}_${a}`}var Jn=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` + `}var Tj=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,a){let n=l5(t,a),r=u5(e,n,a);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=o5(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,a);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].pop();return this.usedTextures[r].push(o),o}let i;return n===pa.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===pa.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===pa.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===pa.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===pa.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,a,n){if(this.freeTextures==null)return;let r=l5(a,n),s=u5(t,r,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=o5(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=B().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l&&l.indexOf(e);if(u==null||u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[u]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Nj(e,t){let a=e;if(t===a.R32F)return 4;if(t===a.R16F)return 2;if(t===a.RGBA32F||t===e.RGBA)return 16;if(t===a.RGBA16F)return 8;if(t===a.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function o5(e,t,a,n,r){let s=Rj(t,n),i;if(r){let[l,u]=Tu(e[0],e[1]);i=l*u}else{let[l,u]=Ep(e[0],e[1]);i=l*u}let o=Nj(a,s);return i*o}function Rj(e,t){switch(e){case pa.PACKED_2X2_FLOAT32:return f3(t);case pa.PACKED_2X2_FLOAT16:return g3(t);case pa.UNPACKED_FLOAT32:return c3(t);case pa.UNPACKED_FLOAT16:return h3(t);case pa.PACKED_4X1_UNSIGNED_BYTE:return m3(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function Ej(e){return B().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?pa.PACKED_2X2_FLOAT32:pa.UNPACKED_FLOAT32:e?pa.PACKED_2X2_FLOAT16:pa.UNPACKED_FLOAT16}function l5(e,t){if(e===mn.UPLOAD)return pa.PACKED_2X2_FLOAT32;if(e===mn.RENDER||e==null)return Ej(t);if(e===mn.DOWNLOAD||e===mn.PIXELS)return pa.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function u5(e,t,a){return`${e[0]}_${e[1]}_${t}_${a}`}var Jn=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } @@ -1178,7 +1178,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},Mn="if (isnan(x)) return x;",Mj="return x;",E5="return abs(x);",$j="return (x >= 0.0) ? x : (exp(x) - 1.0);",Pj=Mn+` + `}},Mn="if (isnan(x)) return x;",Mj="return x;",d5="return abs(x);",$j="return (x >= 0.0) ? x : (exp(x) - 1.0);",Pj=Mn+` return (x < 0.0) ? 0.0 : x; `,_j=Mn+` return (x < 0.0) ? 0.0 : min(6.0, x); @@ -1229,10 +1229,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(getChannel(packedInput, ${i})); } - `}},Vj=En.whereImpl,Uj=1e-7,Gj=1e-4,J2={};function Hj(e){return e in J2||(J2[e]={}),J2[e]}var jj=B().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),qj=600;function Xj(){return B().global.screen==null?1024:B().global.screen.height*B().global.screen.width*window.devicePixelRatio*qj/1024/1024}var Jp=class y8 extends su{nextDataId(){return y8.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!B().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let a;if(t!=null){if(t instanceof Hl)a=t;else{let n=Bn(B().getNumber("WEBGL_VERSION"),t);a=new Hl(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Bn(B().getNumber("WEBGL_VERSION"));a=new Hl(n),this.binaryCache=Hj(B().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=a,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Tj(this.gpgpu),this.numMBBeforeWarning=Xj(),this.texData=new op(this,It())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,a,n,r,s,i){let o=this.makeTensorInfo(a,n),l=this.texData.get(o.dataId);l.isPacked=!1,l.texture={texture:t,texShape:[r,s]},l.texShape=[r,s];let u=Md(a),p=new C5(u,!1,i),c=this.runWebGLProgram(p,[o],n,[[r,s]]);return c.shape=a,l.texture=null,this.disposeIntermediateTensorInfo(o),c.dataId}write(t,a,n){if((B().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||B().getBool("DEBUG"))&&this.checkNumericalProblems(t),n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:a,dtype:n,values:t,usage:mn.UPLOAD,refCount:1}),r}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let a=this.texData.get(t);a.refCount++}decRef(t){if(this.texData.has(t)){let a=this.texData.get(t);a.refCount--}}move(t,a,n,r,s){if(B().getBool("DEBUG")&&this.checkNumericalProblems(a),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:r,values:a,usage:mn.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let a=this.texData.get(t),{values:n,dtype:r,complexTensorInfos:s,slice:i,shape:o,isPacked:l}=a;if(i!=null){let d;l?d=new qr(o,Vr):d=new Jn(o,Vr);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:r}],r),m=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),m}if(n!=null)return this.convertAndCacheOnCPU(t);if(r==="string")return n;let u=this.activeTimers!=null,p;u&&(p=v.now());let c;if(r==="complex64"){let d=this.readSync(s.real.dataId),h=this.readSync(s.imag.dataId);c=C.mergeRealAndImagArrays(d,h)}else c=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=v.now()-p),this.convertAndCacheOnCPU(t,c)}async read(t){if(this.pendingRead.has(t)){let m=this.pendingRead.get(t);return new Promise(f=>m.push(f))}let a=this.texData.get(t),{values:n,shape:r,slice:s,dtype:i,complexTensorInfos:o,isPacked:l}=a;if(s!=null){let m;l?m=new qr(r,Vr):m=new Jn(r,Vr);let f=this.runWebGLProgram(m,[{dataId:t,shape:r,dtype:i}],i),g=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(B().getBool("DEBUG")&&!B().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&B().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,p;if(i!=="complex64"&&B().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(t);let m=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(m.texture.texture,...Zc(r))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(i==="complex64"){let m=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=m[0],g=m[1];c=C.mergeRealAndImagArrays(f,g)}else if(u==null)c=this.getValuesFromTexture(t);else{let m=v.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,m)}if(p!=null&&this.disposeIntermediateTensorInfo(p),u!=null){let m=this.gpgpu.gl;ce(m,()=>m.deleteBuffer(u))}let d=this.convertAndCacheOnCPU(t,c),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(m=>m(d)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&It().removeDataId(t,this),this.pendingDeletes--),d}readToGPU(t,a={}){let n=this.texData.get(t),{values:r,shape:s,slice:i,dtype:o,isPacked:l,texture:u}=n;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let h;l?h=new qr(s,Vr):h=new Jn(s,Vr);let m=this.runWebGLProgram(h,[{dataId:t,shape:s,dtype:o}],o),f=this.readToGPU(m,a);return this.disposeIntermediateTensorInfo(m),f}if(u==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let p=this.decode(t,a.customTexShape),c=It().makeTensorFromTensorInfo(p),d=this.texData.get(p.dataId);return Object.assign({tensorRef:c},d.texture)}bufferSync(t){let a=this.readSync(t.dataId);if(t.dtype==="string")try{let n=a.map(r=>v.decodeString(r));return _e(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return _e(t.shape,t.dtype,a)}checkNumericalProblems(t){if(t!=null)for(let a=0;a0}time(t){let a=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),i=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=a,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);o.kernelMs=v.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(t){return B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=v.now(),t)}async getQueryTime(t){if(B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let a=t;return a.endMs-a.startMs}disposeData(t,a=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(a?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!a&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,a),this.disposeData(n.imag.dataId,a)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:a,dtype:n,texShape:r,usage:s,isPacked:i,slice:o}=this.texData.get(t),l=o&&o.origDataId||t,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),a!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(a,r,s,i)));let p=this.texData.get(t);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,a=jj){return B().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)0&&v.isString(n[0])){let s=n.map(i=>v.encodeString(i));r=this.write(s,t,a)}else r=this.write(n,t,a);return this.texData.get(r).usage=null,{dataId:r,shape:t,dtype:a}}makeOutput(t,a,n){return It().makeTensorFromTensorInfo(this.makeTensorInfo(t,a,n),this)}unpackTensor(t){let a=new Bj(t.shape);return this.runWebGLProgram(a,[t],t.dtype)}packTensor(t){let a=new Sj(t.shape);return this.runWebGLProgram(a,[t],t.dtype,null,!0)}packedReshape(t,a){let n=[ai(t.shape),...ni(t.shape)],r={dtype:t.dtype,shape:n,dataId:t.dataId},s=[ai(a),...ni(a)],i=new g8(s,n),o=!0,l=[n],u=this.runWebGLProgram(i,[r],t.dtype,l,o);return{dataId:u.dataId,shape:a,dtype:u.dtype}}decode(t,a){let n=this.texData.get(t),{isPacked:r,shape:s,dtype:i}=n;if(a!=null){let d=v.sizeFromShape(s),h=a[0]*a[1]*4;v.assert(d<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=Md(s),l;r?l=new TH(o):l=new CH(o);let u=!0,p=[a!=null?a:Zc(o)],c=this.runWebGLProgram(l,[{shape:o,dtype:i,dataId:t}],i,p,u,a);return{dtype:i,shape:s,dataId:c.dataId}}runWebGLProgram(t,a,n,r,s=!1,i){let o=this.makeTensorInfo(t.outputShape,n),l=this.texData.get(o.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===Qd.DENSE){let y=i!=null?i:Zc(t.outputShape);l.texShape=y.map(x=>x*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),v.sizeFromShape(o.shape)===0)return l.values=v.getTypedArrayFromDType(o.dtype,0),o;let u=[],p=a.map(y=>{if(y.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let x=this.texData.get(y.dataId);if(x.texture==null){if(!t.packedInputs&&v.sizeFromShape(y.shape)<=B().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:y.shape,texData:null,isUniform:!0,uniformValues:x.values};t.packedInputs&&(x.isPacked=!0,x.shape=y.shape)}if(this.uploadToGPU(y.dataId),!!x.isPacked!=!!t.packedInputs)y=x.isPacked?this.unpackTensor(y):this.packTensor(y),u.push(y),x=this.texData.get(y.dataId);else if(x.isPacked&&!ep(x.shape,y.shape)){let A=y,b=y.shape;y.shape=x.shape,y=this.packedReshape(y,b),u.push(y),x=this.texData.get(y.dataId),A.shape=b}return{shape:y.shape,texData:x,isUniform:!1}});this.uploadToGPU(o.dataId);let c={shape:o.shape,texData:l,isUniform:!1},d=SH(t,p,c),h=this.getAndSaveBinary(d,()=>kH(this.gpgpu,t,p,c)),m=this.activeTimers!=null,f;m&&(f=this.startTimer()),B().get("ENGINE_COMPILE_ONLY")||IH(this.gpgpu,h,p,c,r),u.forEach(y=>this.disposeIntermediateTensorInfo(y)),m&&(f=this.endTimer(f),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(f)}));let g=B().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let y=v.now();y-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=y)}if(!B().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let y=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),y}return o}compileAndRun(t,a,n,r,s=!1){return n=n||a[0].dtype,this.runWebGLProgram(t,a,n,r,s)}getAndSaveBinary(t,a){return t in this.binaryCache||(this.binaryCache[t]=a()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(B().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!B().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=B().getBool("DEBUG");B().set("DEBUG",!1);let a=this.abs(Ge(1e-8)).dataSync()[0];if(B().set("DEBUG",t),a>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Uj:Gj}uploadToGPU(t){let a=this.texData.get(t),{shape:n,dtype:r,values:s,texture:i,usage:o,isPacked:l}=a;if(i!=null)return;let u=this.activeTimers!=null,p;u&&(p=v.now());let c=a.texShape;if(c==null&&(c=Ov(n,l),a.texShape=c),s!=null){let d=Md(n),h,m=c[1],f=c[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(l||!g)&&([m,f]=ju(c[0],c[1])),l?h=new MH(d,g):h=new C5(d,g);let y=g?[f,m]:c,x=this.makeTensorInfo(y,r),A=this.texData.get(x.dataId);g?A.usage=mn.PIXELS:A.usage=mn.UPLOAD,A.texShape=y,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(x.dataId),m,f,s);let b=[[f,m]],w=this.runWebGLProgram(h,[x],r,b,!0),I=this.texData.get(w.dataId);a.texShape=I.texShape,a.isPacked=I.isPacked,a.usage=I.usage,B().get("ENGINE_COMPILE_ONLY")?this.disposeData(w.dataId):(a.texture=I.texture,a.values=null,this.texData.delete(w.dataId)),this.disposeIntermediateTensorInfo(x),u&&(this.uploadWaitMs+=v.now()-p)}else{let d=this.acquireTexture(c,o,r,l);a.texture=d}}convertAndCacheOnCPU(t,a){let n=this.texData.get(t),{dtype:r}=n;return a!=null&&(n.values=Kj(a,r)),n.values}acquireTexture(t,a,n,r){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,a,r)}computeBytes(t,a){return t[0]*t[1]*v.bytesPerElement(a)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,a]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(a));return Promise.all(t)}else{for(let[,a]of Object.entries(this.binaryCache)){let n=new Promise(r=>{try{this.checkCompletion_(a),r(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await G7(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(N3(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:a,customUniformLocations:n,infLoc:r,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:o,outTexShapeLocation:l}=Xv(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=a,t.customUniformLocations=n,t.infLoc=r,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=o,t.outTexShapeLocation=l}}createTensorFromGPUData(t,a,n){t.channels=t.channels||"RGBA";let{texture:r,height:s,width:i,channels:o}=t,l=It().backend;if(!l.gpgpu.gl.isTexture(r))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=l.writeTexture(r,a,n,s,i,o);return It().makeTensorFromDataId(u,a,n,l)}};Jp.nextDataId=0;function Kj(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let a=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;nnew Jp,2);var Zj={forceHalfFloat:x8},z3=` + `}},Vj=En.whereImpl,Uj=1e-7,Gj=1e-4,$2={};function Hj(e){return e in $2||($2[e]={}),$2[e]}var jj=B().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),qj=600;function Xj(){return B().global.screen==null?1024:B().global.screen.height*B().global.screen.width*window.devicePixelRatio*qj/1024/1024}var $p=class Zv extends Vl{nextDataId(){return Zv.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!B().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let a;if(t!=null){if(t instanceof Nl)a=t;else{let n=Bn(B().getNumber("WEBGL_VERSION"),t);a=new Nl(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Bn(B().getNumber("WEBGL_VERSION"));a=new Nl(n),this.binaryCache=Hj(B().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=a,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Tj(this.gpgpu),this.numMBBeforeWarning=Xj(),this.texData=new Vd(this,It())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,a,n,r,s,i){let o=this.makeTensorInfo(a,n),l=this.texData.get(o.dataId);l.isPacked=!1,l.texture={texture:t,texShape:[r,s]},l.texShape=[r,s];let u=cd(a),p=new i5(u,!1,i),c=this.runWebGLProgram(p,[o],n,[[r,s]]);return c.shape=a,l.texture=null,this.disposeIntermediateTensorInfo(o),c.dataId}write(t,a,n){if((B().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||B().getBool("DEBUG"))&&this.checkNumericalProblems(t),n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:a,dtype:n,values:t,usage:mn.UPLOAD,refCount:1}),r}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let a=this.texData.get(t);a.refCount++}decRef(t){if(this.texData.has(t)){let a=this.texData.get(t);a.refCount--}}move(t,a,n,r,s){if(B().getBool("DEBUG")&&this.checkNumericalProblems(a),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:r,values:a,usage:mn.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let a=this.texData.get(t),{values:n,dtype:r,complexTensorInfos:s,slice:i,shape:o,isPacked:l}=a;if(i!=null){let d;l?d=new qr(o,Vr):d=new Jn(o,Vr);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:r}],r),m=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),m}if(n!=null)return this.convertAndCacheOnCPU(t);if(r==="string")return n;let u=this.activeTimers!=null,p;u&&(p=v.now());let c;if(r==="complex64"){let d=this.readSync(s.real.dataId),h=this.readSync(s.imag.dataId);c=C.mergeRealAndImagArrays(d,h)}else c=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=v.now()-p),this.convertAndCacheOnCPU(t,c)}async read(t){if(this.pendingRead.has(t)){let m=this.pendingRead.get(t);return new Promise(f=>m.push(f))}let a=this.texData.get(t),{values:n,shape:r,slice:s,dtype:i,complexTensorInfos:o,isPacked:l}=a;if(s!=null){let m;l?m=new qr(r,Vr):m=new Jn(r,Vr);let f=this.runWebGLProgram(m,[{dataId:t,shape:r,dtype:i}],i),g=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(B().getBool("DEBUG")&&!B().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&B().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,p;if(i!=="complex64"&&B().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(t);let m=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(m.texture.texture,...Mc(r))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(i==="complex64"){let m=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=m[0],g=m[1];c=C.mergeRealAndImagArrays(f,g)}else if(u==null)c=this.getValuesFromTexture(t);else{let m=v.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,m)}if(p!=null&&this.disposeIntermediateTensorInfo(p),u!=null){let m=this.gpgpu.gl;ce(m,()=>m.deleteBuffer(u))}let d=this.convertAndCacheOnCPU(t,c),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(m=>m(d)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&It().removeDataId(t,this),this.pendingDeletes--),d}readToGPU(t,a={}){let n=this.texData.get(t),{values:r,shape:s,slice:i,dtype:o,isPacked:l,texture:u}=n;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let h;l?h=new qr(s,Vr):h=new Jn(s,Vr);let m=this.runWebGLProgram(h,[{dataId:t,shape:s,dtype:o}],o),f=this.readToGPU(m,a);return this.disposeIntermediateTensorInfo(m),f}if(u==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let p=this.decode(t,a.customTexShape),c=It().makeTensorFromTensorInfo(p),d=this.texData.get(p.dataId);return Object.assign({tensorRef:c},d.texture)}bufferSync(t){let a=this.readSync(t.dataId);if(t.dtype==="string")try{let n=a.map(r=>v.decodeString(r));return _e(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return _e(t.shape,t.dtype,a)}checkNumericalProblems(t){if(t!=null)for(let a=0;a0}time(t){let a=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),i=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=a,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);o.kernelMs=v.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(t){return B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=v.now(),t)}async getQueryTime(t){if(B().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let a=t;return a.endMs-a.startMs}disposeData(t,a=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(a?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!a&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,a),this.disposeData(n.imag.dataId,a)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:a,dtype:n,texShape:r,usage:s,isPacked:i,slice:o}=this.texData.get(t),l=o&&o.origDataId||t,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),a!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(a,r,s,i)));let p=this.texData.get(t);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,a=jj){return B().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)0&&v.isString(n[0])){let s=n.map(i=>v.encodeString(i));r=this.write(s,t,a)}else r=this.write(n,t,a);return this.texData.get(r).usage=null,{dataId:r,shape:t,dtype:a}}makeOutput(t,a,n){return It().makeTensorFromTensorInfo(this.makeTensorInfo(t,a,n),this)}unpackTensor(t){let a=new Bj(t.shape);return this.runWebGLProgram(a,[t],t.dtype)}packTensor(t){let a=new Sj(t.shape);return this.runWebGLProgram(a,[t],t.dtype,null,!0)}packedReshape(t,a){let n=[ti(t.shape),...ai(t.shape)],r={dtype:t.dtype,shape:n,dataId:t.dataId},s=[ti(a),...ai(a)],i=new Yv(s,n),o=!0,l=[n],u=this.runWebGLProgram(i,[r],t.dtype,l,o);return{dataId:u.dataId,shape:a,dtype:u.dtype}}decode(t,a){let n=this.texData.get(t),{isPacked:r,shape:s,dtype:i}=n;if(a!=null){let d=v.sizeFromShape(s),h=a[0]*a[1]*4;v.assert(d<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=cd(s),l;r?l=new TH(o):l=new CH(o);let u=!0,p=[a!=null?a:Mc(o)],c=this.runWebGLProgram(l,[{shape:o,dtype:i,dataId:t}],i,p,u,a);return{dtype:i,shape:s,dataId:c.dataId}}runWebGLProgram(t,a,n,r,s=!1,i){let o=this.makeTensorInfo(t.outputShape,n),l=this.texData.get(o.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===_d.DENSE){let y=i!=null?i:Mc(t.outputShape);l.texShape=y.map(x=>x*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),v.sizeFromShape(o.shape)===0)return l.values=v.getTypedArrayFromDType(o.dtype,0),o;let u=[],p=a.map(y=>{if(y.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let x=this.texData.get(y.dataId);if(x.texture==null){if(!t.packedInputs&&v.sizeFromShape(y.shape)<=B().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:y.shape,texData:null,isUniform:!0,uniformValues:x.values};t.packedInputs&&(x.isPacked=!0,x.shape=y.shape)}if(this.uploadToGPU(y.dataId),!!x.isPacked!=!!t.packedInputs)y=x.isPacked?this.unpackTensor(y):this.packTensor(y),u.push(y),x=this.texData.get(y.dataId);else if(x.isPacked&&!Fd(x.shape,y.shape)){let A=y,b=y.shape;y.shape=x.shape,y=this.packedReshape(y,b),u.push(y),x=this.texData.get(y.dataId),A.shape=b}return{shape:y.shape,texData:x,isUniform:!1}});this.uploadToGPU(o.dataId);let c={shape:o.shape,texData:l,isUniform:!1},d=SH(t,p,c),h=this.getAndSaveBinary(d,()=>kH(this.gpgpu,t,p,c)),m=this.activeTimers!=null,f;m&&(f=this.startTimer()),B().get("ENGINE_COMPILE_ONLY")||IH(this.gpgpu,h,p,c,r),u.forEach(y=>this.disposeIntermediateTensorInfo(y)),m&&(f=this.endTimer(f),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(f)}));let g=B().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let y=v.now();y-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=y)}if(!B().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let y=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),y}return o}compileAndRun(t,a,n,r,s=!1){return n=n||a[0].dtype,this.runWebGLProgram(t,a,n,r,s)}getAndSaveBinary(t,a){return t in this.binaryCache||(this.binaryCache[t]=a()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(B().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!B().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=B().getBool("DEBUG");B().set("DEBUG",!1);let a=this.abs(Ge(1e-8)).dataSync()[0];if(B().set("DEBUG",t),a>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Uj:Gj}uploadToGPU(t){let a=this.texData.get(t),{shape:n,dtype:r,values:s,texture:i,usage:o,isPacked:l}=a;if(i!=null)return;let u=this.activeTimers!=null,p;u&&(p=v.now());let c=a.texShape;if(c==null&&(c=yv(n,l),a.texShape=c),s!=null){let d=cd(n),h,m=c[1],f=c[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(l||!g)&&([m,f]=Tu(c[0],c[1])),l?h=new MH(d,g):h=new i5(d,g);let y=g?[f,m]:c,x=this.makeTensorInfo(y,r),A=this.texData.get(x.dataId);g?A.usage=mn.PIXELS:A.usage=mn.UPLOAD,A.texShape=y,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(x.dataId),m,f,s);let b=[[f,m]],w=this.runWebGLProgram(h,[x],r,b,!0),I=this.texData.get(w.dataId);a.texShape=I.texShape,a.isPacked=I.isPacked,a.usage=I.usage,B().get("ENGINE_COMPILE_ONLY")?this.disposeData(w.dataId):(a.texture=I.texture,a.values=null,this.texData.delete(w.dataId)),this.disposeIntermediateTensorInfo(x),u&&(this.uploadWaitMs+=v.now()-p)}else{let d=this.acquireTexture(c,o,r,l);a.texture=d}}convertAndCacheOnCPU(t,a){let n=this.texData.get(t),{dtype:r}=n;return a!=null&&(n.values=Kj(a,r)),n.values}acquireTexture(t,a,n,r){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,a,r)}computeBytes(t,a){return t[0]*t[1]*v.bytesPerElement(a)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,a]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(a));return Promise.all(t)}else{for(let[,a]of Object.entries(this.binaryCache)){let n=new Promise(r=>{try{this.checkCompletion_(a),r(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await I7(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(l3(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:a,customUniformLocations:n,infLoc:r,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:o,outTexShapeLocation:l}=Nv(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=a,t.customUniformLocations=n,t.infLoc=r,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=o,t.outTexShapeLocation=l}}createTensorFromGPUData(t,a,n){t.channels=t.channels||"RGBA";let{texture:r,height:s,width:i,channels:o}=t,l=It().backend;if(!l.gpgpu.gl.isTexture(r))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=l.writeTexture(r,a,n,s,i,o);return It().makeTensorFromDataId(u,a,n,l)}};$p.nextDataId=0;function Kj(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let a=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;nnew $p,2);var Zj={forceHalfFloat:Jv},x3=` if (isnan(a)) return a; if (isnan(b)) return b; -`,ri=class{constructor(e,t,a){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,a),this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` +`,ni=class{constructor(e,t,a){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,a),this.enableShapeUniforms=ya(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -1242,12 +1242,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}},il=` + `}},Uo=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; -`,Ju=class{constructor(e,t,a,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,a);let r=this.outputShape.length;this.enableShapeUniforms=ya(r);let s="";if(n)if(r===0||v.sizeFromShape(this.outputShape)===1)s=` +`,Pu=class{constructor(e,t,a,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,a);let r=this.outputShape.length;this.enableShapeUniforms=ya(r);let s="";if(n)if(r===0||v.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; @@ -1291,13 +1291,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function en(e){let{inputs:t,backend:a}=e,{x:n}=t;return a.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Jj={kernelName:qi,backendName:"webgl",kernelFunc:en};function fs(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.texData.get(s.dataId),o=en({inputs:{x:n},backend:a}),l=en({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var Qj={kernelName:cp,backendName:"webgl",kernelFunc:fs},A8="return (a < 0.) ? b * a : a;",b8=` + `}};function en(e){let{inputs:t,backend:a}=e,{x:n}=t;return a.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Jj={kernelName:Wi,backendName:"webgl",kernelFunc:en};function ms(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.texData.get(s.dataId),o=en({inputs:{x:n},backend:a}),l=en({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var Qj={kernelName:qd,backendName:"webgl",kernelFunc:ms},Qv="return (a < 0.) ? b * a : a;",e8=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function eq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=a.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ju(b8,r.shape,i.shape):new ri(A8,r.shape,i.shape),l=a.runWebGLProgram(o,[r,i],"float32");return a.disposeIntermediateTensorInfo(i),l}var tq={kernelName:Zi,backendName:"webgl",kernelFunc:eq},v8="return (a < 0.) ? b * a : a;",w8=` +`;function eq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=a.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Pu(e8,r.shape,i.shape):new ni(Qv,r.shape,i.shape),l=a.runWebGLProgram(o,[r,i],"float32");return a.disposeIntermediateTensorInfo(i),l}var tq={kernelName:Gi,backendName:"webgl",kernelFunc:eq},t8="return (a < 0.) ? b * a : a;",a8=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function aq(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ju(w8,n.shape,r.shape):new ri(v8,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],"float32")}var nq={kernelName:Io,backendName:"webgl",kernelFunc:aq},Qu="if (isnan(x)) return x;";function tt({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:a,dtype:n}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&a!=null){let c=o.texData.get(i.dataId),d=a(c.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=B().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new qr(i.shape,t):p=new Jn(i.shape,e),o.runWebGLProgram(p,[i],l)}}function ma({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:a=!1,supportsComplex:n=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(n&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(A=>{let[b,w]=A,I={dataId:b.dataId,dtype:b.dtype,shape:l.shape},T={dataId:w.dataId,dtype:w.dtype,shape:u.shape},N=new ri(e,l.shape,u.shape);return p.runWebGLProgram(N,[I,T],ca(b.dtype,w.dtype))}),x=fs({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),x}let c=s||ca(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?C.fromUint8ToStringArray(m):m,y=l.dtype==="string"?C.fromUint8ToStringArray(f):f,[x,A]=r(l.shape,u.shape,g,y,c),b=p.makeTensorInfo(A,c),w=p.texData.get(b.dataId);return w.values=x,b}let d=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Ju(t,l.shape,u.shape,a):h=new ri(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],c)}}function tp(e,t=!1){if(e==="linear")return t?Dj:Mj;if(e==="relu")return t?zj:Pj;if(e==="elu")return t?Oj:$j;if(e==="relu6")return t?Lj:_j;if(e==="prelu")return t?w8:v8;if(e==="leakyrelu")return t?b8:A8;if(e==="sigmoid")return t?Wj:Fj;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var k8=class{constructor(e,t,a,n=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a,this.enableShapeUniforms=ya(this.outputShape.length);let u=n?e[1]:e[2],p=Math.ceil(u/2),c=n?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) { +`;function aq(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Pu(a8,n.shape,r.shape):new ni(t8,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],"float32")}var nq={kernelName:po,backendName:"webgl",kernelFunc:aq},_u="if (isnan(x)) return x;";function tt({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:a,dtype:n}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&a!=null){let c=o.texData.get(i.dataId),d=a(c.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=B().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new qr(i.shape,t):p=new Jn(i.shape,e),o.runWebGLProgram(p,[i],l)}}function ma({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:a=!1,supportsComplex:n=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(n&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(A=>{let[b,w]=A,I={dataId:b.dataId,dtype:b.dtype,shape:l.shape},T={dataId:w.dataId,dtype:w.dtype,shape:u.shape},N=new ni(e,l.shape,u.shape);return p.runWebGLProgram(N,[I,T],ca(b.dtype,w.dtype))}),x=ms({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),x}let c=s||ca(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?C.fromUint8ToStringArray(m):m,y=l.dtype==="string"?C.fromUint8ToStringArray(f):f,[x,A]=r(l.shape,u.shape,g,y,c),b=p.makeTensorInfo(A,c),w=p.texData.get(b.dataId);return w.values=x,b}let d=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Pu(t,l.shape,u.shape,a):h=new ni(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],c)}}function Dd(e,t=!1){if(e==="linear")return t?Dj:Mj;if(e==="relu")return t?zj:Pj;if(e==="elu")return t?Oj:$j;if(e==="relu6")return t?Lj:_j;if(e==="prelu")return t?a8:t8;if(e==="leakyrelu")return t?e8:Qv;if(e==="sigmoid")return t?Wj:Fj;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var n8=class{constructor(e,t,a,n=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a,this.enableShapeUniforms=ya(this.outputShape.length);let u=n?e[1]:e[2],p=Math.ceil(u/2),c=n?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:l?f=`vec4 activation(vec4 a) { @@ -1336,7 +1336,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}},M5={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},$5=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,a),this.userCode=` + `}},p5={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},c5=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,a),this.userCode=` float binaryOpComplex( float areal, float aimag, float breal, float bimag) { ${e} @@ -1349,7 +1349,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float bimag = getBImagAtOutCoords(); setOutput(binaryOpComplex(areal, aimag, breal, bimag)); } - `}},P5="return a * b;";function L3(e){let{inputs:t,backend:a}=e,{a:n,b:r}=t,s=C.upcastType(n.dtype,r.dtype);if(n.dtype==="complex64"){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),u=new $5(M5.REAL,n.shape,r.shape),p=new $5(M5.IMAG,n.shape,r.shape),c=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),m=fs({inputs:{real:d,imag:h},backend:a});return a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),m}if(a.shouldExecuteOnCPU([n,r])){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),[u,p]=ej(n.shape,r.shape,o.values,l.values,s),c=a.makeTensorInfo(p,s),d=a.texData.get(c.dataId);return d.values=u,c}let i;return B().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Ju(P5,n.shape,r.shape):i=new ri(P5,n.shape,r.shape),a.runWebGLProgram(i,[n,r],s)}var rq={kernelName:yo,backendName:"webgl",kernelFunc:L3};function sq(e,t,a){let n=[ai(e.shape),...ni(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[ai(t),...ni(t)],i=new g8(s,n),o=!0,l=[n],u=a.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function pe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{shape:s}=n,i=a,o=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!ep(r.shape,l)&&!(p.texture!==null&&ep(p.shape,l))?sq(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var iq={kernelName:Eu,backendName:"webgl",kernelFunc:pe},_5=class{constructor(e,t){this.variableNames=["x"];let{windowSize:a,batchSize:n,inSize:r,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(a/4)*4,o=a%4,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%a>0&&(u=` + `}},h5="return a * b;";function A3(e){let{inputs:t,backend:a}=e,{a:n,b:r}=t,s=C.upcastType(n.dtype,r.dtype);if(n.dtype==="complex64"){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),u=new c5(p5.REAL,n.shape,r.shape),p=new c5(p5.IMAG,n.shape,r.shape),c=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),m=ms({inputs:{real:d,imag:h},backend:a});return a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),m}if(a.shouldExecuteOnCPU([n,r])){let o=a.texData.get(n.dataId),l=a.texData.get(r.dataId),[u,p]=ej(n.shape,r.shape,o.values,l.values,s),c=a.makeTensorInfo(p,s),d=a.texData.get(c.dataId);return d.values=u,c}let i;return B().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Pu(h5,n.shape,r.shape):i=new ni(h5,n.shape,r.shape),a.runWebGLProgram(i,[n,r],s)}var rq={kernelName:ro,backendName:"webgl",kernelFunc:A3};function sq(e,t,a){let n=[ti(e.shape),...ai(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[ti(t),...ai(t)],i=new Yv(s,n),o=!0,l=[n],u=a.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function pe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{shape:s}=n,i=a,o=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!Fd(r.shape,l)&&!(p.texture!==null&&Fd(p.shape,l))?sq(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var iq={kernelName:pu,backendName:"webgl",kernelFunc:pe},m5=class{constructor(e,t){this.variableNames=["x"];let{windowSize:a,batchSize:n,inSize:r,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(a/4)*4,o=a%4,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%a>0&&(u=` if (inIdx < 0 || inIdx >= ${r}) { return 0.0; } @@ -1494,12 +1494,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function lq(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let a=t.length?t[t.length-1].outSize:e[1],n=C.computeOptimalWindowSize(a);t.push({inSize:a,windowSize:n,outSize:Math.ceil(a/n)})}return t}function ol(e,t,a,n){let r=lq(e.shape),s=e;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let r=0;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=ft(this.rank),r=f8("rc",this.rank),s=new Array(this.rank);for(let u=0;u6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let r=0;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=ft(this.rank),r=Kv("rc",this.rank),s=new Array(this.rank);for(let u=0;u`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],w=n?[x,m,d]:[x,d,m],I=pe({inputs:{x:e},backend:r,attrs:{shape:b}}),T=pe({inputs:{x:t},backend:r,attrs:{shape:w}}),N=[I,T],M=Math.max(y,x),$=a?I.shape[1]:I.shape[2],E=s!=null,S=i!=null,_=l==="leakyrelu",O=l!=null?tp(l,!0):null,W=E||S||_||O!=null,P;if((h===1||m===1)&&$>I8&&W===!1){let G=I,q=T;a&&(G=Ca({inputs:{x:I},backend:r,attrs:{perm:[0,2,1]}}),N.push(G)),n&&(q=Ca({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),N.push(q));let H=m!==1,V=m===1,Z=G;H&&(Z=pe({inputs:{x:G},backend:r,attrs:{shape:[M,$,1]}}),N.push(Z));let X=m===1?2:1,re=q;V&&(re=pe({inputs:{x:q},backend:r,attrs:{shape:[M,1,$]}}),N.push(re));let ee=L3({inputs:{a:Z,b:re},backend:r});P=i0({inputs:{x:ee},backend:r,attrs:{axis:X,keepDims:!0}}),N.push(ee)}else{let G=ca(e.dtype,t.dtype),q=new k8(b,w,[M,h,m],a,n,E,O,S,_),H=[I,T];if(s!=null&&H.push(s),S&&H.push(i),_){let V=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));H.push(V),N.push(V)}P=r.runWebGLProgram(q,H,G)}let U=pe({inputs:{x:P},backend:r,attrs:{shape:A}});N.push(P);for(let G of N)r.disposeIntermediateTensorInfo(G);return U}function fq(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return kh({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var gq={kernelName:Zr,backendName:"webgl",kernelFunc:fq},F5="return abs(x);";function yq(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=a.texData.get(n.dataId),i=h8(s.values);return a.makeTensorInfo(n.shape,n.dtype,i)}let r;return B().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new qr(n.shape,F5):r=new Jn(n.shape,F5),a.runWebGLProgram(r,[n],n.dtype)}var xq={kernelName:ou,backendName:"webgl",kernelFunc:yq},Aq=Mn+` + `}};function Lh(e,t,a){let n=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new pq(e.shape,t):new uq(e.shape,t);return a.runWebGLProgram(n,[e],e.dtype)}function cq(e,t,a,n){let r=t,s=e.shape.length,i=v.parseAxisParam(r,e.shape),o=i,l=C.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=Lh(e,l,n),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[c,d]=C.computeOutAndReduceShapes(p.shape,o),h=c;a&&(h=C.expandShapeToKeepDim(c,i));let m=v.sizeFromShape(d),f=v.sizeFromShape(e.shape)/m,g=pe({inputs:{x:p},attrs:{shape:[f,m]},backend:n}),y=mp(e.dtype),x=Go(g,y,"sum",n),A=pe({inputs:{x},attrs:{shape:h},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(x),u&&n.disposeIntermediateTensorInfo(p),A}function Wh(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return cq(r,s,i,a)}var hq={kernelName:"Sum",backendName:"webgl",kernelFunc:Wh};function Ca(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{perm:s}=n,i=a,o=r.shape.length,l=new Array(o);for(let p=0;p`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],w=n?[x,m,d]:[x,d,m],I=pe({inputs:{x:e},backend:r,attrs:{shape:b}}),T=pe({inputs:{x:t},backend:r,attrs:{shape:w}}),N=[I,T],M=Math.max(y,x),$=a?I.shape[1]:I.shape[2],E=s!=null,S=i!=null,_=l==="leakyrelu",O=l!=null?Dd(l,!0):null,W=E||S||_||O!=null,P;if((h===1||m===1)&&$>r8&&W===!1){let G=I,q=T;a&&(G=Ca({inputs:{x:I},backend:r,attrs:{perm:[0,2,1]}}),N.push(G)),n&&(q=Ca({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),N.push(q));let H=m!==1,V=m===1,Z=G;H&&(Z=pe({inputs:{x:G},backend:r,attrs:{shape:[M,$,1]}}),N.push(Z));let X=m===1?2:1,re=q;V&&(re=pe({inputs:{x:q},backend:r,attrs:{shape:[M,1,$]}}),N.push(re));let ee=A3({inputs:{a:Z,b:re},backend:r});P=Wh({inputs:{x:ee},backend:r,attrs:{axis:X,keepDims:!0}}),N.push(ee)}else{let G=ca(e.dtype,t.dtype),q=new n8(b,w,[M,h,m],a,n,E,O,S,_),H=[I,T];if(s!=null&&H.push(s),S&&H.push(i),_){let V=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));H.push(V),N.push(V)}P=r.runWebGLProgram(q,H,G)}let U=pe({inputs:{x:P},backend:r,attrs:{shape:A}});N.push(P);for(let G of N)r.disposeIntermediateTensorInfo(G);return U}function fq(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return nh({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var gq={kernelName:Zr,backendName:"webgl",kernelFunc:fq},f5="return abs(x);";function yq(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=a.texData.get(n.dataId),i=qv(s.values);return a.makeTensorInfo(n.shape,n.dtype,i)}let r;return B().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new qr(n.shape,f5):r=new Jn(n.shape,f5),a.runWebGLProgram(r,[n],n.dtype)}var xq={kernelName:"Abs",backendName:"webgl",kernelFunc:yq},Aq=Mn+` if (abs(x) > 1.) { return NAN; } return acos(x); -`,bq=tt({opSnippet:Aq}),vq={kernelName:oi,backendName:"webgl",kernelFunc:bq},wq=Mn+` +`,bq=tt({opSnippet:Aq}),vq={kernelName:ii,backendName:"webgl",kernelFunc:bq},wq=Mn+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,kq=tt({opSnippet:wq}),Iq={kernelName:li,backendName:"webgl",kernelFunc:kq},D5="return a + b;",Sq=ma({opSnippet:D5,packedOpSnippet:D5,supportsComplex:!0,cpuKernelImpl:PH}),Cq={kernelName:ls,backendName:"webgl",kernelFunc:Sq},Tq=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let a=[];this.variableNames.forEach(r=>{a.push(`float v${r} = get${r}AtOutCoords();`)});let n=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` +return log(x + sqrt(x * x - 1.0));`,kq=tt({opSnippet:wq}),Iq={kernelName:oi,backendName:"webgl",kernelFunc:kq},g5="return a + b;",Sq=ma({opSnippet:g5,packedOpSnippet:g5,supportsComplex:!0,cpuKernelImpl:PH}),Cq={kernelName:"Add",backendName:"webgl",kernelFunc:Sq},Tq=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let a=[];this.variableNames.forEach(r=>{a.push(`float v${r} = get${r}AtOutCoords();`)});let n=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` void main() { ${a.join(` `)} @@ -1539,7 +1539,7 @@ return log(x + sqrt(x * x - 1.0));`,kq=tt({opSnippet:wq}),Iq={kernelName:li,back vec4 result = ${n}; setOutput(result); } - `}};function oh(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return en({inputs:{x:n[0]},backend:a});if(n.length>B().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=oh({inputs:n.slice(0,o),backend:a}),u=oh({inputs:n.slice(o),backend:a});return oh({inputs:[l,u],backend:a})}let r=n.map(o=>o.dtype).reduce((o,l)=>ca(o,l)),s=n.map(o=>o.shape),i=B().getBool("WEBGL_PACK")?new Nq(n[0].shape,s):new Tq(n[0].shape,s);return a.runWebGLProgram(i,n,r)}var Rq={kernelName:ui,backendName:"webgl",kernelFunc:oh};function Eq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=C.getAxesPermutation(u,o),c=r;p!=null&&(c=Ca({inputs:{x:r},backend:a,attrs:{perm:p}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("all",u,o);let[d,h]=C.computeOutAndReduceShapes(c.shape,u),m=v.sizeFromShape(h),f=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,m]}}),g=ol(f,f.dtype,"all",a),y;if(i){let x=C.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var Mq={kernelName:di,backendName:"webgl",kernelFunc:Eq};function $q(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=C.getAxesPermutation(u,o),c=r;p!=null&&(c=Ca({inputs:{x:r},backend:a,attrs:{perm:p}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("any",u,o);let[d,h]=C.computeOutAndReduceShapes(c.shape,u),m=v.sizeFromShape(h),f=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,m]}}),g=ol(f,f.dtype,"any",a),y;if(i){let x=C.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var Pq={kernelName:pi,backendName:"webgl",kernelFunc:$q},_q=class{constructor(e,t,a){this.variableNames=["A"];let{windowSize:n,batchSize:r,outSize:s}=e;a||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=a?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + `}};function Bc(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return en({inputs:{x:n[0]},backend:a});if(n.length>B().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=Bc({inputs:n.slice(0,o),backend:a}),u=Bc({inputs:n.slice(o),backend:a});return Bc({inputs:[l,u],backend:a})}let r=n.map(o=>o.dtype).reduce((o,l)=>ca(o,l)),s=n.map(o=>o.shape),i=B().getBool("WEBGL_PACK")?new Nq(n[0].shape,s):new Tq(n[0].shape,s);return a.runWebGLProgram(i,n,r)}var Rq={kernelName:li,backendName:"webgl",kernelFunc:Bc};function Eq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=C.getAxesPermutation(u,o),c=r;p!=null&&(c=Ca({inputs:{x:r},backend:a,attrs:{perm:p}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("all",u,o);let[d,h]=C.computeOutAndReduceShapes(c.shape,u),m=v.sizeFromShape(h),f=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,m]}}),g=Go(f,f.dtype,"all",a),y;if(i){let x=C.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var Mq={kernelName:"All",backendName:"webgl",kernelFunc:Eq};function $q(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=C.getAxesPermutation(u,o),c=r;p!=null&&(c=Ca({inputs:{x:r},backend:a,attrs:{perm:p}}),u=C.getInnerMostAxes(u.length,o)),C.assertAxesAreInnerMostDims("any",u,o);let[d,h]=C.computeOutAndReduceShapes(c.shape,u),m=v.sizeFromShape(h),f=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,m]}}),g=Go(f,f.dtype,"any",a),y;if(i){let x=C.expandShapeToKeepDim(d,l);y=pe({inputs:{x:g},backend:a,attrs:{shape:x}})}else y=pe({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(g),p!=null&&a.disposeIntermediateTensorInfo(c),y}var Pq={kernelName:"Any",backendName:"webgl",kernelFunc:$q},_q=class{constructor(e,t,a){this.variableNames=["A"];let{windowSize:n,batchSize:r,outSize:s}=e;a||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=a?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1621,25 +1621,25 @@ return log(x + sqrt(x * x - 1.0));`,kq=tt({opSnippet:wq}),Iq={kernelName:li,back } setOutput(bestIndex); } - `}};function S8(e,t,a,n=null){let r=t.shape[0],s=t.shape[1];n!=null&&(r=n.shape[0],s=n.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new _q(o,a,n==null),u=[t];n!=null&&u.push(n);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let c=S8(e,t,a,p);return e.disposeIntermediateTensorInfo(p),c}function C8(e,t,a,n=null){let r=n!=null?n.shape:t.shape,s=r[r.length-1],i=C.computeOptimalWindowSize(s),o=new Fq(r,i,a,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=C8(e,t,a,u);return e.disposeIntermediateTensorInfo(u),p}return u}function T8(e,t,a,n){let r=[a];if(C.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,t.shape.length),!B().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=C.computeOutAndReduceShapes(l.shape,r),c=v.sizeFromShape(p),d=pe({inputs:{x:l},backend:e,attrs:{shape:[-1,c]}});s.push(d);let h=S8(e,d,n);s.push(h);let m=pe({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return C8(e,t,n)}function Dq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ca({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=T8(a,l,i[0],"max");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var Oq={kernelName:lu,backendName:"webgl",kernelFunc:Dq};function zq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ca({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=T8(a,l,i[0],"min");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var Lq={kernelName:uu,backendName:"webgl",kernelFunc:zq},Wq=Mn+` + `}};function s8(e,t,a,n=null){let r=t.shape[0],s=t.shape[1];n!=null&&(r=n.shape[0],s=n.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new _q(o,a,n==null),u=[t];n!=null&&u.push(n);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let c=s8(e,t,a,p);return e.disposeIntermediateTensorInfo(p),c}function i8(e,t,a,n=null){let r=n!=null?n.shape:t.shape,s=r[r.length-1],i=C.computeOptimalWindowSize(s),o=new Fq(r,i,a,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=i8(e,t,a,u);return e.disposeIntermediateTensorInfo(u),p}return u}function o8(e,t,a,n){let r=[a];if(C.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,t.shape.length),!B().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=C.computeOutAndReduceShapes(l.shape,r),c=v.sizeFromShape(p),d=pe({inputs:{x:l},backend:e,attrs:{shape:[-1,c]}});s.push(d);let h=s8(e,d,n);s.push(h);let m=pe({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return i8(e,t,n)}function Dq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ca({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=o8(a,l,i[0],"max");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var Oq={kernelName:Gl,backendName:"webgl",kernelFunc:Dq};function zq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ca({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=o8(a,l,i[0],"min");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),p}var Lq={kernelName:Hl,backendName:"webgl",kernelFunc:zq},Wq=Mn+` if (abs(x) > 1.) { return NAN; } return asin(x); -`,Bq=tt({opSnippet:Wq}),Vq={kernelName:ci,backendName:"webgl",kernelFunc:Bq},Uq=Mn+"return log(x + sqrt(x * x + 1.0));",Gq=tt({opSnippet:Uq}),Hq={kernelName:hi,backendName:"webgl",kernelFunc:Gq},jq=Mn+` +`,Bq=tt({opSnippet:Wq}),Vq={kernelName:ui,backendName:"webgl",kernelFunc:Bq},Uq=Mn+"return log(x + sqrt(x * x + 1.0));",Gq=tt({opSnippet:Uq}),Hq={kernelName:di,backendName:"webgl",kernelFunc:Gq},jq=Mn+` return atan(x); -`,qq=tt({opSnippet:jq}),Xq={kernelName:mi,backendName:"webgl",kernelFunc:qq},Kq=z3+` +`,qq=tt({opSnippet:jq}),Xq={kernelName:pi,backendName:"webgl",kernelFunc:qq},Kq=x3+` return atan(a, b); `,Yq=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - `+il+` + `+Uo+` return result; -`,Zq=ma({opSnippet:Kq,packedOpSnippet:Yq}),Jq={kernelName:gi,backendName:"webgl",kernelFunc:Zq},Qq=Mn+` +`,Zq=ma({opSnippet:Kq,packedOpSnippet:Yq}),Jq={kernelName:hi,backendName:"webgl",kernelFunc:Zq},Qq=Mn+` if ((x < -1.0) || (x > 1.0)) return NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelName:fi,backendName:"webgl",kernelFunc:eX},ap=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),a){let T=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelName:ci,backendName:"webgl",kernelFunc:eX},Od=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),a){this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${d}, ${h}); @@ -1681,7 +1681,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); - if (value ${T} currMinMaxValue) { + if (value >= currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n?r?f:g:`wR * ${c} + wC`}; @@ -1780,7 +1780,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam } setOutput(${A}); } - `}},W3=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,c=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",A="0.0";if(x||(A="-1.0 / 1e-20"),a){let M=">=";this.userCode=` + `}},b3=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,c=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",A="0.0";if(x||(A="-1.0 / 1e-20"),a){this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${g}, ${y}); @@ -1831,7 +1831,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); - if (value ${M} currMinMaxValue) { + if (value >= currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} + @@ -1943,7 +1943,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam } setOutput(${w}); } - `}};function aX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;qu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return en({inputs:{x:r},backend:a});let c=new ap(p,"avg",!1);return a.runWebGLProgram(c,[r],"float32")}var nX={kernelName:yi,backendName:"webgl",kernelFunc:aX};function rX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=[1,1,1],c=C.computePool3DInfo(r.shape,s,i,p,o,l,u),d=new W3(c,"avg",!1);return a.runWebGLProgram(d,[r],"float32")}var sX={kernelName:du,backendName:"webgl",kernelFunc:rX},iX=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,c=1/(t*a);this.userCode=` + `}};function aX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;Nu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return en({inputs:{x:r},backend:a});let c=new Od(p,"avg",!1);return a.runWebGLProgram(c,[r],"float32")}var nX={kernelName:mi,backendName:"webgl",kernelFunc:aX};function rX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=[1,1,1],c=C.computePool3DInfo(r.shape,s,i,p,o,l,u),d=new b3(c,"avg",!1);return a.runWebGLProgram(d,[r],"float32")}var sX={kernelName:jl,backendName:"webgl",kernelFunc:rX},iX=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,c=1/(t*a);this.userCode=` const ivec2 pads = ivec2(${u}, ${p}); const float avgMultiplier = float(${c}); @@ -2041,7 +2041,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam } setOutput(dotProd); } - `}};function lX(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,c,u,p),h=new oX(d);return a.runWebGLProgram(h,[r],i.dtype)}var uX={kernelName:pp,backendName:"webgl",kernelFunc:lX};function dX(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;qu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=C.computePool2DInfo(i.shape,o,l,1,u),c=new iX(p);return a.runWebGLProgram(c,[r],i.dtype)}var pX={kernelName:dp,backendName:"webgl",kernelFunc:dX};function cX(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return kh({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var hX={kernelName:xi,backendName:"webgl",kernelFunc:cX},mX=class{constructor(e,t,a,n,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,a);let i="0.0";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function lX(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,c,u,p),h=new oX(d);return a.runWebGLProgram(h,[r],i.dtype)}var uX={kernelName:jd,backendName:"webgl",kernelFunc:lX};function dX(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Nu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=C.computePool2DInfo(i.shape,o,l,1,u),c=new iX(p);return a.runWebGLProgram(c,[r],i.dtype)}var pX={kernelName:Hd,backendName:"webgl",kernelFunc:dX};function cX(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return nh({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var hX={kernelName:fi,backendName:"webgl",kernelFunc:cX},mX=class{constructor(e,t,a,n,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,a);let i="0.0";n!=null&&(C.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2064,7 +2064,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam setOutput((x - mean) * inv + offset); } - `}},gX=({inputs:e,backend:t,attrs:a})=>{let{x:n,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=a;l==null&&(l=.001);let u=[n,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let c=null;o!=null&&(c=o.shape,u.push(o));let d=B().getBool("WEBGL_PACK_NORMALIZATION")?new fX(n.shape,r.shape,s.shape,p,c,l):new mX(n.shape,r.shape,s.shape,p,c,l);return t.runWebGLProgram(d,u,u[0].dtype)},yX={kernelName:Ui,backendName:"webgl",kernelFunc:gX},xX=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ft(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let a=AX(this.rank),n,r=e.map((s,i)=>`sourceLoc.${L1[i]} = start[${i}] + coords.${L1[i]};`);n=` + `}},gX=({inputs:e,backend:t,attrs:a})=>{let{x:n,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=a;l==null&&(l=.001);let u=[n,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let c=null;o!=null&&(c=o.shape,u.push(o));let d=B().getBool("WEBGL_PACK_NORMALIZATION")?new fX(n.shape,r.shape,s.shape,p,c,l):new mX(n.shape,r.shape,s.shape,p,c,l);return t.runWebGLProgram(d,u,u[0].dtype)},yX={kernelName:Di,backendName:"webgl",kernelFunc:gX},xX=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ft(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let a=AX(this.rank),n,r=e.map((s,i)=>`sourceLoc.${A1[i]} = start[${i}] + coords.${A1[i]};`);n=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${r.join(` @@ -2074,7 +2074,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam ${n} setOutput(getSource(${a})); } - `}},L1=["x","y","z","w","u","v"];function AX(e){if(e===1)return"sourceLoc";if(e<=6)return L1.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var bX=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ft(this.rank),a=ka("coords",this.rank),n=ka("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${r})`,i=` + `}},A1=["x","y","z","w","u","v"];function AX(e){if(e===1)return"sourceLoc";if(e<=6)return A1.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var bX=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ft(this.rank),a=ka("coords",this.rank),n=ka("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${r})`,i=` result.x = ${s}; if (++${a[this.rank-1]} < ${e[this.rank-1]}) { ++${n[this.rank-1]}; @@ -2103,7 +2103,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam ${o} setOutput(result); } - `}};function vX(e,t,a,n){let r=n.texData.get(e.dataId),s=n.makeTensorInfo(a,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=a,i.dtype=e.dtype;let o=Nt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function ed(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=Nt.parseSliceParams(r,s,i);if(Nt.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);if(a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.texData.get(r.dataId),d=pj(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}let{isPacked:u}=a.texData.get(r.dataId),p=Nt.isSliceContinous(r.shape,o,l);if(u||!p){let c=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new bX(l):new xX(l),d=[o];return a.runWebGLProgram(c,[r],r.dtype,d)}return a.uploadToGPU(r.dataId),vX(r,o,l,a)}var wX={kernelName:_u,backendName:"webgl",kernelFunc:ed},kX=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=C.getReshaped(r.shape,s,o),u=C.getPermuted(l.length,s.length),p=C.getReshapedPermuted(r.shape,s,o),c=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(p,i,s.length),h=[],m=pe({inputs:{x:r},backend:a,attrs:{shape:l}}),f=Ca({inputs:{x:m},backend:a,attrs:{perm:u}}),g=pe({inputs:{x:f},backend:a,attrs:{shape:p}}),y=ed({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(x=>a.disposeIntermediateTensorInfo(x)),y},IX={kernelName:pu,backendName:"webgl",kernelFunc:kX};function SX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.readSync(r.dataId),l=a.readSync(s.dataId),u=c8(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var CX={kernelName:Ai,backendName:"webgl",kernelFunc:SX},TX=` + `}};function vX(e,t,a,n){let r=n.texData.get(e.dataId),s=n.makeTensorInfo(a,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=a,i.dtype=e.dtype;let o=Nt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function Fu(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=Nt.parseSliceParams(r,s,i);if(Nt.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);if(a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.texData.get(r.dataId),d=pj(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}let{isPacked:u}=a.texData.get(r.dataId),p=Nt.isSliceContinous(r.shape,o,l);if(u||!p){let c=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new bX(l):new xX(l),d=[o];return a.runWebGLProgram(c,[r],r.dtype,d)}return a.uploadToGPU(r.dataId),vX(r,o,l,a)}var wX={kernelName:fu,backendName:"webgl",kernelFunc:Fu},kX=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=C.getReshaped(r.shape,s,o),u=C.getPermuted(l.length,s.length),p=C.getReshapedPermuted(r.shape,s,o),c=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(p,i,s.length),h=[],m=pe({inputs:{x:r},backend:a,attrs:{shape:l}}),f=Ca({inputs:{x:m},backend:a,attrs:{perm:u}}),g=pe({inputs:{x:f},backend:a,attrs:{shape:p}}),y=Fu({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(x=>a.disposeIntermediateTensorInfo(x)),y},IX={kernelName:ql,backendName:"webgl",kernelFunc:kX};function SX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.readSync(r.dataId),l=a.readSync(s.dataId),u=jv(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var CX={kernelName:gi,backendName:"webgl",kernelFunc:SX},TX=` int r = int(a.r) & int(b.r); int g = int(a.g) & int(b.g); int rb = int(a.b) & int(b.b); @@ -2111,7 +2111,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam return vec4(r, g, rb, ra); `,NX=` return float(int(a.r) & int(b.r)); -`;function RX(e){let{inputs:t,backend:a}=e,{a:n,b:r}=t,s=B().getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=B().getNumber("WEBGL_VERSION");if(a.shouldExecuteOnCPU([n,r])||i===1){let l=a.texData.get(n.dataId).values,u=a.texData.get(r.dataId).values,[p,c]=FH(n.shape,r.shape,l,u,n.dtype),d=a.makeTensorInfo(c,n.dtype),h=a.texData.get(d.dataId);return h.values=p,d}let o;return s?o=new Ju(TX,n.shape,r.shape,!1):o=new ri(NX,n.shape,r.shape),a.runWebGLProgram(o,[n,r],n.dtype)}var EX={kernelName:cu,backendName:"webgl",kernelFunc:RX};function MX(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.readSync(n.dataId),i=a.readSync(r.dataId),o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var $X={kernelName:hu,backendName:"webgl",kernelFunc:MX},PX="return float(a != b);",N8=ma({opSnippet:PX,cpuKernelImpl:aj,dtype:"bool"}),_X={kernelName:xo,backendName:"webgl",kernelFunc:N8};function Qp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return en({inputs:{x:r.complexTensorInfos.real},backend:a})}var FX={kernelName:Ip,backendName:"webgl",kernelFunc:Qp},DX="return float(int(x));";function OX(e,t){let a=new Jn(e.shape,DX),n=t.runWebGLProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function W1(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return en({inputs:{x:r},backend:a});let i=yn(r.shape),o=W1({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=fs({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Qp({inputs:{input:r},backend:a}),o=W1({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=en({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.texData.get(r.dataId).values,[o,l,u]=DH(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return OX(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=N8({inputs:{a:r,b:i},backend:a});return a.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var zX={kernelName:bi,backendName:"webgl",kernelFunc:W1},O5="return ceil(x);",LX=tt({opSnippet:O5,packedOpSnippet:O5,cpuKernelImpl:OH}),WX={kernelName:vi,backendName:"webgl",kernelFunc:LX},BX=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` +`;function RX(e){let{inputs:t,backend:a}=e,{a:n,b:r}=t,s=B().getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=B().getNumber("WEBGL_VERSION");if(a.shouldExecuteOnCPU([n,r])||i===1){let l=a.texData.get(n.dataId).values,u=a.texData.get(r.dataId).values,[p,c]=FH(n.shape,r.shape,l,u,n.dtype),d=a.makeTensorInfo(c,n.dtype),h=a.texData.get(d.dataId);return h.values=p,d}let o;return s?o=new Pu(TX,n.shape,r.shape,!1):o=new ni(NX,n.shape,r.shape),a.runWebGLProgram(o,[n,r],n.dtype)}var EX={kernelName:Xl,backendName:"webgl",kernelFunc:RX};function MX(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.readSync(n.dataId),i=a.readSync(r.dataId),o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var $X={kernelName:Kl,backendName:"webgl",kernelFunc:MX},PX="return float(a != b);",l8=ma({opSnippet:PX,cpuKernelImpl:aj,dtype:"bool"}),_X={kernelName:so,backendName:"webgl",kernelFunc:l8};function Pp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return en({inputs:{x:r.complexTensorInfos.real},backend:a})}var FX={kernelName:rp,backendName:"webgl",kernelFunc:Pp},DX="return float(int(x));";function OX(e,t){let a=new Jn(e.shape,DX),n=t.runWebGLProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function b1(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return en({inputs:{x:r},backend:a});let i=yn(r.shape),o=b1({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=ms({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Pp({inputs:{input:r},backend:a}),o=b1({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=en({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.texData.get(r.dataId).values,[o,l,u]=DH(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return OX(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=l8({inputs:{a:r,b:i},backend:a});return a.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var zX={kernelName:yi,backendName:"webgl",kernelFunc:b1},y5="return ceil(x);",LX=tt({opSnippet:y5,packedOpSnippet:y5,cpuKernelImpl:OH}),WX={kernelName:xi,backendName:"webgl",kernelFunc:LX},BX=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { float value = getAAtOutCoords(); @@ -2133,7 +2133,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function UX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o;B().getBool("WEBGL_PACK_CLIP")?o=new VX(r.shape):o=new BX(r.shape);let l=[[s],[i]];return a.runWebGLProgram(o,[r],r.dtype,l)}var GX={kernelName:us,backendName:"webgl",kernelFunc:UX},HX=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` + `}};function UX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o;B().getBool("WEBGL_PACK_CLIP")?o=new VX(r.shape):o=new BX(r.shape);let l=[[s],[i]];return a.runWebGLProgram(o,[r],r.dtype,l)}var GX={kernelName:ls,backendName:"webgl",kernelFunc:UX},HX=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2146,7 +2146,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function z5(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function jX(e){let{inputs:t,backend:a}=e,{x:n}=t,r=a.texData.get(n.dataId),s=new HX(n.shape),i=[z5(n,r.complexTensorInfos.real),z5(n,r.complexTensorInfos.imag)];return a.runWebGLProgram(s,i,i[0].dtype)}var qX={kernelName:hp,backendName:"webgl",kernelFunc:jX},XX=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s= ${o[m-1]}) { return getChannel( - getT${m}(${Qc(i,l,f)}), - vec2(${Qc(u,l,f)})); + getT${m}(${Pc(i,l,f)}), + vec2(${Pc(u,l,f)})); }`}let d=o.length,h=o[o.length-1];c+=` return getChannel( - getT${d}(${Qc(i,l,h)}), - vec2(${Qc(u,l,h)}));`,this.userCode=` + getT${d}(${Pc(i,l,h)}), + vec2(${Pc(u,l,h)}));`,this.userCode=` float getValue(${i.map(m=>"int "+m)}) { ${c} } @@ -2192,7 +2192,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam } setOutput(result); } - `}};function Qc(e,t,a){let n=e.indexOf(t);return e.map((r,s)=>s===n?`${r} - ${a}`:r).join()}function o0(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return en({inputs:{x:r.complexTensorInfos.imag},backend:a})}var YX={kernelName:vp,backendName:"webgl",kernelFunc:o0};function $d(e,t,a){let n=e[0].dtype;if(n==="complex64"){let h=e.map(x=>Qp({inputs:{input:x},backend:a})),m=e.map(x=>o0({inputs:{input:x},backend:a})),f=$d(h,t,a),g=$d(m,t,a),y=fs({inputs:{real:f,imag:g},backend:a});return h.forEach(x=>a.disposeIntermediateTensorInfo(x)),m.forEach(x=>a.disposeIntermediateTensorInfo(x)),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(g),y}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let h=e.map(b=>{let w=[-1,v.sizeFromShape(b.shape.slice(t))];return pe({inputs:{x:b},backend:a,attrs:{shape:w}})}),m=h.map(b=>({vals:a.readSync(b.dataId),shape:b.shape})),f=C.computeOutShape(h.map(b=>b.shape),1),g=h[0].shape[0]===1,y=zH(m,f,n,g),x=C.computeOutShape(e.map(b=>b.shape),t),A=a.makeTensorInfo(x,n,y);return h.forEach(b=>a.disposeIntermediateTensorInfo(b)),A}let s=e.filter(h=>v.sizeFromShape(h.shape)>0),i=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new Jn(e[0].shape,Vr):new qr(e[0].shape,Vr);return a.runWebGLProgram(h,e,n)}let o=B().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let f=0;fm.shape),t);return a.runWebGLProgram(h,s,n)}let{tensors2D:l,outShape:u}=ZX(s,t,a),p=new XX(l.map(h=>h.shape)),c=a.runWebGLProgram(p,l,n);l.forEach(h=>a.disposeIntermediateTensorInfo(h));let d=pe({inputs:{x:c},attrs:{shape:u},backend:a});return a.disposeIntermediateTensorInfo(c),d}function ZX(e,t,a){let n=C.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>pe({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:a})),outShape:n}}function R8(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);C.assertParamsConsistent(i,s);let o=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?en({inputs:{x:l[0]},backend:a}):$d(l,s,a)}var JX={kernelName:mu,backendName:"webgl",kernelFunc:R8},E8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,x=f?3:1,A="",b="";a&&(n?A=`float activation(float a) { + `}};function Pc(e,t,a){let n=e.indexOf(t);return e.map((r,s)=>s===n?`${r} - ${a}`:r).join()}function Bh(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.texData.get(n.dataId);return en({inputs:{x:r.complexTensorInfos.imag},backend:a})}var YX={kernelName:tp,backendName:"webgl",kernelFunc:Bh};function hd(e,t,a){let n=e[0].dtype;if(n==="complex64"){let h=e.map(x=>Pp({inputs:{input:x},backend:a})),m=e.map(x=>Bh({inputs:{input:x},backend:a})),f=hd(h,t,a),g=hd(m,t,a),y=ms({inputs:{real:f,imag:g},backend:a});return h.forEach(x=>a.disposeIntermediateTensorInfo(x)),m.forEach(x=>a.disposeIntermediateTensorInfo(x)),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(g),y}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let h=e.map(b=>{let w=[-1,v.sizeFromShape(b.shape.slice(t))];return pe({inputs:{x:b},backend:a,attrs:{shape:w}})}),m=h.map(b=>({vals:a.readSync(b.dataId),shape:b.shape})),f=C.computeOutShape(h.map(b=>b.shape),1),g=h[0].shape[0]===1,y=zH(m,f,n,g),x=C.computeOutShape(e.map(b=>b.shape),t),A=a.makeTensorInfo(x,n,y);return h.forEach(b=>a.disposeIntermediateTensorInfo(b)),A}let s=e.filter(h=>v.sizeFromShape(h.shape)>0),i=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new Jn(e[0].shape,Vr):new qr(e[0].shape,Vr);return a.runWebGLProgram(h,e,n)}let o=B().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let f=0;fm.shape),t);return a.runWebGLProgram(h,s,n)}let{tensors2D:l,outShape:u}=ZX(s,t,a),p=new XX(l.map(h=>h.shape)),c=a.runWebGLProgram(p,l,n);l.forEach(h=>a.disposeIntermediateTensorInfo(h));let d=pe({inputs:{x:c},attrs:{shape:u},backend:a});return a.disposeIntermediateTensorInfo(c),d}function ZX(e,t,a){let n=C.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>pe({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:a})),outShape:n}}function u8(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);C.assertParamsConsistent(i,s);let o=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?en({inputs:{x:l[0]},backend:a}):hd(l,s,a)}var JX={kernelName:Yl,backendName:"webgl",kernelFunc:u8},d8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,p=e.dilationWidth,c=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,x=f?3:1,A="",b="";a&&(n?A=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${a} }`:r?A=`float activation(float a) { @@ -2414,7 +2414,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam } setOutput(dotProd); } - `}},M8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,c=` + `}},p8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,p=u,c=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let f=0;f=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function $8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),p=a.inChannels,c=l[0]*l[1]*l[2],d=a.outChannels,h=a.dataFormat==="channelsLast",m=!1,f=!1,g,y=[];if(s!=null){let x=Ih(s.shape,h);x!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:x}}),y.push(s))}if(r!=null){let x=Ih(r.shape,h);x!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:x}}),y.push(r))}if(!((c===1||d===1)&&p>I8)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let x=l[0]*l[1]*(l[2]+1),A={dataId:e.dataId,shape:[1,x,a.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(ep(u.shape,A.shape),()=>`packed reshape ${u.shape} to ${A.shape} isn't free`);let w=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});y.push(w);let I=kh({a:A,b:w,backend:n,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),T=n.texData.get(I.dataId);v.assert(T.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,T.shape=a.outShape,g=en({inputs:{x:I},backend:n}),g.shape=a.outShape,y.push(I)}else{let x=a.outHeight*a.outWidth,A=pe({inputs:{x:e},backend:n,attrs:{shape:h?[a.batchSize,x,a.inChannels]:[a.batchSize,a.inChannels,x]}}),b=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}}),w=kh({a:h?A:b,b:h?b:A,transposeA:!h,transposeB:f,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=pe({inputs:{x:w},backend:n,attrs:{shape:a.outShape}}),y.push(A),y.push(b),y.push(w)}for(let x of y)n.disposeIntermediateTensorInfo(x);return g}function P8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:c,outHeight:d,dataFormat:h}=a,m=h==="channelsLast",f=l*u*p,g=d*c,y=[a.batchSize,f,g],x=!0,A=!1,b=[];if(s!=null){let G=Ih(s.shape,m);G!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:G}}),b.push(s))}if(r!=null){let G=Ih(r.shape,m);G!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:G}}),b.push(r))}let w=pe({inputs:{x:t},backend:n,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});b.push(w);let I=new eK(y,a),T=[e.shape,[a.padInfo.top,a.padInfo.left],[a.strideHeight,a.strideWidth],[a.dilationHeight,a.dilationWidth],[a.inChannels],[a.filterWidth*a.inChannels],[a.outWidth]],N=n.runWebGLProgram(I,[e],"float32",T),M=pe({inputs:{x:N},backend:n,attrs:{shape:y}});b.push(N),b.push(M);let $=r!=null,E=s!=null,S=o==="leakyrelu",_=o?tp(o,!0):null,O=new k8(m?M.shape:w.shape,m?w.shape:M.shape,m?[a.batchSize,g,a.outChannels]:[a.batchSize,a.outChannels,g],x,A,$,_,E,S),W=m?[M,w]:[w,M];if(r&&W.push(r),E&&W.push(s),S){let G=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));W.push(G),b.push(G)}let P=n.runWebGLProgram(O,W,"float32"),U=pe({inputs:{x:P},backend:n,attrs:{shape:a.outShape}});b.push(P);for(let G of b)n.disposeIntermediateTensorInfo(G);return U}function tK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=$8({x:r,filter:s,convInfo:d,backend:a});else if(d.strideWidth<=2&&c==="channelsLast"&&B().getBool("WEBGL_EXP_CONV")){let f=new M8(d),g=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=a.runWebGLProgram(f,[r,s],"float32",g)}else if(B().getBool("WEBGL_CONV_IM2COL"))h=P8({x:r,filter:s,convInfo:d,backend:a});else{let f=new E8(d);h=a.runWebGLProgram(f,[r,s],"float32")}let m=pe({inputs:{x:h},backend:a,attrs:{shape:d.outShape}});return a.disposeIntermediateTensorInfo(h),m}var aK={kernelName:wi,backendName:"webgl",kernelFunc:tK},nK=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` + `}};function rh(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function c8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),p=a.inChannels,c=l[0]*l[1]*l[2],d=a.outChannels,h=a.dataFormat==="channelsLast",m=!1,f=!1,g,y=[];if(s!=null){let x=rh(s.shape,h);x!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:x}}),y.push(s))}if(r!=null){let x=rh(r.shape,h);x!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:x}}),y.push(r))}if(!((c===1||d===1)&&p>r8)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let x=l[0]*l[1]*(l[2]+1),A={dataId:e.dataId,shape:[1,x,a.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Fd(u.shape,A.shape),()=>`packed reshape ${u.shape} to ${A.shape} isn't free`);let w=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});y.push(w);let I=nh({a:A,b:w,backend:n,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),T=n.texData.get(I.dataId);v.assert(T.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,T.shape=a.outShape,g=en({inputs:{x:I},backend:n}),g.shape=a.outShape,y.push(I)}else{let x=a.outHeight*a.outWidth,A=pe({inputs:{x:e},backend:n,attrs:{shape:h?[a.batchSize,x,a.inChannels]:[a.batchSize,a.inChannels,x]}}),b=pe({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}}),w=nh({a:h?A:b,b:h?b:A,transposeA:!h,transposeB:f,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=pe({inputs:{x:w},backend:n,attrs:{shape:a.outShape}}),y.push(A),y.push(b),y.push(w)}for(let x of y)n.disposeIntermediateTensorInfo(x);return g}function h8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,outWidth:c,outHeight:d,dataFormat:h}=a,m=h==="channelsLast",f=l*u*p,g=d*c,y=[a.batchSize,f,g],x=!0,A=!1,b=[];if(s!=null){let G=rh(s.shape,m);G!=null&&(s=pe({inputs:{x:s},backend:n,attrs:{shape:G}}),b.push(s))}if(r!=null){let G=rh(r.shape,m);G!=null&&(r=pe({inputs:{x:r},backend:n,attrs:{shape:G}}),b.push(r))}let w=pe({inputs:{x:t},backend:n,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});b.push(w);let I=new eK(y,a),T=[e.shape,[a.padInfo.top,a.padInfo.left],[a.strideHeight,a.strideWidth],[a.dilationHeight,a.dilationWidth],[a.inChannels],[a.filterWidth*a.inChannels],[a.outWidth]],N=n.runWebGLProgram(I,[e],"float32",T),M=pe({inputs:{x:N},backend:n,attrs:{shape:y}});b.push(N),b.push(M);let $=r!=null,E=s!=null,S=o==="leakyrelu",_=o?Dd(o,!0):null,O=new n8(m?M.shape:w.shape,m?w.shape:M.shape,m?[a.batchSize,g,a.outChannels]:[a.batchSize,a.outChannels,g],x,A,$,_,E,S),W=m?[M,w]:[w,M];if(r&&W.push(r),E&&W.push(s),S){let G=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));W.push(G),b.push(G)}let P=n.runWebGLProgram(O,W,"float32"),U=pe({inputs:{x:P},backend:n,attrs:{shape:a.outShape}});b.push(P);for(let G of b)n.disposeIntermediateTensorInfo(G);return U}function tK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=c8({x:r,filter:s,convInfo:d,backend:a});else if(d.strideWidth<=2&&c==="channelsLast"&&B().getBool("WEBGL_EXP_CONV")){let f=new p8(d),g=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=a.runWebGLProgram(f,[r,s],"float32",g)}else if(B().getBool("WEBGL_CONV_IM2COL"))h=h8({x:r,filter:s,convInfo:d,backend:a});else{let f=new d8(d);h=a.runWebGLProgram(f,[r,s],"float32")}let m=pe({inputs:{x:h},backend:a,attrs:{shape:d.outShape}});return a.disposeIntermediateTensorInfo(h),m}var aK={kernelName:Ai,backendName:"webgl",kernelFunc:tK},nK=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2846,7 +2846,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam } setOutput(dotProd); } - `}};function oK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new nK(d);return a.runWebGLProgram(h,[r,s],"float32")}var lK={kernelName:mp,backendName:"webgl",kernelFunc:oK},uK=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=ya(this.outputShape.length);let t=e.filterHeight,a=e.filterWidth,n=t-1-e.padInfo.top,r=a-1-e.padInfo.left;this.userCode=` + `}};function oK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new nK(d);return a.runWebGLProgram(h,[r,s],"float32")}var lK={kernelName:Kd,backendName:"webgl",kernelFunc:oK},uK=class{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=ya(this.outputShape.length);let t=e.filterHeight,a=e.filterWidth,n=t-1-e.padInfo.top,r=a-1-e.padInfo.left;this.userCode=` const ivec2 pads = ivec2(${n}, ${r}); void main() { @@ -2920,17 +2920,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam } setOutput(result); } - `}};function dK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c);if(B().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&c==="channelsLast"){let h=[[d.strideHeight,d.strideWidth]],m=new uK(d);return a.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new rK(d);return a.runWebGLProgram(h,[r,s],"float32")}}var pK={kernelName:ki,backendName:"webgl",kernelFunc:dK};function cK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new QX(u);return a.runWebGLProgram(p,[r,s],"float32")}var hK={kernelName:Ii,backendName:"webgl",kernelFunc:cK};function mK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=C.computeConv3DInfo(r.shape,l,i,1,o),p=new sK(u);return a.runWebGLProgram(p,[r,s],"float32")}var fK={kernelName:fu,backendName:"webgl",kernelFunc:mK};function gK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=C.computeConv3DInfo(l,s.shape,o,1,i),p=new iK(u);return a.runWebGLProgram(p,[r,s],"float32")}var yK={kernelName:Si,backendName:"webgl",kernelFunc:gK},xK=Qu+` + `}};function dK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c);if(B().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&c==="channelsLast"){let h=[[d.strideHeight,d.strideWidth]],m=new uK(d);return a.runWebGLProgram(m,[r,s],"float32",h)}else{let h=new rK(d);return a.runWebGLProgram(h,[r,s],"float32")}}var pK={kernelName:bi,backendName:"webgl",kernelFunc:dK};function cK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new QX(u);return a.runWebGLProgram(p,[r,s],"float32")}var hK={kernelName:vi,backendName:"webgl",kernelFunc:cK};function mK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=C.computeConv3DInfo(r.shape,l,i,1,o),p=new sK(u);return a.runWebGLProgram(p,[r,s],"float32")}var fK={kernelName:Zl,backendName:"webgl",kernelFunc:mK};function gK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=C.computeConv3DInfo(l,s.shape,o,1,i),p=new iK(u);return a.runWebGLProgram(p,[r,s],"float32")}var yK={kernelName:wi,backendName:"webgl",kernelFunc:gK},xK=_u+` return cos(x); `,AK=` vec4 result = cos(x); bvec4 isNaN = isnan(x); - ${il} + ${Uo} return result; -`,bK=tt({opSnippet:xK,packedOpSnippet:AK}),vK={kernelName:Ci,backendName:"webgl",kernelFunc:bK},wK=` +`,bK=tt({opSnippet:xK,packedOpSnippet:AK}),vK={kernelName:"Cos",backendName:"webgl",kernelFunc:bK},wK=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,kK=tt({opSnippet:wK}),IK={kernelName:Ti,backendName:"webgl",kernelFunc:kK},SK=class{constructor(e,t,a,n,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,c]=a;this.outputShape=[u,p,c,l];let d=n==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,A,b]=c>1?[`${(o-1)/(c-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` +`,kK=tt({opSnippet:wK}),IK={kernelName:ki,backendName:"webgl",kernelFunc:kK},SK=class{constructor(e,t,a,n,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,c]=a;this.outputShape=[u,p,c,l];let d=n==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,A,b]=c>1?[`${(o-1)/(c-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` const float height_ratio = float(${f}); const float width_ratio = float(${x}); void main() { @@ -2991,20 +2991,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam setOutput(newValue); } } - `}},CK=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new SK(r.shape,s.shape,o,l,u);return a.runWebGLProgram(p,[r,s,i],"float32")},TK={kernelName:Ei,backendName:"webgl",kernelFunc:CK},np;(function(e){e.Prod="*",e.Sum="+"})(np||(np={}));var L5=class{constructor(e,t,a,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===np.Prod?"1.0":"0.0",i=a?s:`getX(${W5(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";a?(l=n?`end != ${o-1}`:"end != 0",u=n?"end + 1":"end - 1"):(l=n?`end + pow2 < ${o}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=` + `}},CK=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new SK(r.shape,s.shape,o,l,u);return a.runWebGLProgram(p,[r,s,i],"float32")},TK={kernelName:Ci,backendName:"webgl",kernelFunc:CK},zd;(function(e){e.Prod="*",e.Sum="+"})(zd||(zd={}));var A5=class{constructor(e,t,a,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===zd.Prod?"1.0":"0.0",i=a?s:`getX(${b5(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";a?(l=n?`end != ${o-1}`:"end != 0",u=n?"end + 1":"end - 1"):(l=n?`end + pow2 < ${o}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=` void main() { ${ft(r)} coords = getOutputCoords(); - int end = ${B5(r,"coords",this.op)}; + int end = ${v5(r,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${l}) { int idx = ${u}; - ${B5(r,"coords",this.op)} = idx; - val ${this.op}= getX(${W5(r,"coords",this.op)}); + ${v5(r,"coords",this.op)} = idx; + val ${this.op}= getX(${b5(r,"coords",this.op)}); } setOutput(val); } - `}};function W5(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, 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a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(l),h}return c}function NK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return _8(np.Prod,r,a,s,i,o)}var RK={kernelName:Ni,backendName:"webgl",kernelFunc:NK};function EK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return _8(np.Sum,r,a,s,i,o)}var MK={kernelName:Ri,backendName:"webgl",kernelFunc:EK};function $K(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n;if(r.shape.length===1){let l=a.readSync(r.dataId),u=a.readSync(s.dataId),p=c8(l,u,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=a.bufferSync(r),u=a.bufferSync(s),p=_H(l,u,i,o);return a.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var PK={kernelName:gu,backendName:"webgl",kernelFunc:$K},_K=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=a,this.userCode=` + `}};function b5(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function v5(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function m8(e,t,a,n,r,s){let i=t.shape.length,o=C.getAxesPermutation([n],i),l=t;o!=null&&(l=Ca({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=C.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=en({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new A5(e,l.shape,!1,s),m=[[d]],f=c;c=a.runWebGLProgram(h,[c],c.dtype,m),a.disposeIntermediateTensorInfo(f)}if(r){let d=new A5(e,l.shape,r,s),h=c;c=a.runWebGLProgram(d,[c],c.dtype),a.disposeIntermediateTensorInfo(h)}if(o!=null){let d=C.getUndoAxesPermutation(o),h=Ca({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(l),h}return c}function NK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return m8(zd.Prod,r,a,s,i,o)}var RK={kernelName:Ii,backendName:"webgl",kernelFunc:NK};function EK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return m8(zd.Sum,r,a,s,i,o)}var MK={kernelName:Si,backendName:"webgl",kernelFunc:EK};function $K(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n;if(r.shape.length===1){let l=a.readSync(r.dataId),u=a.readSync(s.dataId),p=jv(l,u,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=a.bufferSync(r),u=a.bufferSync(s),p=_H(l,u,i,o);return a.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var PK={kernelName:Jl,backendName:"webgl",kernelFunc:$K},_K=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=a,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3023,7 +3023,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function FK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),m=i==="NHWC"?[o,c,d,h]:[o,h,c,d],f=new _K(m,s,i);return a.runWebGLProgram(f,[r],r.dtype)}var DK={kernelName:Mi,backendName:"webgl",kernelFunc:FK},F8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";a&&(n?l=`float activation(float a) { + `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function FK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),m=i==="NHWC"?[o,c,d,h]:[o,h,c,d],f=new _K(m,s,i);return a.runWebGLProgram(f,[r],r.dtype)}var DK={kernelName:Ti,backendName:"webgl",kernelFunc:FK},f8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";a&&(n?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${a} }`:r?l=`float activation(float a) { @@ -3076,7 +3076,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam ${u} setOutput(result); } - `}},D8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,c=p,d=` + `}},g8=class{constructor(e,t=!1,a=null,n=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ya(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,c=p,d=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let c=C.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),d;B().getBool("WEBGL_PACK_DEPTHWISECONV")&&c.strideWidth<=2&&c.outChannels/c.inChannels===1?d=new D8(c):d=new F8(c);let h=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];return a.runWebGLProgram(d,[r,s],"float32",h)}var zK={kernelName:$i,backendName:"webgl",kernelFunc:OK},LK=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` + `}};function OK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,p=l;p==null&&(p=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let c=C.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),d;B().getBool("WEBGL_PACK_DEPTHWISECONV")&&c.strideWidth<=2&&c.outChannels/c.inChannels===1?d=new g8(c):d=new f8(c);let h=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];return a.runWebGLProgram(d,[r,s],"float32",h)}var zK={kernelName:Ni,backendName:"webgl",kernelFunc:OK},LK=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -3345,13 +3345,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam } setOutput(dotProd); } - `}};function BK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=C.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new LK(c);return a.runWebGLProgram(d,[r,s],"float32")}var VK={kernelName:fp,backendName:"webgl",kernelFunc:BK};function UK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=C.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new WK(c);return a.runWebGLProgram(d,[r,s],"float32")}var GK={kernelName:gp,backendName:"webgl",kernelFunc:UK},HK=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function BK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=C.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new LK(c);return a.runWebGLProgram(d,[r,s],"float32")}var VK={kernelName:Yd,backendName:"webgl",kernelFunc:BK};function UK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=C.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new WK(c);return a.runWebGLProgram(d,[r,s],"float32")}var GK={kernelName:Zd,backendName:"webgl",kernelFunc:UK},HK=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function jK(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=pe({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new HK(s),l=a.runWebGLProgram(o,[i],i.dtype),u=pe({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(l),u}var qK={kernelName:yu,backendName:"webgl",kernelFunc:jK},XK=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:a,padInfo:n,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:c}=n;this.userCode=` + `}};function jK(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=pe({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new HK(s),l=a.runWebGLProgram(o,[i],i.dtype),u=pe({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(l),u}var qK={kernelName:Ql,backendName:"webgl",kernelFunc:jK},XK=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:a,padInfo:n,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:c}=n;this.userCode=` const ivec2 strides = ivec2(${r}, ${s}); const ivec2 pads = ivec2(${p}, ${c}); const float neg_infinity = -3.4e38; @@ -3389,7 +3389,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam float result = curVal; setOutput(result); } - `}};function KK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,c=new XK(u);p=a.runWebGLProgram(c,[r,s],"float32");let d=pe({inputs:{x:p},backend:a,attrs:{shape:u.outShape}});return a.disposeIntermediateTensorInfo(p),d}var YK={kernelName:Pi,backendName:"webgl",kernelFunc:KK};function ZK(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(r,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=C.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,m=[];for(let f=0;f=0&&(d=i0({inputs:{x:d},backend:a,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(d)),h--)}for(let f of m)f!==d&&a.disposeIntermediateTensorInfo(f);return d}var JK={kernelName:xp,backendName:"webgl",kernelFunc:ZK},QK="return (x >= 0.0) ? x : (exp(x) - 1.0);",eY=` + `}};function KK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,c=new XK(u);p=a.runWebGLProgram(c,[r,s],"float32");let d=pe({inputs:{x:p},backend:a,attrs:{shape:u.outShape}});return a.disposeIntermediateTensorInfo(p),d}var YK={kernelName:Ri,backendName:"webgl",kernelFunc:KK};function ZK(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(r,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=C.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,m=[];for(let f=0;f=0&&(d=Wh({inputs:{x:d},backend:a,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(d)),h--)}for(let f of m)f!==d&&a.disposeIntermediateTensorInfo(f);return d}var JK={kernelName:Qd,backendName:"webgl",kernelFunc:ZK},QK="return (x >= 0.0) ? x : (exp(x) - 1.0);",eY=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3398,12 +3398,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,tY=tt({opSnippet:QK,packedOpSnippet:eY}),aY={kernelName:Fi,backendName:"webgl",kernelFunc:tY},nY="return (b >= 0.0) ? a : a * (b + 1.0);",rY=` +`,tY=tt({opSnippet:QK,packedOpSnippet:eY}),aY={kernelName:"Elu",backendName:"webgl",kernelFunc:tY},nY="return (b >= 0.0) ? a : a * (b + 1.0);",rY=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,sY=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ju(rY,n.shape,r.shape):new ri(nY,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],n.dtype)},iY={kernelName:xu,backendName:"webgl",kernelFunc:sY},oY=` +`,sY=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=B().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Pu(rY,n.shape,r.shape):new ni(nY,n.shape,r.shape);return a.runWebGLProgram(s,[n,r],n.dtype)},iY={kernelName:eu,backendName:"webgl",kernelFunc:sY},oY=` return vec4(equal(a, b)); -`,lY="return float(a == b);",uY=ma({opSnippet:lY,packedOpSnippet:oY,dtype:"bool",cpuKernelImpl:LH}),dY={kernelName:Oi,backendName:"webgl",kernelFunc:uY},pY=` +`,lY="return float(a == b);",uY=ma({opSnippet:lY,packedOpSnippet:oY,dtype:"bool",cpuKernelImpl:LH}),dY={kernelName:Mi,backendName:"webgl",kernelFunc:uY},pY=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -3418,7 +3418,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,cY=tt({opSnippet:pY}),hY={kernelName:Di,backendName:"webgl",kernelFunc:cY},mY=Qu+` +`,cY=tt({opSnippet:pY}),hY={kernelName:"Erf",backendName:"webgl",kernelFunc:cY},mY=_u+` return exp(x); `,fY=` vec4 result = exp(x); @@ -3429,7 +3429,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam result.a = isNaN.a ? x.a : result.a; return result; -`,O8=tt({opSnippet:mY,packedOpSnippet:fY,cpuKernelImpl:WH,dtype:"float32"}),gY={kernelName:zi,backendName:"webgl",kernelFunc:O8};function B1(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),pe({inputs:{x:s},backend:n,attrs:{shape:o}})}var yY={kernelName:Au,backendName:"webgl",kernelFunc:B1},V5="return exp(x) - 1.0;",xY=tt({opSnippet:V5,packedOpSnippet:V5,cpuKernelImpl:BH}),AY={kernelName:Li,backendName:"webgl",kernelFunc:xY},U5=class{constructor(e,t,a){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let r=a?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=a?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` +`,y8=tt({opSnippet:mY,packedOpSnippet:fY,cpuKernelImpl:WH,dtype:"float32"}),gY={kernelName:"Exp",backendName:"webgl",kernelFunc:y8};function v1(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),pe({inputs:{x:s},backend:n,attrs:{shape:o}})}var yY={kernelName:tu,backendName:"webgl",kernelFunc:v1},w5="return exp(x) - 1.0;",xY=tt({opSnippet:w5,packedOpSnippet:w5,cpuKernelImpl:BH}),AY={kernelName:$i,backendName:"webgl",kernelFunc:xY},k5=class{constructor(e,t,a){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let r=a?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=a?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` const float exponentMultiplier = ${r}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -3462,12 +3462,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function z8(e,t,a){let n=a.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=pe({inputs:{x:e},backend:a,attrs:{shape:[i,s]}}),l=o.shape,u=new U5("real",l,t),p=new U5("imag",l,t),c=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),m=fs({inputs:{real:d,imag:h},backend:a});a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h);let f=pe({inputs:{x:m},backend:a,attrs:{shape:e.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(m),f}function bY(e){let{inputs:t,backend:a}=e,{input:n}=t;return z8(n,!1,a)}var vY={kernelName:Ap,backendName:"webgl",kernelFunc:bY},wY=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function x8(e,t,a){let n=a.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=pe({inputs:{x:e},backend:a,attrs:{shape:[i,s]}}),l=o.shape,u=new k5("real",l,t),p=new k5("imag",l,t),c=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],d=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(p,c,"float32"),m=ms({inputs:{real:d,imag:h},backend:a});a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h);let f=pe({inputs:{x:m},backend:a,attrs:{shape:e.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(m),f}function bY(e){let{inputs:t,backend:a}=e,{input:n}=t;return x8(n,!1,a)}var vY={kernelName:"FFT",backendName:"webgl",kernelFunc:bY},wY=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); 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-`,hZ=ma({opSnippet:pZ,packedOpSnippet:cZ,cpuKernelImpl:qH,dtype:"bool"}),mZ={kernelName:Ji,backendName:"webgl",kernelFunc:hZ},fZ="return float(a <= b);",gZ=` +`,hZ=ma({opSnippet:pZ,packedOpSnippet:cZ,cpuKernelImpl:qH,dtype:"bool"}),mZ={kernelName:Hi,backendName:"webgl",kernelFunc:hZ},fZ="return float(a <= b);",gZ=` return vec4(lessThanEqual(a, b)); -`,yZ=ma({opSnippet:fZ,packedOpSnippet:gZ,cpuKernelImpl:XH,dtype:"bool"}),xZ={kernelName:Qi,backendName:"webgl",kernelFunc:yZ};function AZ(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=KH(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var bZ={kernelName:eo,backendName:"webgl",kernelFunc:AZ},vZ=Qu+` +`,yZ=ma({opSnippet:fZ,packedOpSnippet:gZ,cpuKernelImpl:XH,dtype:"bool"}),xZ={kernelName:ji,backendName:"webgl",kernelFunc:yZ};function AZ(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=KH(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var bZ={kernelName:qi,backendName:"webgl",kernelFunc:AZ},vZ=_u+` return x < 0.0 ? 0./0. : log(x); 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-`,OZ=ma({opSnippet:FZ,packedOpSnippet:DZ,dtype:"bool"}),zZ={kernelName:so,backendName:"webgl",kernelFunc:OZ},LZ=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` +`,OZ=ma({opSnippet:FZ,packedOpSnippet:DZ,dtype:"bool"}),zZ={kernelName:Zi,backendName:"webgl",kernelFunc:OZ},LZ=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3700,7 +3700,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam vec4 result = xAtOutputCoords * ${o}; setOutput(result); } - `}},BZ=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=B().getBool("WEBGL_PACK_NORMALIZATION")?new WZ(r.shape,s,i,o,l):new LZ(r.shape,s,i,o,l);return a.runWebGLProgram(u,[r],r.dtype)},VZ={kernelName:io,backendName:"webgl",kernelFunc:BZ},UZ=class{constructor(e,t,a,n,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=a,this.alpha=n,this.beta=r,this.userCode=` + `}},BZ=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=B().getBool("WEBGL_PACK_NORMALIZATION")?new WZ(r.shape,s,i,o,l):new LZ(r.shape,s,i,o,l);return a.runWebGLProgram(u,[r],r.dtype)},VZ={kernelName:"LRN",backendName:"webgl",kernelFunc:BZ},UZ=class{constructor(e,t,a,n,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=a,this.alpha=n,this.beta=r,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3755,16 +3755,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam } setOutput(result); } - `}},GZ=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new UZ(r.shape,o,l,u,p);return a.runWebGLProgram(c,[r,s,i],r.dtype)},HZ={kernelName:wu,backendName:"webgl",kernelFunc:GZ};function jZ(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=ol(i,e.dtype,"max",n),l=pe({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function W8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=C.getAxesPermutation(u,o),c=p!=null,d=a.shouldExecuteOnCPU([r]),h=r;if(c){if(d){let x=a.texData.get(h.dataId).values,A=new Array(o);for(let I=0;I{let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new UZ(r.shape,o,l,u,p);return a.runWebGLProgram(c,[r,s,i],r.dtype)},HZ={kernelName:ru,backendName:"webgl",kernelFunc:GZ};function jZ(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=Go(i,e.dtype,"max",n),l=pe({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function b8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=C.getAxesPermutation(u,o),c=p!=null,d=a.shouldExecuteOnCPU([r]),h=r;if(c){if(d){let x=a.texData.get(h.dataId).values,A=new Array(o);for(let I=0;I`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return en({inputs:{x:r},backend:a});let c=new ap(p,"max",!1);return a.runWebGLProgram(c,[r],r.dtype)}var QZ={kernelName:uo,backendName:"webgl",kernelFunc:JZ};function eJ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=C.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new W3(c,"max",!1);return a.runWebGLProgram(d,[r],r.dtype)}var tJ={kernelName:ku,backendName:"webgl",kernelFunc:eJ},aJ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,a=e.strideWidth,n=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` +`,YZ=ma({opSnippet:XZ,packedOpSnippet:KZ,cpuKernelImpl:JH}),ZZ={kernelName:Ji,backendName:"webgl",kernelFunc:YZ};function JZ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;Nu(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return en({inputs:{x:r},backend:a});let c=new Od(p,"max",!1);return a.runWebGLProgram(c,[r],r.dtype)}var QZ={kernelName:Qi,backendName:"webgl",kernelFunc:JZ};function eJ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=C.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new b3(c,"max",!1);return a.runWebGLProgram(d,[r],r.dtype)}var tJ={kernelName:su,backendName:"webgl",kernelFunc:eJ},aJ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,a=e.strideWidth,n=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -3874,16 +3874,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam } setOutput(dotProd); } - `}};function rJ(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,c,u,p),h=new W3(d,"max",!0),m=a.runWebGLProgram(h,[i],i.dtype),f=new nJ(d),g=a.runWebGLProgram(f,[r,m],i.dtype);return a.disposeIntermediateTensorInfo(m),g}var sJ={kernelName:kp,backendName:"webgl",kernelFunc:rJ};function iJ(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;qu([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=C.computePool2DInfo(o.shape,l,u,1,p,c),h=!0,m=new ap(d,"max",h),f=a.runWebGLProgram(m,[o],o.dtype),g=new aJ(d),y=a.runWebGLProgram(g,[r,f],o.dtype);return a.disposeIntermediateTensorInfo(f),y}var oJ={kernelName:wp,backendName:"webgl",kernelFunc:iJ};function lJ(e,t,a,n){let r=new ap(a,"max",!1),s=n.runWebGLProgram(r,[e],"float32");r=new ap(a,"max",!0,!0,t);let i=n.runWebGLProgram(r,[e],"float32");return[s,i]}var uJ={kernelName:Iu,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=a;v.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];v.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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int end = ${i}; @@ -3968,13 +3968,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eX=tt({opSnippet:Qq}),tX={kernelNam ${d} setOutput(result); } - `}},bJ=({inputs:e,backend:t,attrs:a})=>{let{x:n}=e,{paddings:r,mode:s}=a,i=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new AJ(n.shape,r,s):new xJ(n.shape,r,s);return t.runWebGLProgram(i,[n],n.dtype)},vJ={kernelName:mo,backendName:"webgl",kernelFunc:bJ},wJ=`if (b == 0.0) return NAN; + `}},bJ=({inputs:e,backend:t,attrs:a})=>{let{x:n}=e,{paddings:r,mode:s}=a,i=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new AJ(n.shape,r,s):new xJ(n.shape,r,s);return t.runWebGLProgram(i,[n],n.dtype)},vJ={kernelName:ao,backendName:"webgl",kernelFunc:bJ},wJ=`if (b == 0.0) return NAN; return mod(a, b);`,kJ=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - `+il+` + `+Uo+` return result; -`,IJ=ma({opSnippet:wJ,packedOpSnippet:kJ}),SJ={kernelName:fo,backendName:"webgl",kernelFunc:IJ},CJ=class{constructor(e,t,a){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,a],this.userCode=` +`,IJ=ma({opSnippet:wJ,packedOpSnippet:kJ}),SJ={kernelName:"Mod",backendName:"webgl",kernelFunc:IJ},CJ=class{constructor(e,t,a){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,a],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4016,7 +4016,7 @@ return a / b;`,NJ=` } return result; -`,B8=ma({opSnippet:TJ,packedOpSnippet:NJ,checkOutOfBounds:!0}),RJ={kernelName:_i,backendName:"webgl",kernelFunc:B8},H5="return a - b;",V8=ma({opSnippet:H5,packedOpSnippet:H5,supportsComplex:!0,cpuKernelImpl:bj}),EJ={kernelName:Ko,backendName:"webgl",kernelFunc:V8};function U8(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=v.parseAxisParam([s],r.shape),o=W8({inputs:{x:r},backend:a,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=pe({inputs:{x:o},backend:a,attrs:{shape:l}}),p=V8({inputs:{a:r,b:u},backend:a}),c=O8({inputs:{x:p},backend:a}),d=i0({inputs:{x:c},backend:a,attrs:{axis:i,keepDims:!1}}),h=pe({inputs:{x:d},backend:a,attrs:{shape:l}}),m=B8({inputs:{a:c,b:h},backend:a});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),m}var MJ={kernelName:Ho,backendName:"webgl",kernelFunc:U8};function $J(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:U8({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new CJ(u,p,s),d=[[i]],h=a.runWebGLProgram(c,[l],"int32",d);return o||a.disposeIntermediateTensorInfo(l),h}var PJ={kernelName:go,backendName:"webgl",kernelFunc:$J},_J=Mn+` +`,v8=ma({opSnippet:TJ,packedOpSnippet:NJ,checkOutOfBounds:!0}),RJ={kernelName:Ei,backendName:"webgl",kernelFunc:v8},S5="return a - b;",w8=ma({opSnippet:S5,packedOpSnippet:S5,supportsComplex:!0,cpuKernelImpl:bj}),EJ={kernelName:"Sub",backendName:"webgl",kernelFunc:w8};function k8(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=v.parseAxisParam([s],r.shape),o=b8({inputs:{x:r},backend:a,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),u=pe({inputs:{x:o},backend:a,attrs:{shape:l}}),p=w8({inputs:{a:r,b:u},backend:a}),c=y8({inputs:{x:p},backend:a}),d=Wh({inputs:{x:c},backend:a,attrs:{axis:i,keepDims:!1}}),h=pe({inputs:{x:d},backend:a,attrs:{shape:l}}),m=v8({inputs:{a:c,b:h},backend:a});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(u),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(h),m}var MJ={kernelName:Eo,backendName:"webgl",kernelFunc:k8};function $J(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:k8({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new CJ(u,p,s),d=[[i]],h=a.runWebGLProgram(c,[l],"int32",d);return o||a.disposeIntermediateTensorInfo(l),h}var PJ={kernelName:no,backendName:"webgl",kernelFunc:$J},_J=Mn+` return -x; `,FJ=` vec4 result = -x; @@ -4028,14 +4028,14 @@ return a / b;`,NJ=` result.a = isNaN.a ? x.a : result.a; return result; -`;function DJ(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.texData.get(n.dataId),[i,o]=tj(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r;return B().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new qr(n.shape,FJ):r=new Jn(n.shape,_J),a.runWebGLProgram(r,[n],n.dtype)}var OJ={kernelName:Su,backendName:"webgl",kernelFunc:DJ},zJ=En.nonMaxSuppressionV3Impl;function LJ(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. 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QJ={kernelName:Nu,backendName:"webgl",kernelFunc:JJ},eQ=class{constructor(e,t,a){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,r=ft(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=` + `}},XJ=e=>{let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new qJ(u,i,o,l),c=pe({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=a.runWebGLProgram(p,[c],s);a.disposeIntermediateTensorInfo(c);let h=[...r.shape,i],m=pe({inputs:{x:d},backend:a,attrs:{shape:h}});return a.disposeIntermediateTensorInfo(d),m},KJ={kernelName:lo,backendName:"webgl",kernelFunc:XJ};function sh(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=Pp({inputs:{input:n},backend:a}),s=sh({inputs:{x:r},backend:a}),i=Bh({inputs:{input:n},backend:a}),o=sh({inputs:{x:i},backend:a}),l=ms({inputs:{real:s,imag:o},backend:a});return a.disposeIntermediateTensorInfo(r),a.disposeIntermediateTensorInfo(s),a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}else return _p({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var YJ={kernelName:ku,backendName:"webgl",kernelFunc:sh};function I8(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=Pp({inputs:{input:n},backend:a}),s=I8({inputs:{x:r},backend:a}),i=Bh({inputs:{input:n},backend:a}),o=sh({inputs:{x:i},backend:a}),l=ms({inputs:{real:s,imag:o},backend:a});return a.disposeIntermediateTensorInfo(r),a.disposeIntermediateTensorInfo(s),a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}else return _p({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var ZJ={kernelName:lu,backendName:"webgl",kernelFunc:I8};function JJ(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return v1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=v1({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=u8({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeIntermediateTensorInfo(p)),u}var QJ={kernelName:uu,backendName:"webgl",kernelFunc:JJ},eQ=class{constructor(e,t,a){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,r=ft(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -4084,7 +4084,7 @@ return a / b;`,NJ=` ${h} setOutput(result); } - `}},H8=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return ec({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new tQ(r.shape,s,i):new eQ(r.shape,s,i),l=[[i]];return a.runWebGLProgram(o,[r],r.dtype,l)},aQ={kernelName:wo,backendName:"webgl",kernelFunc:H8},nQ=` + `}},S8=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return _p({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new tQ(r.shape,s,i):new eQ(r.shape,s,i),l=[[i]];return a.runWebGLProgram(o,[r],r.dtype,l)},aQ={kernelName:uo,backendName:"webgl",kernelFunc:S8},nQ=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4109,9 +4109,9 @@ return a / b;`,NJ=` bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); - `+il+` + `+Uo+` return result; -`,sQ=ma({opSnippet:nQ,packedOpSnippet:rQ}),iQ={kernelName:ko,backendName:"webgl",kernelFunc:sQ};function oQ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,c=C.getAxesPermutation(p,o),d=r;c!=null&&(d=Ca({inputs:{x:r},backend:a,attrs:{perm:c}}),p=C.getInnerMostAxes(p.length,o),l.push(d)),C.assertAxesAreInnerMostDims("prod",p,o);let h;if(a.shouldExecuteOnCPU([d])){let m=a.texData.get(d.dataId).values,{outVals:f,outShape:g,outDtype:y}=nj(d.shape,d.dtype,m,p);h=a.makeTensorInfo(g,y,f)}else{let[m,f]=C.computeOutAndReduceShapes(d.shape,p),g=v.sizeFromShape(f),y=pe({inputs:{x:d},backend:a,attrs:{shape:[-1,g]}}),x=_p(r.dtype),A=ol(y,x,"prod",a);h=pe({inputs:{x:A},backend:a,attrs:{shape:m}}),l.push(y),l.push(A)}if(i){l.push(h);let m=C.expandShapeToKeepDim(h.shape,u);h=pe({inputs:{x:h},backend:a,attrs:{shape:m}})}return l.forEach(m=>a.disposeIntermediateTensorInfo(m)),h}var lQ={kernelName:So,backendName:"webgl",kernelFunc:oQ};function uQ(e){let{inputs:t,backend:a,attrs:n}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=n,l=r.map(y=>a.readSync(y.dataId)),u=r.map(y=>y.shape),p=a.readSync(s.dataId),c=a.readSync(i.dataId),[d,h,m]=rj(l,u,p,s.shape,s.dtype,c,i.shape,o),f=d.map(y=>a.makeTensorInfo([y.length],"int32",y)),g=a.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var dQ={kernelName:$h,backendName:"webgl",kernelFunc:uQ};function pQ(e){let{inputs:t,backend:a}=e,{starts:n,limits:r,deltas:s}=t,i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=sj(i,n.shape,n.dtype,o,r.shape,l,s.shape),c=a.makeTensorInfo([u.length],"int32",u),d=a.makeTensorInfo([p.length],n.dtype,p);return[c,d]}var cQ={kernelName:Ph,backendName:"webgl",kernelFunc:pQ};function hQ(e){let{inputs:t,backend:a,attrs:n}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),c=a.readSync(i.dataId),d=o.map(g=>a.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=ij(u,r.shape,p,s.shape,s.dtype,c,i.shape,d,h,l);return a.makeTensorInfo(m,s.dtype,f)}var mQ={kernelName:_h,backendName:"webgl",kernelFunc:hQ},j8=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=oj(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},fQ={kernelName:Ru,backendName:"webgl",kernelFunc:j8},gQ="return 1.0 / x;",yQ=tt({opSnippet:gQ}),xQ={kernelName:Co,backendName:"webgl",kernelFunc:yQ},AQ=Mn+` +`,sQ=ma({opSnippet:nQ,packedOpSnippet:rQ}),iQ={kernelName:"Pow",backendName:"webgl",kernelFunc:sQ};function oQ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,c=C.getAxesPermutation(p,o),d=r;c!=null&&(d=Ca({inputs:{x:r},backend:a,attrs:{perm:c}}),p=C.getInnerMostAxes(p.length,o),l.push(d)),C.assertAxesAreInnerMostDims("prod",p,o);let h;if(a.shouldExecuteOnCPU([d])){let m=a.texData.get(d.dataId).values,{outVals:f,outShape:g,outDtype:y}=nj(d.shape,d.dtype,m,p);h=a.makeTensorInfo(g,y,f)}else{let[m,f]=C.computeOutAndReduceShapes(d.shape,p),g=v.sizeFromShape(f),y=pe({inputs:{x:d},backend:a,attrs:{shape:[-1,g]}}),x=mp(r.dtype),A=Go(y,x,"prod",a);h=pe({inputs:{x:A},backend:a,attrs:{shape:m}}),l.push(y),l.push(A)}if(i){l.push(h);let m=C.expandShapeToKeepDim(h.shape,u);h=pe({inputs:{x:h},backend:a,attrs:{shape:m}})}return l.forEach(m=>a.disposeIntermediateTensorInfo(m)),h}var lQ={kernelName:co,backendName:"webgl",kernelFunc:oQ};function uQ(e){let{inputs:t,backend:a,attrs:n}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=n,l=r.map(y=>a.readSync(y.dataId)),u=r.map(y=>y.shape),p=a.readSync(s.dataId),c=a.readSync(i.dataId),[d,h,m]=rj(l,u,p,s.shape,s.dtype,c,i.shape,o),f=d.map(y=>a.makeTensorInfo([y.length],"int32",y)),g=a.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var dQ={kernelName:ch,backendName:"webgl",kernelFunc:uQ};function pQ(e){let{inputs:t,backend:a}=e,{starts:n,limits:r,deltas:s}=t,i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=sj(i,n.shape,n.dtype,o,r.shape,l,s.shape),c=a.makeTensorInfo([u.length],"int32",u),d=a.makeTensorInfo([p.length],n.dtype,p);return[c,d]}var cQ={kernelName:hh,backendName:"webgl",kernelFunc:pQ};function hQ(e){let{inputs:t,backend:a,attrs:n}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),c=a.readSync(i.dataId),d=o.map(g=>a.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=ij(u,r.shape,p,s.shape,s.dtype,c,i.shape,d,h,l);return a.makeTensorInfo(m,s.dtype,f)}var mQ={kernelName:mh,backendName:"webgl",kernelFunc:hQ},C8=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=oj(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},fQ={kernelName:du,backendName:"webgl",kernelFunc:C8},gQ="return 1.0 / x;",yQ=tt({opSnippet:gQ}),xQ={kernelName:ho,backendName:"webgl",kernelFunc:yQ},AQ=Mn+` return (x < 0.0) ? 0.0 : x; `,bQ=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); @@ -4123,7 +4123,7 @@ return a / b;`,NJ=` result.a = isNaN.a ? x.a : result.a; return result; -`,vQ=tt({opSnippet:AQ,packedOpSnippet:bQ}),wQ={kernelName:To,backendName:"webgl",kernelFunc:vQ},kQ=Mn+` +`,vQ=tt({opSnippet:AQ,packedOpSnippet:bQ}),wQ={kernelName:mo,backendName:"webgl",kernelFunc:vQ},kQ=Mn+` return (x < 0.0) ? 0.0 : min(6.0, x); `,IQ=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); @@ -4135,7 +4135,7 @@ return a / b;`,NJ=` result.a = isNaN.a ? x.a : result.a; return result; -`,SQ=tt({opSnippet:kQ,packedOpSnippet:IQ}),CQ={kernelName:Eo,backendName:"webgl",kernelFunc:SQ},TQ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,SQ=tt({opSnippet:kQ,packedOpSnippet:IQ}),CQ={kernelName:yo,backendName:"webgl",kernelFunc:SQ},TQ=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4245,7 +4245,7 @@ return a / b;`,NJ=` setOutput(newValue); } - `}};function RQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=B().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new NQ(r.shape,l,u,s,i):new TQ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],"float32")}var EQ={kernelName:Ro,backendName:"webgl",kernelFunc:RQ},MQ=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,m=Math.ceil(d)*2+2;this.userCode=` + `}};function RQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=B().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new NQ(r.shape,l,u,s,i):new TQ(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],"float32")}var EQ={kernelName:go,backendName:"webgl",kernelFunc:RQ},MQ=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,m=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4326,7 +4326,7 @@ return a / b;`,NJ=` setOutput(accumulator); } - `}};function $Q(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new MQ(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var PQ={kernelName:$u,backendName:"webgl",kernelFunc:$Q},_Q=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function $Q(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new MQ(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var PQ={kernelName:hu,backendName:"webgl",kernelFunc:$Q},_Q=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],p=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4389,7 +4389,7 @@ return a / b;`,NJ=` setOutput(newValue); } - `}};function DQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=B().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new FQ(r.shape,l,u,s,i):new _Q(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],r.dtype)}var OQ={kernelName:No,backendName:"webgl",kernelFunc:DQ},zQ=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,m=Math.ceil(d)*2+2;this.userCode=` + `}};function DQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=B().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new FQ(r.shape,l,u,s,i):new _Q(r.shape,l,u,s,i);return a.runWebGLProgram(p,[r],r.dtype)}var OQ={kernelName:fo,backendName:"webgl",kernelFunc:DQ},zQ=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],c=1/u,d=1/p,h=Math.ceil(c)*2+2,m=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4459,7 +4459,7 @@ return a / b;`,NJ=` setOutput(accumulator); } - `}};function LQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new zQ(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var WQ={kernelName:Mu,backendName:"webgl",kernelFunc:LQ},BQ=class{constructor(e,t){this.variableNames=["x"];let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);if(this.outputShape=e,a===1){this.userCode=` + `}};function LQ(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new zQ(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var WQ={kernelName:cu,backendName:"webgl",kernelFunc:LQ},BQ=class{constructor(e,t){this.variableNames=["x"];let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);if(this.outputShape=e,a===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); @@ -4497,7 +4497,7 @@ return a / b;`,NJ=` } setOutput(result); } - `;function o(h){return c(h)}function l(h){return h[a-1]="("+h[a-1]+" + 1)",c(h)}function u(h){return h[a-2]="("+h[a-2]+" + 1)",c(h)}function p(h){return h[a-1]="("+h[a-1]+" + 1)",h[a-2]="("+h[a-2]+" + 1)",c(h)}function c(h){let m=e.map((y,x)=>d(x,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function d(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function UQ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return en({inputs:{x:r},backend:a});let l=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VQ(r.shape,o):new BQ(r.shape,o);return a.runWebGLProgram(l,[r],r.dtype)}var GQ={kernelName:Mo,backendName:"webgl",kernelFunc:UQ},HQ=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let a=e[1],n=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` + `;function o(h){return c(h)}function l(h){return h[a-1]="("+h[a-1]+" + 1)",c(h)}function u(h){return h[a-2]="("+h[a-2]+" + 1)",c(h)}function p(h){return h[a-1]="("+h[a-1]+" + 1)",h[a-2]="("+h[a-2]+" + 1)",c(h)}function c(h){let m=e.map((y,x)=>d(x,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function d(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function UQ(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return en({inputs:{x:r},backend:a});let l=B().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VQ(r.shape,o):new BQ(r.shape,o);return a.runWebGLProgram(l,[r],r.dtype)}var GQ={kernelName:xo,backendName:"webgl",kernelFunc:UQ},HQ=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let a=e[1],n=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` vec3 fill = vec3(${t.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -4516,7 +4516,7 @@ return a / b;`,NJ=` } setOutput(outputValue); } - `}},jQ={kernelName:el,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new HQ(n.shape,s),[u,p]=C.getImageCenter(i,n.shape[1],n.shape[2]),c=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[n],n.dtype,c)}},qQ=` + `}},jQ={kernelName:Oo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new HQ(n.shape,s),[u,p]=C.getImageCenter(i,n.shape[1],n.shape[2]),c=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[n],n.dtype,c)}},qQ=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4531,7 +4531,7 @@ return a / b;`,NJ=` return base + 1.0; } } -`,XQ=tt({opSnippet:qQ}),KQ={kernelName:$o,backendName:"webgl",kernelFunc:XQ},YQ="return inversesqrt(x);",ZQ=tt({opSnippet:YQ,cpuKernelImpl:lj}),JQ={kernelName:Po,backendName:"webgl",kernelFunc:ZQ},B3=class{constructor(e,t,a,n,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let l=ft(r.length),u=ft(s.length),p="";a===1?p="i":a===2&&(p="i, j");let c=`getIndices(${p})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let h=`getUpdates(${d})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides";this.userCode=` +`,XQ=tt({opSnippet:qQ}),KQ={kernelName:Ao,backendName:"webgl",kernelFunc:XQ},YQ="return inversesqrt(x);",ZQ=tt({opSnippet:YQ,cpuKernelImpl:lj}),JQ={kernelName:bo,backendName:"webgl",kernelFunc:ZQ},v3=class{constructor(e,t,a,n,r,s,i=!0,o=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let l=ft(r.length),u=ft(s.length),p="";a===1?p="i":a===2&&(p="i, j");let c=`getIndices(${p})`,d="";n===1?d="i":n===2&&(d="i, coords[1]");let h=`getUpdates(${d})`,m="";o&&(m="coords[0], coords[1]");let f=`getDefaultValue(${m})`,g=t>1?"strides[j]":"strides";this.userCode=` ${l} strides = ${l}(${r}); void main() { @@ -4588,7 +4588,7 @@ return a / b;`,NJ=` } setOutput(mix(${f}, sum, found)); } - `}};function eee(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=C.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return 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eee(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=C.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=pe({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),m=pe({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),f=a.makeTensorInfo([],"float32",new Float32Array([0])),g;B().getBool("WEBGL_PACK")?g=new QQ(l,o,h.shape.length,m.shape.length,p,d):g=new v3(l,o,h.shape.length,m.shape.length,p,d);let y=a.runWebGLProgram(g,[m,h,f],m.dtype),x=pe({inputs:{x:y},backend:a,attrs:{shape:i}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(f),x}var tee={kernelName:vo,backendName:"webgl",kernelFunc:eee},aee=class{constructor(e,t,a,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,a];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=B().getNumber("WEBGL_VERSION")===2?r:s,o=n==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; @@ -4613,7 +4613,7 @@ return a / b;`,NJ=` setOutput(float(findBound(batch, value))); } - `}};function nee(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n,o=new aee(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return a.runWebGLProgram(o,[r,s],"int32",l)}var ree={kernelName:Do,backendName:"webgl",kernelFunc:nee},see=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.outputShape=t;let n,r;if(a>4)throw Error(`Where for rank ${a} is not yet supported`);if(a===1)r="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u4)throw Error(`Where for rank ${a} is not yet supported`);if(a===1)r="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,uee=tt({opSnippet:lee}),dee={kernelName:Oo,backendName:"webgl",kernelFunc:uee},pee=Qu+` +`,uee=tt({opSnippet:lee}),dee={kernelName:Io,backendName:"webgl",kernelFunc:uee},pee=_u+` return 1.0 / (1.0 + exp(-1.0 * x)); `,cee=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); @@ -4641,20 +4641,20 @@ return a / b;`,NJ=` result.a = isNaN.a ? x.a : result.a; return result; -`,hee=tt({opSnippet:pee,packedOpSnippet:cee,cpuKernelImpl:dj}),mee={kernelName:Bo,backendName:"webgl",kernelFunc:hee},fee=` +`,hee=tt({opSnippet:pee,packedOpSnippet:cee,cpuKernelImpl:dj}),mee={kernelName:To,backendName:"webgl",kernelFunc:hee},fee=` if (isnan(x)) { return 0.0; } return sign(x); -`,gee=tt({opSnippet:fee}),yee={kernelName:Wo,backendName:"webgl",kernelFunc:gee},xee=Qu+` +`,gee=tt({opSnippet:fee}),yee={kernelName:Co,backendName:"webgl",kernelFunc:gee},xee=_u+` return sin(x); `,Aee=` vec4 result = sin(x); bvec4 isNaN = isnan(x); - ${il} + ${Uo} return result; -`,bee=tt({opSnippet:xee,packedOpSnippet:Aee}),vee={kernelName:zo,backendName:"webgl",kernelFunc:bee},wee=` +`,bee=tt({opSnippet:xee,packedOpSnippet:Aee}),vee={kernelName:"Sin",backendName:"webgl",kernelFunc:bee},wee=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,kee=tt({opSnippet:wee}),Iee={kernelName:Lo,backendName:"webgl",kernelFunc:kee},See=` +`,kee=tt({opSnippet:wee}),Iee={kernelName:So,backendName:"webgl",kernelFunc:kee},See=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -4674,17 +4674,17 @@ return a / b;`,NJ=` result = log(exp_x + 1.0); } return result; -`,Cee=tt({opSnippet:See}),Tee={kernelName:Vo,backendName:"webgl",kernelFunc:Cee},Nee=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,x)=>y*x),l=[[0,0]];l.push(...i);for(let 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i=Array.from(a.readSync(r.dataId)),o=a.readSync(n.dataId),l=Array.from(a.readSync(s.dataId)),[u,p,c]=hj(o,n.shape,n.dtype,i,l);return[a.makeTensorInfo(p,n.dtype,u),a.makeTensorInfo([c.length],s.dtype,new Int32Array(c))]}var Pee={kernelName:Ou,backendName:"webgl",kernelFunc:$ee};function _ee(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape + ${i.shape}`);let o=a.readSync(n.dataId),l=a.readSync(r.dataId),u=a.readSync(s.dataId),p=a.readSync(i.dataId)[0],[c,d,h,m,f]=cj(o,n.shape,n.dtype,l,r.dtype,u,p);return[a.makeTensorInfo(d,n.dtype,c),a.makeTensorInfo([d[0]],r.dtype,h),a.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),a.makeTensorInfo([f.length],n.dtype,new Int32Array(f))]}var Mee={kernelName:sp,backendName:"webgl",kernelFunc:Eee};function $ee(e){let{inputs:t,backend:a}=e,{inputIndices:n,inputShape:r,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(a.readSync(r.dataId)),o=a.readSync(n.dataId),l=Array.from(a.readSync(s.dataId)),[u,p,c]=hj(o,n.shape,n.dtype,i,l);return[a.makeTensorInfo(p,n.dtype,u),a.makeTensorInfo([c.length],s.dtype,new Int32Array(c))]}var Pee={kernelName:xu,backendName:"webgl",kernelFunc:$ee};function _ee(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be 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Wee(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=C.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let m=ed({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return p[o]+=d,m})}var Bee={kernelName:Du,backendName:"webgl",kernelFunc:Wee},j5="return sqrt(x);",Vee=tt({opSnippet:j5,packedOpSnippet:j5,cpuKernelImpl:mj}),Uee={kernelName:Uo,backendName:"webgl",kernelFunc:Vee},Gee="return x * x;",Hee=tt({opSnippet:Gee}),jee={kernelName:Cp,backendName:"webgl",kernelFunc:Hee},q5="return (a - b) * (a - b);",qee=ma({opSnippet:q5,packedOpSnippet:q5}),Xee={kernelName:qo,backendName:"webgl",kernelFunc:qee};function Kee(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;if(r.dtype!=="string")throw new Error("Input must be of datatype string");let s=a.readSync(r.dataId),i=C.fromUint8ToStringArray(s),o=fj(i,"string",n);return a.makeTensorInfo(r.shape,"string",o)}var Yee={kernelName:Tp,backendName:"webgl",kernelFunc:Kee};function Zee({inputs:e,attrs:t,backend:a}){let{x:n}=e,r=Mn+` + ${s.shape}`);let i=a.readSync(n.dataId),o=a.readSync(r.dataId),l=a.readSync(s.dataId),[u,p]=Xv(i,n.shape,n.dtype,o,l);return a.makeTensorInfo(p,n.dtype,u)}var Oee={kernelName:bu,backendName:"webgl",kernelFunc:Dee};function zee(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=C.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let y=a.bufferSync(r),x=a.bufferSync(s),A=v.decodeString(a.readSync(i.dataId)[0]),b=uj(y,x,o,d,p,u,l,c,A,h);return a.makeTensorInfo(o,b.dtype,b.values)}let m=new v3(u,l,r.shape.length,s.shape.length,c,[d,1],h),f=a.runWebGLProgram(m,[s,r,i],s.dtype),g=pe({inputs:{x:f},backend:a,attrs:{shape:o}});return a.disposeIntermediateTensorInfo(f),g}var Lee={kernelName:Mo,backendName:"webgl",kernelFunc:zee};function Wee(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=C.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let m=Fu({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return p[o]+=d,m})}var Bee={kernelName:yu,backendName:"webgl",kernelFunc:Wee},C5="return sqrt(x);",Vee=tt({opSnippet:C5,packedOpSnippet:C5,cpuKernelImpl:mj}),Uee={kernelName:Ro,backendName:"webgl",kernelFunc:Vee},Gee="return x * x;",Hee=tt({opSnippet:Gee}),jee={kernelName:ip,backendName:"webgl",kernelFunc:Hee},T5="return (a - b) * (a - b);",qee=ma({opSnippet:T5,packedOpSnippet:T5}),Xee={kernelName:$o,backendName:"webgl",kernelFunc:qee};function Kee(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;if(r.dtype!=="string")throw new Error("Input must be of datatype string");let s=a.readSync(r.dataId),i=C.fromUint8ToStringArray(s),o=fj(i,"string",n);return a.makeTensorInfo(r.shape,"string",o)}var Yee={kernelName:op,backendName:"webgl",kernelFunc:Kee};function Zee({inputs:e,attrs:t,backend:a}){let{x:n}=e,r=Mn+` return x > 0.0 ? 1.0 : float(${t.alpha}); - `,s=new Jn(n.shape,r);return a.runWebGLProgram(s,[n],n.dtype)}var Jee={kernelName:ps,backendName:"webgl",kernelFunc:Zee},Qee=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=a;let n=a.length,r=ft(a.length),s=ft(a.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=a.map((l,u)=>(o++,a.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` + `,s=new Jn(n.shape,r);return a.runWebGLProgram(s,[n],n.dtype)}var Jee={kernelName:ds,backendName:"webgl",kernelFunc:Zee},Qee=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=a;let n=a.length,r=ft(a.length),s=ft(a.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=a.map((l,u)=>(o++,a.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * 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Qee(x,b,h);w=a.runWebGLProgram(T,[r],r.dtype)}let I=pe({inputs:{x:w},backend:a,attrs:{shape:m}});return a.disposeIntermediateTensorInfo(w),I}var tte={kernelName:Xo,backendName:"webgl",kernelFunc:ete};function ate(e){let{inputs:t,backend:a,attrs:n}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:p,dataSplits:c}=t,d=a.readSync(p.dataId),h=a.readSync(c.dataId),[m,f]=yj(d,h,r,s,i,o,l,u);return[a.makeTensorInfo([m.length],"string",m),a.makeTensorInfo(c.shape,"int32",f)]}var nte={kernelName:Wu,backendName:"webgl",kernelFunc:ate};function rte(e){let{inputs:t,backend:a,attrs:n}=e,{skipEmpty:r}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=a.readSync(s.dataId),l=a.readSync(i.dataId)[0],[u,p,c]=xj(o,l,r),d=p.length;return[a.makeTensorInfo([d,2],"int32",u),a.makeTensorInfo([d],"string",p),a.makeTensorInfo([2],"int32",new Int32Array(c))]}var ste={kernelName:Np,backendName:"webgl",kernelFunc:rte};function ite(e){let{inputs:t,backend:a,attrs:n}=e,{numBuckets:r}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=a.readSync(s.dataId),o=Aj(i,r);return a.makeTensorInfo(s.shape,"int32",o)}var ote={kernelName:Rp,backendName:"webgl",kernelFunc:ite},lte="return tan(x);",ute=tt({opSnippet:lte}),dte={kernelName:Yo,backendName:"webgl",kernelFunc:ute},pte=` + `}};function ete(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Nt.sliceInfo(r.shape,s,i,o,l,u,p,c,d),w;if(f)w=pe({inputs:{x:r},backend:a,attrs:{shape:m}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=Nt.computeOutShape(x,A,b),N=Fu({inputs:{x:r},backend:a,attrs:{begin:x,size:T}});w=pe({inputs:{x:N},backend:a,attrs:{shape:m}}),a.disposeIntermediateTensorInfo(N)}else if(a.shouldExecuteOnCPU([r])){let T=a.readSync(r.dataId),N=_e(r.shape,r.dtype,T),M=gj(h,N,b,x);w=a.makeTensorInfo(m,r.dtype,M.values)}else{let T=new Qee(x,b,h);w=a.runWebGLProgram(T,[r],r.dtype)}let I=pe({inputs:{x:w},backend:a,attrs:{shape:m}});return a.disposeIntermediateTensorInfo(w),I}var tte={kernelName:Po,backendName:"webgl",kernelFunc:ete};function ate(e){let{inputs:t,backend:a,attrs:n}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:p,dataSplits:c}=t,d=a.readSync(p.dataId),h=a.readSync(c.dataId),[m,f]=yj(d,h,r,s,i,o,l,u);return[a.makeTensorInfo([m.length],"string",m),a.makeTensorInfo(c.shape,"int32",f)]}var nte={kernelName:vu,backendName:"webgl",kernelFunc:ate};function rte(e){let{inputs:t,backend:a,attrs:n}=e,{skipEmpty:r}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=a.readSync(s.dataId),l=a.readSync(i.dataId)[0],[u,p,c]=xj(o,l,r),d=p.length;return[a.makeTensorInfo([d,2],"int32",u),a.makeTensorInfo([d],"string",p),a.makeTensorInfo([2],"int32",new Int32Array(c))]}var ste={kernelName:lp,backendName:"webgl",kernelFunc:rte};function ite(e){let{inputs:t,backend:a,attrs:n}=e,{numBuckets:r}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=a.readSync(s.dataId),o=Aj(i,r);return a.makeTensorInfo(s.shape,"int32",o)}var ote={kernelName:up,backendName:"webgl",kernelFunc:ite},lte="return tan(x);",ute=tt({opSnippet:lte}),dte={kernelName:"Tan",backendName:"webgl",kernelFunc:ute},pte=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); -`,cte=tt({opSnippet:pte}),hte={kernelName:Zo,backendName:"webgl",kernelFunc:cte};function mte(e){let{inputs:t,backend:a,attrs:n}=e,{tensor:r,indices:s,updates:i}=t,{}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=C.calculateShapes(i,s,r.shape),d=[c/u,u];if(c===0)return a.makeTensorInfo(r.shape,s.dtype);let h=pe({inputs:{x:s},backend:a,attrs:{shape:[l,o]}}),m=pe({inputs:{x:i},backend:a,attrs:{shape:[l,u]}}),f=pe({inputs:{x:r},backend:a,attrs:{shape:d}}),g=new B3(l,o,h.shape.length,m.shape.length,p,d,!1,!0),y=a.runWebGLProgram(g,[m,h,f],f.dtype),x=pe({inputs:{x:y},backend:a,attrs:{shape:r.shape}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(y),x}var fte={kernelName:Fo,backendName:"webgl",kernelFunc:mte},gte=class{constructor(e,t){this.variableNames=["A"];let a=new Array(e.length);for(let s=0;s5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let r=0;r5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=_e(r.shape,r.dtype,l),p=vj(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new gte(r.shape,s);return a.runWebGLProgram(i,[r],r.dtype)}var xte={kernelName:ds,backendName:"webgl",kernelFunc:q8},Ate=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` + `}};function yte(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let a=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let r=0;r5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=_e(r.shape,r.dtype,l),p=vj(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new gte(r.shape,s);return a.runWebGLProgram(i,[r],r.dtype)}var xte={kernelName:us,backendName:"webgl",kernelFunc:T8},Ate=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4774,7 +4774,7 @@ return a / b;`,NJ=` setOutput(x0 >= x1 ? float(i0) : float(i1)); } - `}};function zs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function X5(e){let t=1;for(;tl){let M=a.readSync(r.dataId),[$,E]=wj(M,u,r.dtype,s,i);return[a.makeTensorInfo($.shape,$.dtype,$.values),a.makeTensorInfo(E.shape,E.dtype,E.values)]}if(s===0)return u[u.length-1]=0,[a.makeTensorInfo(u,r.dtype,[]),a.makeTensorInfo(u,"int32",[])];if(p===1)return[r,ec({attrs:{shape:u,dtype:"int32",value:0},backend:a})];let c=a.texData.get(r.dataId),d=c!==null&&c.isPacked,h=d?a.unpackTensor(r):r,m=v.sizeFromShape(u)/p,f=pe({inputs:{x:h},attrs:{shape:[m,p]},backend:a});d&&zs(a,h);let g=X5(s),y=X5(p),x=null,A=()=>x===null?[f,f]:[f,x],b=(M,$,E)=>{let S=A(),_=new Ate(E),O=[[p],[x===null?1:0],[Number.NEGATIVE_INFINITY],[M],[$]],W=x;x=a.runWebGLProgram(_,S,"int32",O),zs(a,W)};for(let M=1;M=1;E/=2)b($,E,[m,y])}for(let M=y;M>g;M/=2){let $=A(),E=new bte([m,M/2]),S=[[p],[x===null?1:0],[g]],_=x;x=a.runWebGLProgram(E,$,"int32",S),zs(a,_);let O=g/2,W=O*2;for(let P=O;P>=1;P/=2)b(W,P,x.shape)}let w=x;x=ed({inputs:{x},backend:a,attrs:{begin:0,size:[m,s]}}),zs(a,w);let I=L8({inputs:{x:f,indices:x},backend:a,attrs:{axis:1,batchDims:1}});zs(a,f);let T=u.slice(0,-1);T.push(s),w=x,x=pe({inputs:{x},attrs:{shape:T},backend:a}),zs(a,w);let N=I;return I=pe({inputs:{x:I},attrs:{shape:T},backend:a}),zs(a,N),[I,x]}var wte={kernelName:Jo,backendName:"webgl",kernelFunc:vte},kte=class{constructor(e,t,a,n,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=a==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` + `}};function Os(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function N5(e){let t=1;for(;tl){let M=a.readSync(r.dataId),[$,E]=wj(M,u,r.dtype,s,i);return[a.makeTensorInfo($.shape,$.dtype,$.values),a.makeTensorInfo(E.shape,E.dtype,E.values)]}if(s===0)return u[u.length-1]=0,[a.makeTensorInfo(u,r.dtype,[]),a.makeTensorInfo(u,"int32",[])];if(p===1)return[r,_p({attrs:{shape:u,dtype:"int32",value:0},backend:a})];let c=a.texData.get(r.dataId),d=c!==null&&c.isPacked,h=d?a.unpackTensor(r):r,m=v.sizeFromShape(u)/p,f=pe({inputs:{x:h},attrs:{shape:[m,p]},backend:a});d&&Os(a,h);let g=N5(s),y=N5(p),x=null,A=()=>x===null?[f,f]:[f,x],b=(M,$,E)=>{let S=A(),_=new Ate(E),O=[[p],[x===null?1:0],[Number.NEGATIVE_INFINITY],[M],[$]],W=x;x=a.runWebGLProgram(_,S,"int32",O),Os(a,W)};for(let M=1;M=1;E/=2)b($,E,[m,y])}for(let M=y;M>g;M/=2){let $=A(),E=new bte([m,M/2]),S=[[p],[x===null?1:0],[g]],_=x;x=a.runWebGLProgram(E,$,"int32",S),Os(a,_);let O=g/2,W=O*2;for(let P=O;P>=1;P/=2)b(W,P,x.shape)}let w=x;x=Fu({inputs:{x},backend:a,attrs:{begin:0,size:[m,s]}}),Os(a,w);let I=A8({inputs:{x:f,indices:x},backend:a,attrs:{axis:1,batchDims:1}});Os(a,f);let T=u.slice(0,-1);T.push(s),w=x,x=pe({inputs:{x},attrs:{shape:T},backend:a}),Os(a,w);let N=I;return I=pe({inputs:{x:I},attrs:{shape:T},backend:a}),Os(a,N),[I,x]}var wte={kernelName:Fo,backendName:"webgl",kernelFunc:vte},kte=class{constructor(e,t,a,n,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=a==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` float mapCoord(float outCoord, float len) 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f=0;fa.disposeIntermediateTensorInfo(f)),m}var Rte={kernelName:Bu,backendName:"webgl",kernelFunc:Nte},Ete=class{constructor(e,t){this.variableNames=["x","segmentIds"];let a=e.windowSize,n=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/a);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(a/4)*4,p=a%4,c=` + `}};function Ite(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[p,c,d,h]=r.shape,[m,f]=u!=null?u:[c,d],g=[p,m,f,h],y=new kte(c,d,i,o,l,g);return a.runWebGLProgram(y,[r,s],"float32")}var Ste={kernelName:Do,backendName:"webgl",kernelFunc:Ite};function Cte(e){let{inputs:t,attrs:a,backend:n}=e,{axis:r}=a,{x:s}=t;Nu(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=kj(i,r,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var Tte={kernelName:dp,backendName:"webgl",kernelFunc:Cte};function Nte(e){let{inputs:t,backend:a,attrs:n}=e,{value:r}=t,{axis:s}=n;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let f=0;fa.disposeIntermediateTensorInfo(f)),m}var Rte={kernelName:wu,backendName:"webgl",kernelFunc:Nte},Ete=class{constructor(e,t){this.variableNames=["x","segmentIds"];let a=e.windowSize,n=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/a);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(a/4)*4,p=a%4,c=` sumValue += dot(values, segFilter); `,d="";r%a>0&&(d=` if (inIdx < 0 || inIdx >= ${r}) { @@ -4992,9 +4992,9 @@ return a / b;`,NJ=` } setOutput(${l}); } - `}};function Mte(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,segmentIds:s}=t,{numSegments:i}=n,o=r.shape.length,l=[],u=0,p=C.getAxesPermutation([u],o),c=r;p!=null&&(c=Ca({inputs:{x:r},backend:a,attrs:{perm:p}}),l.push(c),u=C.getInnerMostAxes(1,o)[0]);let d=C.segment_util.computeOutShape(c.shape,u,i),h=v.sizeFromShape([c.shape[u]]),m=pe({inputs:{x:c},backend:a,attrs:{shape:[-1,h]}});l.push(m);let f=_p(r.dtype),g=(b,w,I,T,N)=>{let M=b.shape[0],$=b.shape[1],E=C.segment_util.segOpComputeOptimalWindowSize($,N),S={windowSize:E,inSize:$,batchSize:M,numSegments:N},_=new Ete(S,w),O=a.compileAndRun(_,[b,I],T);if(l.push(O),O.shape[1]===N)return O;let W=j8({backend:a,attrs:{start:0,stop:N,step:1,dtype:"float32"}}),P=q8({inputs:{x:W},backend:a,attrs:{reps:[$/E]}});return l.push(W),l.push(P),g(O,w,P,T,N)},y=g(m,"unsortedSegmentSum",s,f,i),x=pe({inputs:{x:y},backend:a,attrs:{shape:d}}),A=x;if(p!=null){l.push(x);let b=C.getUndoAxesPermutation(p);A=Ca({inputs:{x:A},backend:a,attrs:{perm:b}})}return l.forEach(b=>a.disposeIntermediateTensorInfo(b)),A}var $te={kernelName:Mp,backendName:"webgl",kernelFunc:Mte},Pte=[gq,xq,vq,Iq,Cq,Rq,Mq,Pq,Oq,Lq,Vq,Hq,Xq,Jq,tX,nX,sX,uX,pX,hX,yX,IX,CX,EX,$X,zX,WX,GX,Qj,qX,JX,aK,lK,pK,hK,fK,yK,vK,IK,TK,RK,MK,PK,DK,zK,VK,GK,qK,YK,JK,aY,iY,dY,hY,gY,yY,AY,vY,kY,SY,TY,MY,_Y,OY,LY,VY,HY,KY,QY,Jj,tZ,YX,rZ,oZ,dZ,tq,mZ,xZ,bZ,IZ,TZ,MZ,_Z,zZ,VZ,HZ,qZ,ZZ,QZ,tJ,sJ,oJ,uJ,pJ,hJ,yJ,vJ,SJ,PJ,rq,OJ,WJ,UJ,jJ,_X,KJ,ZJ,QJ,aQ,iQ,nq,lQ,dQ,cQ,mQ,fQ,FX,RJ,xQ,wQ,CQ,iq,EQ,PQ,OQ,WQ,GQ,jQ,KQ,JQ,tee,ree,oee,dee,mee,yee,vee,Iee,wX,MJ,Tee,Ree,Mee,Pee,Fee,Oee,Lee,Bee,Uee,jee,Xee,Yee,Jee,tte,nte,ste,ote,EJ,hq,dte,hte,fte,xte,wte,Ste,mq,Tte,Rte,$te,YJ];for(let e of Pte)xn(e);var nt;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(nt||(nt={}));var rp;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(rp||(rp={}));var X8;function _te(e){X8=e.wasm.cwrap(Zr,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Fte(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n,d=a.dataIdMap.get(r.dataId).id,h=a.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let N=a.dataIdMap.get(i.dataId);if(N.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${N.shape.length}.`);m=N.id}let f=o==null?0:a.dataIdMap.get(o.dataId).id,g=rp[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],x=u?s.shape[1]:s.shape[2],A=nl.assertAndGetBroadcastShape(r.shape.slice(0,-2),s.shape.slice(0,-2)),b=a.makeOutput([...A,y,x],r.dtype),w=a.dataIdMap.get(b.dataId).id,I=new Uint8Array(new Int32Array(r.shape).buffer),T=new Uint8Array(new Int32Array(s.shape).buffer);return X8(d,I,r.shape.length,h,T,s.shape.length,l,u,g,m,f,c||0,w),b}var Dte={kernelName:Zr,backendName:"wasm",setupFunc:_te,kernelFunc:Fte};function Qe(e,t){let a;function n(s){a=s.wasm.cwrap(e,null,["number","number","number"])}function r(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),p=i.dataIdMap.get(u.dataId).id;return v.sizeFromShape(u.shape)===0||a(l,nt[o.dtype],p),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:r}}var Ote=Qe(ou),zte=Qe(oi),Lte=Qe(li);function Gt(e,t,a){let n;function r(i){n=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:p}=l,c=o.dataIdMap.get(u.dataId).id,d=o.dataIdMap.get(p.dataId).id,h=a!=null?a:u.dtype,m=C.assertAndGetBroadcastShape(u.shape,p.shape),f=o.makeOutput(m,h);if(v.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(p.shape).buffer),x=o.dataIdMap.get(f.dataId).id;return n(c,g,u.shape.length,d,y,p.shape.length,nt[u.dtype],x),f}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var Wte=!0,Bte=Gt(ls,Wte),K8;function Vte(e){K8=e.wasm.cwrap(ui,null,["array","number","number","number"])}function Ute(e){let{inputs:t,backend:a}=e,n=a.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(n.shape)===0)return n;let r=t.map(o=>a.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=a.dataIdMap.get(n.dataId).id;return K8(s,r.length,nt[n.dtype],i),n}var Gte={kernelName:ui,backendName:"wasm",setupFunc:Vte,kernelFunc:Ute};function l0(e){let{inputs:{x:t},backend:a}=e;if(t.dtype==="string")return Ve(a.readSync(t.dataId),t.shape,t.dtype);let n=a.makeOutput(t.shape,t.dtype),r=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(n).set(r),n}var Hte={kernelName:qi,backendName:"wasm",kernelFunc:l0},Y8;function jte(e){Y8=e.wasm.cwrap(Ir,null,["number","array","number","number","number","array","number"])}function os(e){let{inputs:t,backend:a,attrs:n}=e,[r,s]=Xte(t.x.shape,n.perm),i=!0;for(let m=0;m=r&&(s===-1||n[s]>n[i])&&(s=i);n[s]=r}return[a,n]}var Kte={kernelName:Ir,backendName:"wasm",kernelFunc:os,setupFunc:jte};function gs(e,t,a){let n=e.shape,r=e.shape.length,s=v.parseAxisParam(t,n),i=s,o=C.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let p=new Array(r);for(let d=0;d`new shape: ${i}, old shape: ${n.shape}. 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t.dtype==="string"?c.stringBytes=l.slice(m,m+v.sizeFromShape(i)):r.typedArrayFromHeap(u).set(l.subarray(m,m+v.sizeFromShape(i))),u}if(t.dtype==="string"){let m=Ah(l,s,i,t.shape,t.dtype);return c.stringBytes=m,u}let d=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)Sae(l,p[0],d,s,i);else if(h===3)Cae(l,p[0],p[1],d,s,i);else if(h===4)Tae(l,p[0],p[1],p[2],d,s,i);else{let m=Ah(l,s,i,t.shape,t.dtype);d.set(m)}return u}function Sae(e,t,a,n,r){let s=0,i=n[0],o=n[1],l=i+r[0];for(let u=i;uy*x),l=C.getReshaped(r.shape,s,o),u=C.getPermuted(l.length,s.length),p=C.getReshapedPermuted(r.shape,s,o),c=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(p,i,s.length),h=La({inputs:{x:r},backend:a,attrs:{shape:l}}),m=os({inputs:{x:h},backend:a,attrs:{perm:u}}),f=La({inputs:{x:m},backend:a,attrs:{shape:p}}),g=si({inputs:{x:f},backend:a,attrs:{begin:c,size:d}});return a.disposeData(h.dataId),a.disposeData(m.dataId),a.disposeData(f.dataId),g}var Eae={kernelName:pu,backendName:"wasm",kernelFunc:Rae},sw;function Mae(e){sw=e.wasm.cwrap(Ai,null,["number","number","boolean","number","number","number"])}function $ae(e){let{backend:t,inputs:a,attrs:n}=e,{x:r,weights:s}=a,{size:i}=n,o=s.shape.reduce((c,d)=>c*d,1)!==0,l=r.shape.length===1?[i]:[r.shape[0],i],u=t.makeOutput(l,s.dtype);function p(c){return t.dataIdMap.get(c.dataId).id}return sw(p(r),i,o,p(s),nt[s.dtype],p(u)),u}var Pae={kernelName:Ai,backendName:"wasm",setupFunc:Mae,kernelFunc:$ae},_ae=!0,Fae=Gt(cu,_ae);function Dae(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.typedArrayFromHeap(n),i=a.typedArrayFromHeap(r),o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeOutput([o.length],"int32",void 0,new Int32Array(o))}var Oae={kernelName:hu,backendName:"wasm",kernelFunc:Dae};function ys(e){let{inputs:{x:t},attrs:{dtype:a},backend:n}=e,r=n.makeOutput(t.shape,a),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(s),r}var zae={kernelName:bi,backendName:"wasm",kernelFunc:ys},Lae=Qe(vi),iw;function Wae(e){iw=e.wasm.cwrap(us,null,["number","number","number","number"])}function Bae(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o=a.dataIdMap.get(r.dataId).id,l=a.makeOutput(r.shape,r.dtype),u=a.dataIdMap.get(l.dataId).id;return iw(o,s,i,u),l}var Vae={kernelName:us,backendName:"wasm",setupFunc:Wae,kernelFunc:Bae};function ow(e){let{inputs:t,backend:a}=e,n=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=t.map(h=>h.shape);C.assertParamsConsistent(r,n);let s=C.computeOutShape(t.map(h=>h.shape),n),i=t.filter(h=>v.sizeFromShape(h.shape)>0);if(i.length===1)return l0({inputs:{x:i[0]},backend:a});let o=a.makeOutput(s,t[0].dtype);if(v.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(A=>{let b=[-1,v.sizeFromShape(A.shape.slice(n))];return La({inputs:{x:A},backend:a,attrs:{shape:b}})}),m=h.map(A=>({vals:a.readSync(A.dataId),shape:A.shape}));s=C.computeOutShape(h.map(A=>A.shape),1);let f=h[0].shape[0]===1,g=m3(m,s,t[0].dtype,f),y=C.computeOutShape(i.map(A=>A.shape),n);o.shape=y;let x=a.dataIdMap.get(o.dataId);return x.stringBytes=C.fromStringArrayToUint8(g),h.forEach(A=>a.disposeData(A.dataId)),o}let l=v.sizeFromShape(i[0].shape.slice(0,n)),u=0,p=i.map(h=>{let m=v.sizeFromShape(h.shape.slice(n));return u+=m,m}),c=i.map(h=>a.typedArrayFromHeap(h)),d=a.typedArrayFromHeap(o);for(let h=0;h`cumprod does not support ${r.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],l),p=r;u!==null&&(p=os({inputs:{x:r},attrs:{perm:u},backend:a}));let c=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumprod",[c],l);let d=a.makeOutput(p.shape,p.dtype),h=p.shape[c],m=a.dataIdMap.get(p.dataId).id,f=a.dataIdMap.get(d.dataId).id;mw(m,i?1:0,o?1:0,h,f,nt[r.dtype]);let g=d;if(u!==null){let y=C.getUndoAxesPermutation(u);g=os({inputs:{x:d},attrs:{perm:y},backend:a}),a.disposeData(p.dataId),a.disposeData(d.dataId)}return g}var cne={kernelName:Ni,backendName:"wasm",setupFunc:dne,kernelFunc:pne},fw;function hne(e){fw=e.wasm.cwrap(Ri,null,["number","number","number","number","number","number"])}function mne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],l),p=r;u!==null&&(p=os({inputs:{x:r},attrs:{perm:u},backend:a}));let c=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumsum",[c],l);let d=a.makeOutput(p.shape,p.dtype),h=p.shape[c],m=a.dataIdMap.get(p.dataId).id,f=a.dataIdMap.get(d.dataId).id;fw(m,i?1:0,o?1:0,h,f,nt[r.dtype]);let g=d;if(u!==null){let y=C.getUndoAxesPermutation(u);g=os({inputs:{x:d},attrs:{perm:y},backend:a}),a.disposeData(p.dataId),a.disposeData(d.dataId)}return g}var fne={kernelName:Ri,backendName:"wasm",setupFunc:hne,kernelFunc:mne},gw;function gne(e){gw=e.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function yne(e){let{backend:t,inputs:a,attrs:n}=e,{x:r,weights:s}=a,{size:i,binaryOutput:o}=n,l=s.shape.reduce((d,h)=>d*h,1)!==0,u=r.shape.length===1?[i]:[r.shape[0],i],p=t.makeOutput(u,s.dtype);function c(d){return t.dataIdMap.get(d.dataId).id}return gw(c(r),new Uint8Array(new Int32Array(r.shape).buffer),r.shape.length,i,l,c(s),nt[s.dtype],o,c(p)),p}var xne={kernelName:gu,backendName:"wasm",setupFunc:gne,kernelFunc:yne},yw;function Ane(e){yw=e.wasm.cwrap(Mi,null,["number","number","number","array","number","array","array","number","number"])}function bne(e){let{backend:t,inputs:a,attrs:n}=e,{x:r}=a,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),m=i==="NHWC"?[o,c,d,h]:[o,h,c,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(m).buffer),A=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),b=t.dataIdMap.get(f.dataId).id;return yw(g,s,i==="NHWC"?1:0,y,r.shape.length-1,x,A,m.length,b),f}var vne={kernelName:Mi,backendName:"wasm",setupFunc:Ane,kernelFunc:bne},xw;function wne(e){xw=e.wasm.cwrap($i,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kne(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:c}=a,d=u==null?[1,1]:u,h=C.computeConv2DInfo(r.shape,s.shape,l,d,p,c,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,x=h.padInfo.bottom,A=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,I=h.strideHeight,T=h.strideWidth,N=h.inChannels,M=h.outChannels,$=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let E=n.makeOutput(h.outShape,"float32"),S=n.dataIdMap.get(E.dataId).id;return xw(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,x,A,$,b,w,I,T,N,M,S),E}var Ine={kernelName:$i,backendName:"wasm",setupFunc:wne,kernelFunc:kne},Aw;function Sne(e){Aw=e.wasm.cwrap("Diag",null,["number","number","number","number"])}function Cne(e){let{inputs:t,backend:a}=e,{x:n}=t,r=v.sizeFromShape(n.shape),s=a.makeOutput([...n.shape,...n.shape],n.dtype);return Aw(a.dataIdMap.get(n.dataId).id,nt[n.dtype],r,a.dataIdMap.get(s.dataId).id),s}var Tne={kernelName:yu,backendName:"wasm",setupFunc:Sne,kernelFunc:Cne},bw;function Nne(e){bw=e.wasm.cwrap(Pi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Rne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n;if(r.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${r.dtype} and ${s.dtype}`);let u=C.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=a.makeOutput(u.outShape,r.dtype);return bw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(p.dataId).id,nt[r.dtype],u.batchSize,u.inChannels,u.inHeight,u.inWidth,u.outHeight,u.outWidth,u.strideHeight,u.strideWidth,u.dilationHeight,u.dilationWidth,u.filterHeight,u.filterWidth,u.padInfo.top,u.padInfo.left),p}var Ene={kernelName:Pi,backendName:"wasm",setupFunc:Nne,kernelFunc:Rne},vw;function Mne(e){vw=e.wasm.cwrap(Xl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function $ne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=C.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=a.makeOutput(s.shape,s.dtype);return vw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,nt[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),c}var Pne={kernelName:Xl,backendName:"wasm",setupFunc:Mne,kernelFunc:$ne},ww;function _ne(e){ww=e.wasm.cwrap(ql,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Fne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=C.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=a.makeOutput(r.shape,r.dtype);return ww(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,nt[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),c}var Dne={kernelName:ql,backendName:"wasm",setupFunc:_ne,kernelFunc:Fne},One=Qe(Fi),kw;function zne(e){kw=e.wasm.cwrap(xu,null,["number","number","number"])}function Lne(e){let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=a.makeOutput(r.shape,"float32"),i=o=>a.dataIdMap.get(o.dataId).id;return kw(i(r),i(n),i(s)),s}var Wne={kernelName:xu,backendName:"wasm",setupFunc:zne,kernelFunc:Lne},Bne=!1,Vne=Gt(Oi,Bne,"bool"),Une=Qe(Di),Gne=Qe(zi,"float32");function U1(e){let{inputs:t,attrs:a,backend:n}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),La({inputs:{x:r},backend:n,attrs:{shape:o}})}var Hne={kernelName:Au,backendName:"wasm",kernelFunc:U1},jne=Qe(Li,"float32");function Iw(e){let{attrs:{shape:t,value:a},backend:n}=e,{attrs:{dtype:r}}=e;r=r||v.inferDtype(a);let s=n.makeOutput(t,r);return n.typedArrayFromHeap(s).fill(a),s}var qne={kernelName:bu,backendName:"wasm",kernelFunc:Iw},Sw;function Xne(e){Sw=e.wasm.cwrap(Wi,null,["number","number","number","number","number","number"])}function Kne(e){let{inputs:t,backend:a}=e,{image:n}=t,r=a.makeOutput(n.shape,n.dtype),s=a.dataIdMap.get(n.dataId).id,i=a.dataIdMap.get(r.dataId).id,[o,l,u,p]=n.shape;return Sw(s,o,l,u,p,i),r}var Yne={kernelName:Wi,backendName:"wasm",kernelFunc:Kne,setupFunc:Xne},Zne=Qe(Bi),Jne=!1,Qne=Gt(Vi,Jne),Cw;function ere(e){Cw=e.wasm.cwrap(Ui,null,["number","number","number","number","number","number","number"])}function tre(e){let{backend:t,inputs:a,attrs:n}=e,{varianceEpsilon:r}=n,{x:s,mean:i,variance:o,offset:l,scale:u}=a,p=t.dataIdMap.get(s.dataId).id,c=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return Cw(p,c,d,h,m,r,g),f}var are={kernelName:Ui,backendName:"wasm",setupFunc:ere,kernelFunc:tre},Tw;function nre(e){Tw=e.wasm.cwrap(Jr,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function rre(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:c,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=C.computeConv2DInfo(r.shape,s.shape,l,p,u,d),g=rp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=n.dataIdMap.get(r.dataId).id,x=n.dataIdMap.get(s.dataId).id,A=f.outChannels,b=0;if(i!=null){let X=n.dataIdMap.get(i.dataId);if(X.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${X.shape.length}.`);if(X.shape[0]!==A)throw new Error(`FusedConv2D bias shape (${X.shape}) does not match the number of output channels (${A})`);b=X.id}let w=f.filterHeight,I=f.filterWidth,T=f.padInfo.top,N=f.padInfo.right,M=f.padInfo.bottom,$=f.padInfo.left,E=f.dilationHeight,S=f.dilationWidth,_=f.strideHeight,O=f.strideWidth,W=f.inChannels,P=f.padInfo.type==="SAME"?1:0,U=f.batchSize,G=f.inHeight,q=f.inWidth;if(c!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${c}'. Please use 'NHWC'.`);let H=n.makeOutput(f.outShape,"float32"),V=n.dataIdMap.get(H.dataId).id,Z=o==null?0:n.dataIdMap.get(o.dataId).id;return Tw(y,U,G,q,x,w,I,b,T,N,M,$,P,E,S,_,O,W,A,g,Z,m||0,V),H}var sre={kernelName:Jr,backendName:"wasm",setupFunc:nre,kernelFunc:rre},Nw;function ire(e){Nw=e.wasm.cwrap(Qr,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ore(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:c,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=C.computeConv2DInfo(r.shape,s.shape,l,p,u,d,!0),g=rp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=n.dataIdMap.get(r.dataId).id,x=n.dataIdMap.get(s.dataId).id,A=f.outChannels,b=0;if(i!=null){let X=n.dataIdMap.get(i.dataId);if(X.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${X.shape.length}.`);if(X.shape[0]!==A)throw new Error(`FusedDepthwiseConv2D bias shape (${X.shape}) does not match the number of output channels (${A})`);b=X.id}let w=f.filterHeight,I=f.filterWidth,T=f.padInfo.top,N=f.padInfo.right,M=f.padInfo.bottom,$=f.padInfo.left,E=f.dilationHeight,S=f.dilationWidth,_=f.strideHeight,O=f.strideWidth,W=f.inChannels,P=f.padInfo.type==="SAME"?1:0,U=f.batchSize,G=f.inHeight,q=f.inWidth;if(c!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${c}'. Please use 'NHWC'.`);let H=n.makeOutput(f.outShape,"float32"),V=n.dataIdMap.get(H.dataId).id,Z=o==null?0:n.dataIdMap.get(o.dataId).id;return Nw(y,U,G,q,x,w,I,b,T,N,M,$,P,E,S,_,O,W,A,g,Z,m||0,V),H}var lre={kernelName:Qr,backendName:"wasm",setupFunc:ire,kernelFunc:ore},Rw;function ure(e){Rw=e.wasm.cwrap(Gi,null,["number","number","number","number","number","number","array","number"])}function dre(e){let{backend:t,inputs:a}=e,{params:n,indices:r}=a,[s,i,o,l]=s3.prepareAndValidate(n,r),u=t.makeOutput(s,n.dtype);if(i===0)return u;let p=r.shape,c=p[p.length-1],d=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return Rw(d,nt[n.dtype],h,i,c,o,m,f),u}var pre={kernelName:Gi,backendName:"wasm",setupFunc:ure,kernelFunc:dre},Ew;function cre(e){Ew=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function hre(e){let{backend:t,inputs:a,attrs:n}=e,{x:r,indices:s}=a,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let T=0;T=0,()=>`GatherV2: the index value ${N} is not in [0, ${p-1}]`)}let c=C.segment_util.collectGatherOpShapeInfo(r,s,l,o),d=La({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),m=La({inputs:{x:s},attrs:{shape:[c.batchSize,h/c.batchSize]},backend:t}),f=[c.batchSize,c.outerSize,h/c.batchSize,c.sliceSize],g=t.makeOutput(f,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let y=d.shape.length-1,x=t.dataIdMap.get(d.dataId).id,A=t.dataIdMap.get(m.dataId).id,b=t.dataIdMap.get(g.dataId).id,w=new Uint8Array(new Int32Array(v.computeStrides(d.shape)).buffer),I=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer);return Ew(x,nt[r.dtype],w,y,A,c.batchSize,I,b),t.disposeData(d.dataId),t.disposeData(m.dataId),g.shape=c.outputShape,g}var mre={kernelName:vu,backendName:"wasm",setupFunc:cre,kernelFunc:hre},fre=!1,gre=Gt(Hi,fre,"bool"),yre=!1,xre=Gt(ji,yre,"bool"),Are=Qe(Xi,"bool"),bre=Qe(Ki,"bool"),vre=Qe(Yi,"bool"),Mw;function wre(e){Mw=e.wasm.cwrap(Zi,null,["number","number","number","number"])}function kre(e){let{inputs:{x:t},attrs:{alpha:a},backend:n}=e,r=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;Mw(r,nt[t.dtype],a,i)}return s}var Ire={kernelName:Zi,backendName:"wasm",setupFunc:wre,kernelFunc:kre},Sre=!1,Cre=Gt(Ji,Sre,"bool"),Tre=!1,Nre=Gt(Qi,Tre,"bool"),$w;function Rre(e){$w=e.wasm.cwrap(eo,null,["number","number","number","number"])}function Ere(e){let{attrs:t,backend:a}=e,{start:n,stop:r,num:s}=t,i=Math.floor(s),o=a.makeOutput([i],"float32");return $w(a.dataIdMap.get(o.dataId).id,n,r,i),o}var Mre={kernelName:eo,backendName:"wasm",setupFunc:Rre,kernelFunc:Ere},$re=Qe(to),Pre=Qe(ao),_re=!1,Fre=Gt(no,_re,"bool"),Dre=Qe(ro),Ore=!1,zre=Gt(so,Ore,"bool"),Lre=!1,Wre=Gt(RA,Lre,"bool"),Pw;function Bre(e){Pw=e.wasm.cwrap(io,null,["number","number","number","number","number","number","number"])}function Vre(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=a.makeOutput(r.shape,r.dtype);return Pw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var Ure={kernelName:io,backendName:"wasm",setupFunc:Bre,kernelFunc:Vre},_w;function Gre(e){_w=e.wasm.cwrap(wu,null,["number","number","number","number","number","number","number","number","number"])}function Hre(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let c=a.makeOutput(r.shape,r.dtype);return _w(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,i.shape[3],o,l,u,p),c}var jre={kernelName:wu,backendName:"wasm",setupFunc:Gre,kernelFunc:Hre},Fw;function qre(e){Fw=e.wasm.cwrap(oo,null,["number","number","number","number"])}function Xre(e){let{backend:t,inputs:a,attrs:n}=e,{reductionIndices:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:c,inputWasTransposed:d}=gs(i,r,t);if(d){let x=t.dataIdMap.get(u.dataId).id;l=u,o=x}let h=l.shape.length;C.assertAxesAreInnerMostDims("max",p,h);let[m,f]=C.computeOutAndReduceShapes(l.shape,p),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;Fw(o,nt[i.dtype],g,x)}if(d&&t.disposeData(u.dataId),s){let x=C.expandShapeToKeepDim(y.shape,c);y.shape=x}return y}var Kre={kernelName:oo,backendName:"wasm",setupFunc:qre,kernelFunc:Xre},Yre=!1,Zre=Gt(lo,Yre),Dw;function Jre(e){Dw=e.wasm.cwrap(uo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qre(e){let{inputs:t,attrs:a,backend:n}=e,r=t.x,s=n.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a,p=C.computePool2DInfo(r.shape,i,o,1,l,u),c=p.filterHeight,d=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,y=p.dilationHeight,x=p.dilationWidth,A=p.strideHeight,b=p.strideWidth,w=p.inChannels,I=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let T=n.makeOutput(p.outShape,"float32"),N=n.dataIdMap.get(T.dataId).id;return Dw(s,r.shape[0],r.shape[1],r.shape[2],c,d,h,m,f,g,y,x,A,b,w,I,N),T}var ese={kernelName:uo,backendName:"wasm",setupFunc:Jre,kernelFunc:Qre},Ow;function tse(e){Ow=e.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ase(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=C.computePool3DInfo(r.shape,s,i,1,o,l,u),c=a.makeOutput(p.outShape,r.dtype);return Ow(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),c}var nse={kernelName:ku,backendName:"wasm",setupFunc:tse,kernelFunc:ase},zw;function rse(e){zw=e.wasm.cwrap("MaxPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function sse(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=C.computePool3DInfo(s.shape,i,o,1,l,u),c=a.makeOutput(s.shape,s.dtype);return zw(a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),c}var ise={kernelName:kp,backendName:"wasm",setupFunc:rse,kernelFunc:sse},Lw;function ose(e){Lw=e.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function lse(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=C.computePool2DInfo(s.shape,i,o,1,l,u),c=a.makeOutput(s.shape,s.dtype);return Lw(a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),c}var use={kernelName:wp,backendName:"wasm",setupFunc:ose,kernelFunc:lse},Ww;function dse(e){Ww=e.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function pse(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,includeBatchInIndex:l}=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,[1,1],o),c=a.makeOutput(p.outShape,r.dtype),d=a.makeOutput(p.outShape,"int32");return Ww(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,a.dataIdMap.get(d.dataId).id,nt[r.dtype],l,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),[c,d]}var cse={kernelName:Iu,backendName:"wasm",setupFunc:dse,kernelFunc:pse},Bw;function hse(e){Bw=e.wasm.cwrap(po,null,["number, number, number"])}function mse(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=gs(i,r,t),m=c;if(h){let b=t.dataIdMap.get(p.dataId).id;b!==o&&(u=p,l=b,m=C.getInnerMostAxes(m.length,u.shape.length))}C.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=C.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),x=u;u.dtype!=="float32"&&(x=ys({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(x.dataId).id);let A=t.makeOutput(f,"float32");if(v.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(A.dataId).id;Bw(l,y,b)}if(h&&t.disposeData(p.dataId),s){let b=C.expandShapeToKeepDim(A.shape,d);A.shape=b}return u.dtype!=="float32"&&t.disposeData(x.dataId),A}var fse={kernelName:po,backendName:"wasm",setupFunc:hse,kernelFunc:mse},Vw;function gse(e){Vw=e.wasm.cwrap(co,null,["number","number","number","number"])}function yse(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=gs(i,r,t);if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A)}let m=u.shape.length;C.assertAxesAreInnerMostDims("min",c,m);let[f,g]=C.computeOutAndReduceShapes(u.shape,c),y=v.sizeFromShape(g),x=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;Vw(l,nt[i.dtype],y,A)}if(h&&t.disposeData(p.dataId),s){let A=C.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var xse={kernelName:co,backendName:"wasm",setupFunc:gse,kernelFunc:yse},Ase=!1,bse=Gt(ho,Ase),G1;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(G1||(G1={}));var Uw;function vse(e){Uw=e.wasm.cwrap(mo,null,["number","array","number","number","array","array","number","number"])}function wse(e){let{inputs:{x:t},backend:a,attrs:{paddings:n,mode:r}}=e,s=n.map((m,f)=>m[0]+t.shape[f]+m[1]),i=a.dataIdMap.get(t.dataId).id,o=a.makeOutput(s,t.dtype),l=a.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(m=>m[0]),c=n.map(m=>m[1]),d=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(c).buffer);return Uw(i,u,t.shape.length,nt[t.dtype],d,h,G1[r],l),o}var kse={kernelName:mo,backendName:"wasm",kernelFunc:wse,setupFunc:vse},Gw;function Ise(e){Gw=e.wasm.cwrap(Ho,null,["number","number","number","number"])}function Hw(e){let{backend:t,inputs:{logits:a},attrs:{dim:n}}=e,r=t.dataIdMap.get(a.dataId).id,s=t.makeOutput(a.shape,a.dtype),i=t.dataIdMap.get(s.dataId).id,o=a.shape[n],l=v.sizeFromShape(a.shape)/o;return v.sizeFromShape(s.shape)===0||Gw(r,i,o,l),s}var Sse={kernelName:Ho,backendName:"wasm",setupFunc:Ise,kernelFunc:Hw},jw;function Cse(e){jw=e.wasm.cwrap(go,null,["number","number","number","number","number","number"])}function Tse(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n;if(r.dtype!=="float32")throw new Error(`Tensor logits must have dtype float32, got ${r.dtype}`);let l=o?r:Hw({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),[u,p]=l.shape,c=a.makeOutput([u,s],"int32");return jw(a.dataIdMap.get(l.dataId).id,u,p,s,i,a.dataIdMap.get(c.dataId).id),o||a.disposeData(l.dataId),c}var Nse={kernelName:go,backendName:"wasm",setupFunc:Cse,kernelFunc:Tse},Rse=Gt(fo,!0),Ese=!0,Mse=Gt(yo,Ese),$se=Qe(Su);function V3(e,t){let a=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=a[0],r=a[1],s=a[2],i=a[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var qw;function Pse(e){qw=e.wasm.cwrap(Ao,"number",["number","number","number","number","number"])}function _se(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=a,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,c=qw(u,p,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=V3(t,c);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var Fse={kernelName:Ao,backendName:"wasm",setupFunc:Pse,kernelFunc:_se},Xw;function Dse(e){Xw=e.wasm.cwrap(Cu,"number",["number","number","number","number","number","bool"])}function Ose(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=Xw(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=V3(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),x=t.makeOutput([],"int32",g);return[y,x]}var zse={kernelName:Cu,backendName:"wasm",setupFunc:Dse,kernelFunc:Ose},Kw;function Lse(e){Kw=e.wasm.cwrap(bo,"number",["number","number","number","number","number","number"])}function Wse(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=Kw(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=V3(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),x=t.makeOutput([m],"float32",f);return[y,x]}var Bse={kernelName:bo,backendName:"wasm",setupFunc:Lse,kernelFunc:Wse},Vse=!1,Use=Gt(xo,Vse,"bool"),Yw;function Gse(e){Yw=e.wasm.cwrap(vo,null,["number","number","number","number","number"])}function Hse(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=a.makeOutput([...r.shape,i],s),p=a.dataIdMap.get(u.dataId).id,c=a.dataIdMap.get(r.dataId).id;return Yw(c,i,o,l,p),u}var jse={kernelName:vo,backendName:"wasm",setupFunc:Gse,kernelFunc:Hse};function qse(e){let{inputs:{x:t},backend:a}=e,n=a.makeOutput(t.shape,t.dtype);return a.typedArrayFromHeap(n).fill(1),n}var Xse={kernelName:Tu,backendName:"wasm",kernelFunc:qse};function Kse(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return U1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=U1({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=ow({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeData(p.dataId)),u}var Yse={kernelName:Nu,backendName:"wasm",kernelFunc:Kse},Zw;function Zse(e){Zw=e.wasm.cwrap(wo,null,["number","array","number","number","array","array","number","number"])}function Jse(e){let{inputs:{x:t},backend:a,attrs:{paddings:n,constantValue:r}}=e,s=n.map((m,f)=>m[0]+t.shape[f]+m[1]);if(v.sizeFromShape(t.shape)===0)return Iw({backend:a,attrs:{shape:s,value:r,dtype:t.dtype}});let i=a.dataIdMap.get(t.dataId).id,o=a.makeOutput(s,t.dtype),l=a.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(m=>m[0]),c=n.map(m=>m[1]),d=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(c).buffer);return Zw(i,u,t.shape.length,nt[t.dtype],d,h,r,l),o}var Jw={kernelName:wo,backendName:"wasm",kernelFunc:Jse,setupFunc:Zse},Qse=!1,eie=Gt(ko,Qse),Qw;function tie(e){Qw=e.wasm.cwrap(Io,null,["number","number","number"])}function aie(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=a.dataIdMap.get(n.dataId).id,i=a.dataIdMap.get(r.dataId).id,o=s,l=n,u=l;l.dtype!=="float32"&&(u=ys({backend:a,inputs:{x:n},attrs:{dtype:"float32"}}),o=a.dataIdMap.get(u.dataId).id);let p=a.makeOutput(n.shape,"float32"),c=a.dataIdMap.get(p.dataId).id;return Qw(o,i,c),l.dtype!=="float32"&&a.disposeData(u.dataId),p}var nie={kernelName:Io,backendName:"wasm",setupFunc:tie,kernelFunc:aie},ek;function rie(e){ek=e.wasm.cwrap(So,null,["number","number","number","number"])}function sie(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=gs(i,r,t),m=c;if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A,m=C.getInnerMostAxes(m.length,u.shape.length))}C.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=C.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),x=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;ek(l,y,nt[x.dtype],A)}if(h&&t.disposeData(p.dataId),s){let A=C.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var iie={kernelName:So,backendName:"wasm",setupFunc:rie,kernelFunc:sie},oie=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=y3(n,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},lie={kernelName:Ru,backendName:"wasm",kernelFunc:oie},uie=!0,die=Gt(_i,uie),pie=Qe(Co),cie=Qe(To),hie=Qe(Eo),tk;function mie(e){tk=e.wasm.cwrap(Ro,null,["number","number","number","number","number","number","number","number","number","number"])}function fie(e){let{backend:t,inputs:a,attrs:n}=e,{images:r}=a,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[p,c,d,h]=r.shape,m=[p,l,u,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=ys({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,x=t.makeOutput(m,"float32");if(v.sizeFromShape(r.shape)===0)return x;let A=t.dataIdMap.get(x.dataId).id;return tk(y,p,c,d,h,l,u,s?1:0,i?1:0,A),g!=null&&t.disposeData(g.dataId),x}var gie={kernelName:Ro,backendName:"wasm",setupFunc:mie,kernelFunc:fie},ak;function yie(e){ak=e.wasm.cwrap($u,null,["number","number","number","array","array","boolean"])}function xie(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=a.makeOutput(r.shape,"float32"),l=a.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=ys({backend:a,inputs:{x:r},attrs:{dtype:"float32"}}),l=a.dataIdMap.get(u.dataId)),ak(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(o.dataId).id,new Uint8Array(new Int32Array(r.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),i),u!=null&&a.disposeData(u.dataId),o}var Aie={kernelName:$u,backendName:"wasm",setupFunc:yie,kernelFunc:xie},nk;function bie(e){nk=e.wasm.cwrap(No,null,["number","number","number","number","number","number","number","number","number","number"])}function vie(e){let{backend:t,inputs:a,attrs:n}=e,{images:r}=a,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[p,c,d,h]=r.shape,m=[p,l,u,h],f=t.makeOutput(m,"float32");if(v.sizeFromShape(r.shape)===0)return f;let g=t.dataIdMap.get(r.dataId),y;g.dtype!=="float32"&&(y=ys({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),g=t.dataIdMap.get(y.dataId));let x=g.id,A=t.dataIdMap.get(f.dataId).id;return nk(x,p,c,d,h,l,u,s?1:0,i?1:0,A),y!=null&&t.disposeData(y.dataId),f}var wie={kernelName:No,backendName:"wasm",setupFunc:bie,kernelFunc:vie},rk;function kie(e){rk=e.wasm.cwrap(Mu,null,["number","number","number","array","array","boolean"])}function Iie(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=a.makeOutput(r.shape,"float32"),l=a.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=ys({backend:a,inputs:{x:r},attrs:{dtype:"float32"}}),l=a.dataIdMap.get(u.dataId)),rk(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(o.dataId).id,new Uint8Array(new Int32Array(r.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),i),u!=null&&a.disposeData(u.dataId),o}var Sie={kernelName:Mu,backendName:"wasm",setupFunc:kie,kernelFunc:Iie},sk;function Cie(e){sk=e.wasm.cwrap(Mo,null,["number","array","number","array","number","number"])}function Tie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=v.parseAxisParam(s,r.shape);if(r.shape.length===0)return l0({inputs:{x:r},backend:a});let o=a.makeOutput(r.shape,r.dtype),l=a.dataIdMap.get(r.dataId).id,u=a.dataIdMap.get(o.dataId).id,p=new Uint8Array(new Int32Array(i).buffer),c=new Uint8Array(new Int32Array(r.shape).buffer);sk(l,p,i.length,c,r.shape.length,u);let d=La({inputs:{x:o},attrs:{shape:r.shape},backend:a});return a.disposeData(o.dataId),d}var Nie={kernelName:Mo,backendName:"wasm",kernelFunc:Tie,setupFunc:Cie},ik;function Rie(e){ik=e.wasm.cwrap(el,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Eie(e){let{inputs:t,backend:a,attrs:n}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=n,l=a.makeOutput(r.shape,r.dtype),u=a.dataIdMap.get(r.dataId).id,p=a.dataIdMap.get(l.dataId).id,[c,d,h,m]=r.shape,[f,g]=C.getImageCenter(o,d,h),y=i===0,x=255,A=typeof i=="number"?[i,i,i,y?0:x]:[...i,x],b=new Uint8Array(new Int32Array(A).buffer);return ik(u,c,d,h,m,s,f,g,b,A.length,p),l}var Mie={kernelName:el,backendName:"wasm",kernelFunc:Eie,setupFunc:Rie},$ie=Qe($o),Pie=Qe(Po),ok;function _ie(e){ok=e.wasm.cwrap(_o,null,["number","number","number","number","number","number","array","number","number"])}function Fie(e){let{backend:t,inputs:a,attrs:n}=e,{indices:r,updates:s}=a,{shape:i}=n,o=t.makeOutput(i,s.dtype);if(v.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=jh.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(c).buffer),g=t.dataIdMap.get(o.dataId).id;return ok(h,m,nt[s.dtype],l,u,p,f,d,g),o}var Die={kernelName:_o,backendName:"wasm",setupFunc:_ie,kernelFunc:Fie},lk;function Oie(e){lk=e.wasm.cwrap(Do,null,["number","number","number","number","number","number","bool","number"])}function zie(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n;if(r.dtype!==s.dtype)throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. 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Ld;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(Ld||(Ld={}));var N8;function _te(e){N8=e.wasm.cwrap(Zr,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Fte(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n,d=a.dataIdMap.get(r.dataId).id,h=a.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let N=a.dataIdMap.get(i.dataId);if(N.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${N.shape.length}.`);m=N.id}let f=o==null?0:a.dataIdMap.get(o.dataId).id,g=Ld[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],x=u?s.shape[1]:s.shape[2],A=Wo.assertAndGetBroadcastShape(r.shape.slice(0,-2),s.shape.slice(0,-2)),b=a.makeOutput([...A,y,x],r.dtype),w=a.dataIdMap.get(b.dataId).id,I=new Uint8Array(new Int32Array(r.shape).buffer),T=new Uint8Array(new Int32Array(s.shape).buffer);return N8(d,I,r.shape.length,h,T,s.shape.length,l,u,g,m,f,c||0,w),b}var Dte={kernelName:Zr,backendName:"wasm",setupFunc:_te,kernelFunc:Fte};function Qe(e,t){let a;function n(s){a=s.wasm.cwrap(e,null,["number","number","number"])}function r(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),p=i.dataIdMap.get(u.dataId).id;return v.sizeFromShape(u.shape)===0||a(l,nt[o.dtype],p),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:r}}var Ote=Qe("Abs"),zte=Qe(ii),Lte=Qe(oi);function Gt(e,t,a){let n;function r(i){n=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:p}=l,c=o.dataIdMap.get(u.dataId).id,d=o.dataIdMap.get(p.dataId).id,h=a!=null?a:u.dtype,m=C.assertAndGetBroadcastShape(u.shape,p.shape),f=o.makeOutput(m,h);if(v.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(p.shape).buffer),x=o.dataIdMap.get(f.dataId).id;return n(c,g,u.shape.length,d,y,p.shape.length,nt[u.dtype],x),f}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var Wte=!0,Bte=Gt("Add",Wte),R8;function Vte(e){R8=e.wasm.cwrap(li,null,["array","number","number","number"])}function Ute(e){let{inputs:t,backend:a}=e,n=a.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(n.shape)===0)return n;let r=t.map(o=>a.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=a.dataIdMap.get(n.dataId).id;return R8(s,r.length,nt[n.dtype],i),n}var Gte={kernelName:li,backendName:"wasm",setupFunc:Vte,kernelFunc:Ute};function Vh(e){let{inputs:{x:t},backend:a}=e;if(t.dtype==="string")return Ve(a.readSync(t.dataId),t.shape,t.dtype);let n=a.makeOutput(t.shape,t.dtype),r=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(n).set(r),n}var Hte={kernelName:Wi,backendName:"wasm",kernelFunc:Vh},E8;function jte(e){E8=e.wasm.cwrap(Ir,null,["number","array","number","number","number","array","number"])}function os(e){let{inputs:t,backend:a,attrs:n}=e,[r,s]=Xte(t.x.shape,n.perm),i=!0;for(let m=0;m=r&&(s===-1||n[s]>n[i])&&(s=i);n[s]=r}return[a,n]}var Kte={kernelName:Ir,backendName:"wasm",kernelFunc:os,setupFunc:jte};function fs(e,t,a){let n=e.shape,r=e.shape.length,s=v.parseAxisParam(t,n),i=s,o=C.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let p=new Array(r);for(let d=0;d`new shape: ${i}, old shape: ${n.shape}. 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t.dtype==="string"?c.stringBytes=l.slice(m,m+v.sizeFromShape(i)):r.typedArrayFromHeap(u).set(l.subarray(m,m+v.sizeFromShape(i))),u}if(t.dtype==="string"){let m=Qc(l,s,i,t.shape,t.dtype);return c.stringBytes=m,u}let d=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)Sae(l,p[0],d,s,i);else if(h===3)Cae(l,p[0],p[1],d,s,i);else if(h===4)Tae(l,p[0],p[1],p[2],d,s,i);else{let m=Qc(l,s,i,t.shape,t.dtype);d.set(m)}return u}function Sae(e,t,a,n,r){let s=0,i=n[0],o=n[1],l=i+r[0];for(let u=i;uy*x),l=C.getReshaped(r.shape,s,o),u=C.getPermuted(l.length,s.length),p=C.getReshapedPermuted(r.shape,s,o),c=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(p,i,s.length),h=La({inputs:{x:r},backend:a,attrs:{shape:l}}),m=os({inputs:{x:h},backend:a,attrs:{perm:u}}),f=La({inputs:{x:m},backend:a,attrs:{shape:p}}),g=ri({inputs:{x:f},backend:a,attrs:{begin:c,size:d}});return a.disposeData(h.dataId),a.disposeData(m.dataId),a.disposeData(f.dataId),g}var Eae={kernelName:ql,backendName:"wasm",kernelFunc:Rae},L8;function Mae(e){L8=e.wasm.cwrap(gi,null,["number","number","boolean","number","number","number"])}function $ae(e){let{backend:t,inputs:a,attrs:n}=e,{x:r,weights:s}=a,{size:i}=n,o=s.shape.reduce((c,d)=>c*d,1)!==0,l=r.shape.length===1?[i]:[r.shape[0],i],u=t.makeOutput(l,s.dtype);function p(c){return t.dataIdMap.get(c.dataId).id}return L8(p(r),i,o,p(s),nt[s.dtype],p(u)),u}var Pae={kernelName:gi,backendName:"wasm",setupFunc:Mae,kernelFunc:$ae},_ae=!0,Fae=Gt(Xl,_ae);function Dae(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.typedArrayFromHeap(n),i=a.typedArrayFromHeap(r),o=C.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeOutput([o.length],"int32",void 0,new Int32Array(o))}var Oae={kernelName:Kl,backendName:"wasm",kernelFunc:Dae};function gs(e){let{inputs:{x:t},attrs:{dtype:a},backend:n}=e,r=n.makeOutput(t.shape,a),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(s),r}var zae={kernelName:yi,backendName:"wasm",kernelFunc:gs},Lae=Qe(xi),W8;function Wae(e){W8=e.wasm.cwrap(ls,null,["number","number","number","number"])}function Bae(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o=a.dataIdMap.get(r.dataId).id,l=a.makeOutput(r.shape,r.dtype),u=a.dataIdMap.get(l.dataId).id;return W8(o,s,i,u),l}var Vae={kernelName:ls,backendName:"wasm",setupFunc:Wae,kernelFunc:Bae};function B8(e){let{inputs:t,backend:a}=e,n=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=t.map(h=>h.shape);C.assertParamsConsistent(r,n);let s=C.computeOutShape(t.map(h=>h.shape),n),i=t.filter(h=>v.sizeFromShape(h.shape)>0);if(i.length===1)return Vh({inputs:{x:i[0]},backend:a});let o=a.makeOutput(s,t[0].dtype);if(v.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(A=>{let b=[-1,v.sizeFromShape(A.shape.slice(n))];return La({inputs:{x:A},backend:a,attrs:{shape:b}})}),m=h.map(A=>({vals:a.readSync(A.dataId),shape:A.shape}));s=C.computeOutShape(h.map(A=>A.shape),1);let f=h[0].shape[0]===1,g=Xg(m,s,t[0].dtype,f),y=C.computeOutShape(i.map(A=>A.shape),n);o.shape=y;let x=a.dataIdMap.get(o.dataId);return x.stringBytes=C.fromStringArrayToUint8(g),h.forEach(A=>a.disposeData(A.dataId)),o}let l=v.sizeFromShape(i[0].shape.slice(0,n)),u=0,p=i.map(h=>{let m=v.sizeFromShape(h.shape.slice(n));return u+=m,m}),c=i.map(h=>a.typedArrayFromHeap(h)),d=a.typedArrayFromHeap(o);for(let h=0;h`cumprod does not support ${r.dtype} tensors in the WASM backend`);let u=C.getAxesPermutation([s],l),p=r;u!==null&&(p=os({inputs:{x:r},attrs:{perm:u},backend:a}));let c=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumprod",[c],l);let d=a.makeOutput(p.shape,p.dtype),h=p.shape[c],m=a.dataIdMap.get(p.dataId).id,f=a.dataIdMap.get(d.dataId).id;X8(m,i?1:0,o?1:0,h,f,nt[r.dtype]);let g=d;if(u!==null){let 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y=C.getUndoAxesPermutation(u);g=os({inputs:{x:d},attrs:{perm:y},backend:a}),a.disposeData(p.dataId),a.disposeData(d.dataId)}return g}var fne={kernelName:Si,backendName:"wasm",setupFunc:hne,kernelFunc:mne},Y8;function gne(e){Y8=e.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function yne(e){let{backend:t,inputs:a,attrs:n}=e,{x:r,weights:s}=a,{size:i,binaryOutput:o}=n,l=s.shape.reduce((d,h)=>d*h,1)!==0,u=r.shape.length===1?[i]:[r.shape[0],i],p=t.makeOutput(u,s.dtype);function c(d){return t.dataIdMap.get(d.dataId).id}return Y8(c(r),new Uint8Array(new Int32Array(r.shape).buffer),r.shape.length,i,l,c(s),nt[s.dtype],o,c(p)),p}var xne={kernelName:Jl,backendName:"wasm",setupFunc:gne,kernelFunc:yne},Z8;function Ane(e){Z8=e.wasm.cwrap(Ti,null,["number","number","number","array","number","array","array","number","number"])}function bne(e){let{backend:t,inputs:a,attrs:n}=e,{x:r}=a,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),m=i==="NHWC"?[o,c,d,h]:[o,h,c,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(m).buffer),A=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),b=t.dataIdMap.get(f.dataId).id;return Z8(g,s,i==="NHWC"?1:0,y,r.shape.length-1,x,A,m.length,b),f}var vne={kernelName:Ti,backendName:"wasm",setupFunc:Ane,kernelFunc:bne},J8;function wne(e){J8=e.wasm.cwrap(Ni,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kne(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:c}=a,d=u==null?[1,1]:u,h=C.computeConv2DInfo(r.shape,s.shape,l,d,p,c,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,x=h.padInfo.bottom,A=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,I=h.strideHeight,T=h.strideWidth,N=h.inChannels,M=h.outChannels,$=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let E=n.makeOutput(h.outShape,"float32"),S=n.dataIdMap.get(E.dataId).id;return J8(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,x,A,$,b,w,I,T,N,M,S),E}var Ine={kernelName:Ni,backendName:"wasm",setupFunc:wne,kernelFunc:kne},Q8;function Sne(e){Q8=e.wasm.cwrap("Diag",null,["number","number","number","number"])}function Cne(e){let{inputs:t,backend:a}=e,{x:n}=t,r=v.sizeFromShape(n.shape),s=a.makeOutput([...n.shape,...n.shape],n.dtype);return Q8(a.dataIdMap.get(n.dataId).id,nt[n.dtype],r,a.dataIdMap.get(s.dataId).id),s}var Tne={kernelName:Ql,backendName:"wasm",setupFunc:Sne,kernelFunc:Cne},ew;function Nne(e){ew=e.wasm.cwrap(Ri,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Rne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n;if(r.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${r.dtype} and ${s.dtype}`);let u=C.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=a.makeOutput(u.outShape,r.dtype);return ew(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(p.dataId).id,nt[r.dtype],u.batchSize,u.inChannels,u.inHeight,u.inWidth,u.outHeight,u.outWidth,u.strideHeight,u.strideWidth,u.dilationHeight,u.dilationWidth,u.filterHeight,u.filterWidth,u.padInfo.top,u.padInfo.left),p}var Ene={kernelName:Ri,backendName:"wasm",setupFunc:Nne,kernelFunc:Rne},tw;function Mne(e){tw=e.wasm.cwrap(Ml,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function $ne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=C.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=a.makeOutput(s.shape,s.dtype);return tw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,nt[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),c}var Pne={kernelName:Ml,backendName:"wasm",setupFunc:Mne,kernelFunc:$ne},aw;function _ne(e){aw=e.wasm.cwrap(El,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Fne(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=C.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=a.makeOutput(r.shape,r.dtype);return aw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,nt[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),c}var Dne={kernelName:El,backendName:"wasm",setupFunc:_ne,kernelFunc:Fne},One=Qe("Elu"),nw;function zne(e){nw=e.wasm.cwrap(eu,null,["number","number","number"])}function Lne(e){let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=a.makeOutput(r.shape,"float32"),i=o=>a.dataIdMap.get(o.dataId).id;return nw(i(r),i(n),i(s)),s}var Wne={kernelName:eu,backendName:"wasm",setupFunc:zne,kernelFunc:Lne},Bne=!1,Vne=Gt(Mi,Bne,"bool"),Une=Qe("Erf"),Gne=Qe("Exp","float32");function k1(e){let{inputs:t,attrs:a,backend:n}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),La({inputs:{x:r},backend:n,attrs:{shape:o}})}var Hne={kernelName:tu,backendName:"wasm",kernelFunc:k1},jne=Qe($i,"float32");function rw(e){let{attrs:{shape:t,value:a},backend:n}=e,{attrs:{dtype:r}}=e;r=r||v.inferDtype(a);let s=n.makeOutput(t,r);return n.typedArrayFromHeap(s).fill(a),s}var qne={kernelName:au,backendName:"wasm",kernelFunc:rw},sw;function Xne(e){sw=e.wasm.cwrap(Pi,null,["number","number","number","number","number","number"])}function Kne(e){let{inputs:t,backend:a}=e,{image:n}=t,r=a.makeOutput(n.shape,n.dtype),s=a.dataIdMap.get(n.dataId).id,i=a.dataIdMap.get(r.dataId).id,[o,l,u,p]=n.shape;return sw(s,o,l,u,p,i),r}var Yne={kernelName:Pi,backendName:"wasm",kernelFunc:Kne,setupFunc:Xne},Zne=Qe(_i),Jne=!1,Qne=Gt(Fi,Jne),iw;function ere(e){iw=e.wasm.cwrap(Di,null,["number","number","number","number","number","number","number"])}function tre(e){let{backend:t,inputs:a,attrs:n}=e,{varianceEpsilon:r}=n,{x:s,mean:i,variance:o,offset:l,scale:u}=a,p=t.dataIdMap.get(s.dataId).id,c=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return iw(p,c,d,h,m,r,g),f}var are={kernelName:Di,backendName:"wasm",setupFunc:ere,kernelFunc:tre},ow;function nre(e){ow=e.wasm.cwrap(Jr,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function rre(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:c,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=C.computeConv2DInfo(r.shape,s.shape,l,p,u,d),g=Ld[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=n.dataIdMap.get(r.dataId).id,x=n.dataIdMap.get(s.dataId).id,A=f.outChannels,b=0;if(i!=null){let X=n.dataIdMap.get(i.dataId);if(X.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${X.shape.length}.`);if(X.shape[0]!==A)throw new Error(`FusedConv2D bias shape (${X.shape}) does not match the number of output channels (${A})`);b=X.id}let w=f.filterHeight,I=f.filterWidth,T=f.padInfo.top,N=f.padInfo.right,M=f.padInfo.bottom,$=f.padInfo.left,E=f.dilationHeight,S=f.dilationWidth,_=f.strideHeight,O=f.strideWidth,W=f.inChannels,P=f.padInfo.type==="SAME"?1:0,U=f.batchSize,G=f.inHeight,q=f.inWidth;if(c!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${c}'. Please use 'NHWC'.`);let H=n.makeOutput(f.outShape,"float32"),V=n.dataIdMap.get(H.dataId).id,Z=o==null?0:n.dataIdMap.get(o.dataId).id;return ow(y,U,G,q,x,w,I,b,T,N,M,$,P,E,S,_,O,W,A,g,Z,m||0,V),H}var sre={kernelName:Jr,backendName:"wasm",setupFunc:nre,kernelFunc:rre},lw;function ire(e){lw=e.wasm.cwrap(Qr,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ore(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:c,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=C.computeConv2DInfo(r.shape,s.shape,l,p,u,d,!0),g=Ld[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=n.dataIdMap.get(r.dataId).id,x=n.dataIdMap.get(s.dataId).id,A=f.outChannels,b=0;if(i!=null){let X=n.dataIdMap.get(i.dataId);if(X.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${X.shape.length}.`);if(X.shape[0]!==A)throw new Error(`FusedDepthwiseConv2D bias shape (${X.shape}) does not match the number of output channels (${A})`);b=X.id}let w=f.filterHeight,I=f.filterWidth,T=f.padInfo.top,N=f.padInfo.right,M=f.padInfo.bottom,$=f.padInfo.left,E=f.dilationHeight,S=f.dilationWidth,_=f.strideHeight,O=f.strideWidth,W=f.inChannels,P=f.padInfo.type==="SAME"?1:0,U=f.batchSize,G=f.inHeight,q=f.inWidth;if(c!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${c}'. Please use 'NHWC'.`);let H=n.makeOutput(f.outShape,"float32"),V=n.dataIdMap.get(H.dataId).id,Z=o==null?0:n.dataIdMap.get(o.dataId).id;return lw(y,U,G,q,x,w,I,b,T,N,M,$,P,E,S,_,O,W,A,g,Z,m||0,V),H}var lre={kernelName:Qr,backendName:"wasm",setupFunc:ire,kernelFunc:ore},uw;function ure(e){uw=e.wasm.cwrap(Oi,null,["number","number","number","number","number","number","array","number"])}function dre(e){let{backend:t,inputs:a}=e,{params:n,indices:r}=a,[s,i,o,l]=Lg.prepareAndValidate(n,r),u=t.makeOutput(s,n.dtype);if(i===0)return u;let p=r.shape,c=p[p.length-1],d=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return uw(d,nt[n.dtype],h,i,c,o,m,f),u}var pre={kernelName:Oi,backendName:"wasm",setupFunc:ure,kernelFunc:dre},dw;function cre(e){dw=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function hre(e){let{backend:t,inputs:a,attrs:n}=e,{x:r,indices:s}=a,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let T=0;T=0,()=>`GatherV2: the index value ${N} is not in [0, ${p-1}]`)}let c=C.segment_util.collectGatherOpShapeInfo(r,s,l,o),d=La({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),m=La({inputs:{x:s},attrs:{shape:[c.batchSize,h/c.batchSize]},backend:t}),f=[c.batchSize,c.outerSize,h/c.batchSize,c.sliceSize],g=t.makeOutput(f,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let y=d.shape.length-1,x=t.dataIdMap.get(d.dataId).id,A=t.dataIdMap.get(m.dataId).id,b=t.dataIdMap.get(g.dataId).id,w=new Uint8Array(new Int32Array(v.computeStrides(d.shape)).buffer),I=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer);return dw(x,nt[r.dtype],w,y,A,c.batchSize,I,b),t.disposeData(d.dataId),t.disposeData(m.dataId),g.shape=c.outputShape,g}var mre={kernelName:nu,backendName:"wasm",setupFunc:cre,kernelFunc:hre},fre=!1,gre=Gt(zi,fre,"bool"),yre=!1,xre=Gt(Li,yre,"bool"),Are=Qe(Bi,"bool"),bre=Qe(Vi,"bool"),vre=Qe(Ui,"bool"),pw;function wre(e){pw=e.wasm.cwrap(Gi,null,["number","number","number","number"])}function kre(e){let{inputs:{x:t},attrs:{alpha:a},backend:n}=e,r=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;pw(r,nt[t.dtype],a,i)}return s}var Ire={kernelName:Gi,backendName:"wasm",setupFunc:wre,kernelFunc:kre},Sre=!1,Cre=Gt(Hi,Sre,"bool"),Tre=!1,Nre=Gt(ji,Tre,"bool"),cw;function Rre(e){cw=e.wasm.cwrap(qi,null,["number","number","number","number"])}function Ere(e){let{attrs:t,backend:a}=e,{start:n,stop:r,num:s}=t,i=Math.floor(s),o=a.makeOutput([i],"float32");return cw(a.dataIdMap.get(o.dataId).id,n,r,i),o}var Mre={kernelName:qi,backendName:"wasm",setupFunc:Rre,kernelFunc:Ere},$re=Qe("Log"),Pre=Qe(Xi),_re=!1,Fre=Gt(Ki,_re,"bool"),Dre=Qe(Yi),Ore=!1,zre=Gt(Zi,Ore,"bool"),Lre=!1,Wre=Gt(uA,Lre,"bool"),hw;function Bre(e){hw=e.wasm.cwrap("LRN",null,["number","number","number","number","number","number","number"])}function Vre(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=a.makeOutput(r.shape,r.dtype);return hw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var Ure={kernelName:"LRN",backendName:"wasm",setupFunc:Bre,kernelFunc:Vre},mw;function Gre(e){mw=e.wasm.cwrap(ru,null,["number","number","number","number","number","number","number","number","number"])}function Hre(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let c=a.makeOutput(r.shape,r.dtype);return mw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(i.dataId).id,a.dataIdMap.get(c.dataId).id,i.shape[3],o,l,u,p),c}var jre={kernelName:ru,backendName:"wasm",setupFunc:Gre,kernelFunc:Hre},fw;function qre(e){fw=e.wasm.cwrap("Max",null,["number","number","number","number"])}function Xre(e){let{backend:t,inputs:a,attrs:n}=e,{reductionIndices:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:c,inputWasTransposed:d}=fs(i,r,t);if(d){let x=t.dataIdMap.get(u.dataId).id;l=u,o=x}let h=l.shape.length;C.assertAxesAreInnerMostDims("max",p,h);let[m,f]=C.computeOutAndReduceShapes(l.shape,p),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;fw(o,nt[i.dtype],g,x)}if(d&&t.disposeData(u.dataId),s){let x=C.expandShapeToKeepDim(y.shape,c);y.shape=x}return y}var Kre={kernelName:"Max",backendName:"wasm",setupFunc:qre,kernelFunc:Xre},Yre=!1,Zre=Gt(Ji,Yre),gw;function Jre(e){gw=e.wasm.cwrap(Qi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qre(e){let{inputs:t,attrs:a,backend:n}=e,r=t.x,s=n.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a,p=C.computePool2DInfo(r.shape,i,o,1,l,u),c=p.filterHeight,d=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,y=p.dilationHeight,x=p.dilationWidth,A=p.strideHeight,b=p.strideWidth,w=p.inChannels,I=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let T=n.makeOutput(p.outShape,"float32"),N=n.dataIdMap.get(T.dataId).id;return gw(s,r.shape[0],r.shape[1],r.shape[2],c,d,h,m,f,g,y,x,A,b,w,I,N),T}var ese={kernelName:Qi,backendName:"wasm",setupFunc:Jre,kernelFunc:Qre},yw;function tse(e){yw=e.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ase(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,p=C.computePool3DInfo(r.shape,s,i,1,o,l,u),c=a.makeOutput(p.outShape,r.dtype);return yw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),c}var nse={kernelName:su,backendName:"wasm",setupFunc:tse,kernelFunc:ase},xw;function rse(e){xw=e.wasm.cwrap("MaxPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function sse(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=C.computePool3DInfo(s.shape,i,o,1,l,u),c=a.makeOutput(s.shape,s.dtype);return xw(a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inDepth,p.inHeight,p.inWidth,p.outDepth,p.outHeight,p.outWidth,p.strideDepth,p.strideHeight,p.strideWidth,p.dilationDepth,p.dilationHeight,p.dilationWidth,p.effectiveFilterDepth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.front,p.padInfo.top,p.padInfo.left),c}var ise={kernelName:np,backendName:"wasm",setupFunc:rse,kernelFunc:sse},Aw;function ose(e){Aw=e.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function lse(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=C.computePool2DInfo(s.shape,i,o,1,l,u),c=a.makeOutput(s.shape,s.dtype);return Aw(a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),c}var use={kernelName:ap,backendName:"wasm",setupFunc:ose,kernelFunc:lse},bw;function dse(e){bw=e.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function pse(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,includeBatchInIndex:l}=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computePool2DInfo(r.shape,s,i,[1,1],o),c=a.makeOutput(p.outShape,r.dtype),d=a.makeOutput(p.outShape,"int32");return bw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(c.dataId).id,a.dataIdMap.get(d.dataId).id,nt[r.dtype],l,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),[c,d]}var cse={kernelName:iu,backendName:"wasm",setupFunc:dse,kernelFunc:pse},vw;function hse(e){vw=e.wasm.cwrap(eo,null,["number, number, number"])}function mse(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=fs(i,r,t),m=c;if(h){let b=t.dataIdMap.get(p.dataId).id;b!==o&&(u=p,l=b,m=C.getInnerMostAxes(m.length,u.shape.length))}C.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=C.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),x=u;u.dtype!=="float32"&&(x=gs({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(x.dataId).id);let A=t.makeOutput(f,"float32");if(v.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(A.dataId).id;vw(l,y,b)}if(h&&t.disposeData(p.dataId),s){let b=C.expandShapeToKeepDim(A.shape,d);A.shape=b}return u.dtype!=="float32"&&t.disposeData(x.dataId),A}var fse={kernelName:eo,backendName:"wasm",setupFunc:hse,kernelFunc:mse},ww;function gse(e){ww=e.wasm.cwrap("Min",null,["number","number","number","number"])}function yse(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=fs(i,r,t);if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A)}let m=u.shape.length;C.assertAxesAreInnerMostDims("min",c,m);let[f,g]=C.computeOutAndReduceShapes(u.shape,c),y=v.sizeFromShape(g),x=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;ww(l,nt[i.dtype],y,A)}if(h&&t.disposeData(p.dataId),s){let A=C.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var xse={kernelName:"Min",backendName:"wasm",setupFunc:gse,kernelFunc:yse},Ase=!1,bse=Gt(to,Ase),I1;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(I1||(I1={}));var kw;function vse(e){kw=e.wasm.cwrap(ao,null,["number","array","number","number","array","array","number","number"])}function wse(e){let{inputs:{x:t},backend:a,attrs:{paddings:n,mode:r}}=e,s=n.map((m,f)=>m[0]+t.shape[f]+m[1]),i=a.dataIdMap.get(t.dataId).id,o=a.makeOutput(s,t.dtype),l=a.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(m=>m[0]),c=n.map(m=>m[1]),d=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(c).buffer);return kw(i,u,t.shape.length,nt[t.dtype],d,h,I1[r],l),o}var kse={kernelName:ao,backendName:"wasm",kernelFunc:wse,setupFunc:vse},Iw;function Ise(e){Iw=e.wasm.cwrap(Eo,null,["number","number","number","number"])}function Sw(e){let{backend:t,inputs:{logits:a},attrs:{dim:n}}=e,r=t.dataIdMap.get(a.dataId).id,s=t.makeOutput(a.shape,a.dtype),i=t.dataIdMap.get(s.dataId).id,o=a.shape[n],l=v.sizeFromShape(a.shape)/o;return v.sizeFromShape(s.shape)===0||Iw(r,i,o,l),s}var Sse={kernelName:Eo,backendName:"wasm",setupFunc:Ise,kernelFunc:Sw},Cw;function Cse(e){Cw=e.wasm.cwrap(no,null,["number","number","number","number","number","number"])}function Tse(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n;if(r.dtype!=="float32")throw new Error(`Tensor logits must have dtype float32, got ${r.dtype}`);let l=o?r:Sw({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),[u,p]=l.shape,c=a.makeOutput([u,s],"int32");return Cw(a.dataIdMap.get(l.dataId).id,u,p,s,i,a.dataIdMap.get(c.dataId).id),o||a.disposeData(l.dataId),c}var Nse={kernelName:no,backendName:"wasm",setupFunc:Cse,kernelFunc:Tse},Rse=Gt("Mod",!0),Ese=!0,Mse=Gt(ro,Ese),$se=Qe("Neg");function w3(e,t){let a=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=a[0],r=a[1],s=a[2],i=a[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var Tw;function Pse(e){Tw=e.wasm.cwrap(io,"number",["number","number","number","number","number"])}function _se(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=a,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,c=Tw(u,p,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=w3(t,c);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var Fse={kernelName:io,backendName:"wasm",setupFunc:Pse,kernelFunc:_se},Nw;function Dse(e){Nw=e.wasm.cwrap(ou,"number",["number","number","number","number","number","bool"])}function Ose(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=Nw(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=w3(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),x=t.makeOutput([],"int32",g);return[y,x]}var zse={kernelName:ou,backendName:"wasm",setupFunc:Dse,kernelFunc:Ose},Rw;function Lse(e){Rw=e.wasm.cwrap(oo,"number",["number","number","number","number","number","number"])}function Wse(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=a,p=t.dataIdMap.get(l.dataId).id,c=t.dataIdMap.get(u.dataId).id,d=Rw(p,c,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=w3(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),x=t.makeOutput([m],"float32",f);return[y,x]}var Bse={kernelName:oo,backendName:"wasm",setupFunc:Lse,kernelFunc:Wse},Vse=!1,Use=Gt(so,Vse,"bool"),Ew;function Gse(e){Ew=e.wasm.cwrap(lo,null,["number","number","number","number","number"])}function Hse(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=a.makeOutput([...r.shape,i],s),p=a.dataIdMap.get(u.dataId).id,c=a.dataIdMap.get(r.dataId).id;return Ew(c,i,o,l,p),u}var jse={kernelName:lo,backendName:"wasm",setupFunc:Gse,kernelFunc:Hse};function qse(e){let{inputs:{x:t},backend:a}=e,n=a.makeOutput(t.shape,t.dtype);return a.typedArrayFromHeap(n).fill(1),n}var Xse={kernelName:lu,backendName:"wasm",kernelFunc:qse};function Kse(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return k1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=k1({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=B8({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeData(p.dataId)),u}var Yse={kernelName:uu,backendName:"wasm",kernelFunc:Kse},Mw;function Zse(e){Mw=e.wasm.cwrap(uo,null,["number","array","number","number","array","array","number","number"])}function Jse(e){let{inputs:{x:t},backend:a,attrs:{paddings:n,constantValue:r}}=e,s=n.map((m,f)=>m[0]+t.shape[f]+m[1]);if(v.sizeFromShape(t.shape)===0)return rw({backend:a,attrs:{shape:s,value:r,dtype:t.dtype}});let i=a.dataIdMap.get(t.dataId).id,o=a.makeOutput(s,t.dtype),l=a.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(m=>m[0]),c=n.map(m=>m[1]),d=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(c).buffer);return Mw(i,u,t.shape.length,nt[t.dtype],d,h,r,l),o}var $w={kernelName:uo,backendName:"wasm",kernelFunc:Jse,setupFunc:Zse},Qse=!1,eie=Gt("Pow",Qse),Pw;function tie(e){Pw=e.wasm.cwrap(po,null,["number","number","number"])}function aie(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=a.dataIdMap.get(n.dataId).id,i=a.dataIdMap.get(r.dataId).id,o=s,l=n,u=l;l.dtype!=="float32"&&(u=gs({backend:a,inputs:{x:n},attrs:{dtype:"float32"}}),o=a.dataIdMap.get(u.dataId).id);let p=a.makeOutput(n.shape,"float32"),c=a.dataIdMap.get(p.dataId).id;return Pw(o,i,c),l.dtype!=="float32"&&a.disposeData(u.dataId),p}var nie={kernelName:po,backendName:"wasm",setupFunc:tie,kernelFunc:aie},_w;function rie(e){_w=e.wasm.cwrap(co,null,["number","number","number","number"])}function sie(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=fs(i,r,t),m=c;if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A,m=C.getInnerMostAxes(m.length,u.shape.length))}C.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=C.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),x=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;_w(l,y,nt[x.dtype],A)}if(h&&t.disposeData(p.dataId),s){let A=C.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var iie={kernelName:co,backendName:"wasm",setupFunc:rie,kernelFunc:sie},oie=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=Zg(n,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},lie={kernelName:du,backendName:"wasm",kernelFunc:oie},uie=!0,die=Gt(Ei,uie),pie=Qe(ho),cie=Qe(mo),hie=Qe(yo),Fw;function mie(e){Fw=e.wasm.cwrap(go,null,["number","number","number","number","number","number","number","number","number","number"])}function fie(e){let{backend:t,inputs:a,attrs:n}=e,{images:r}=a,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[p,c,d,h]=r.shape,m=[p,l,u,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=gs({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,x=t.makeOutput(m,"float32");if(v.sizeFromShape(r.shape)===0)return x;let A=t.dataIdMap.get(x.dataId).id;return Fw(y,p,c,d,h,l,u,s?1:0,i?1:0,A),g!=null&&t.disposeData(g.dataId),x}var gie={kernelName:go,backendName:"wasm",setupFunc:mie,kernelFunc:fie},Dw;function yie(e){Dw=e.wasm.cwrap(hu,null,["number","number","number","array","array","boolean"])}function xie(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=a.makeOutput(r.shape,"float32"),l=a.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=gs({backend:a,inputs:{x:r},attrs:{dtype:"float32"}}),l=a.dataIdMap.get(u.dataId)),Dw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(o.dataId).id,new Uint8Array(new Int32Array(r.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),i),u!=null&&a.disposeData(u.dataId),o}var Aie={kernelName:hu,backendName:"wasm",setupFunc:yie,kernelFunc:xie},Ow;function bie(e){Ow=e.wasm.cwrap(fo,null,["number","number","number","number","number","number","number","number","number","number"])}function vie(e){let{backend:t,inputs:a,attrs:n}=e,{images:r}=a,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[p,c,d,h]=r.shape,m=[p,l,u,h],f=t.makeOutput(m,"float32");if(v.sizeFromShape(r.shape)===0)return f;let g=t.dataIdMap.get(r.dataId),y;g.dtype!=="float32"&&(y=gs({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),g=t.dataIdMap.get(y.dataId));let x=g.id,A=t.dataIdMap.get(f.dataId).id;return Ow(x,p,c,d,h,l,u,s?1:0,i?1:0,A),y!=null&&t.disposeData(y.dataId),f}var wie={kernelName:fo,backendName:"wasm",setupFunc:bie,kernelFunc:vie},zw;function kie(e){zw=e.wasm.cwrap(cu,null,["number","number","number","array","array","boolean"])}function Iie(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=a.makeOutput(r.shape,"float32"),l=a.dataIdMap.get(r.dataId),u;return l.dtype!=="float32"&&(u=gs({backend:a,inputs:{x:r},attrs:{dtype:"float32"}}),l=a.dataIdMap.get(u.dataId)),zw(a.dataIdMap.get(r.dataId).id,a.dataIdMap.get(s.dataId).id,a.dataIdMap.get(o.dataId).id,new Uint8Array(new Int32Array(r.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),i),u!=null&&a.disposeData(u.dataId),o}var Sie={kernelName:cu,backendName:"wasm",setupFunc:kie,kernelFunc:Iie},Lw;function Cie(e){Lw=e.wasm.cwrap(xo,null,["number","array","number","array","number","number"])}function Tie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=v.parseAxisParam(s,r.shape);if(r.shape.length===0)return Vh({inputs:{x:r},backend:a});let o=a.makeOutput(r.shape,r.dtype),l=a.dataIdMap.get(r.dataId).id,u=a.dataIdMap.get(o.dataId).id,p=new Uint8Array(new Int32Array(i).buffer),c=new Uint8Array(new Int32Array(r.shape).buffer);Lw(l,p,i.length,c,r.shape.length,u);let d=La({inputs:{x:o},attrs:{shape:r.shape},backend:a});return a.disposeData(o.dataId),d}var Nie={kernelName:xo,backendName:"wasm",kernelFunc:Tie,setupFunc:Cie},Ww;function Rie(e){Ww=e.wasm.cwrap(Oo,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Eie(e){let{inputs:t,backend:a,attrs:n}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=n,l=a.makeOutput(r.shape,r.dtype),u=a.dataIdMap.get(r.dataId).id,p=a.dataIdMap.get(l.dataId).id,[c,d,h,m]=r.shape,[f,g]=C.getImageCenter(o,d,h),y=i===0,x=255,A=typeof i=="number"?[i,i,i,y?0:x]:[...i,x],b=new Uint8Array(new Int32Array(A).buffer);return Ww(u,c,d,h,m,s,f,g,b,A.length,p),l}var Mie={kernelName:Oo,backendName:"wasm",kernelFunc:Eie,setupFunc:Rie},$ie=Qe(Ao),Pie=Qe(bo),Bw;function _ie(e){Bw=e.wasm.cwrap(vo,null,["number","number","number","number","number","number","array","number","number"])}function Fie(e){let{backend:t,inputs:a,attrs:n}=e,{indices:r,updates:s}=a,{shape:i}=n,o=t.makeOutput(i,s.dtype);if(v.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=Ch.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(c).buffer),g=t.dataIdMap.get(o.dataId).id;return Bw(h,m,nt[s.dtype],l,u,p,f,d,g),o}var Die={kernelName:vo,backendName:"wasm",setupFunc:_ie,kernelFunc:Fie},Vw;function Oie(e){Vw=e.wasm.cwrap(ko,null,["number","number","number","number","number","number","bool","number"])}function zie(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n;if(r.dtype!==s.dtype)throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. 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Hie(e){let{backend:t,inputs:{x:a}}=e,n=t.dataIdMap.get(a.dataId).id,r=t.makeOutput(a.shape,a.dtype),s=t.dataIdMap.get(r.dataId).id;return v.sizeFromShape(r.shape)===0||Gw(n,s),r}var jie={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Gie,kernelFunc:Hie},qie=Qe(Co),Xie=Qe("Sin"),Kie=Qe(So),Yie=Qe(No);function Zie(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n,o=v.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g0?l+1:0;if(u<0)throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let p=r.shape.slice();p[0]=u;let c=a.dataIdMap.get(r.dataId).id,d=a.dataIdMap.get(s.dataId).id,h=a.dataIdMap.get(i.dataId).id,m=a.makeOutput(p,r.dtype),f=a.dataIdMap.get(m.dataId).id,g=a.makeOutput([4],"int32"),y=a.dataIdMap.get(g.dataId).id;hk(c,nt[r.dtype],r.shape[0],d,h,f,y,t,0);let x=a.readSync(g.dataId),A;switch(x[0]){case 0:{A=C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{A=C.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:A=C.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(x[1],x[2]);break;case 3:A=C.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(x[1],x[2],x[3]);break;default:A=""}if(a.disposeData(g.dataId),A)throw a.disposeData(m.dataId),new Error(A);return m}function soe(e){return fk(e,!0)}var ioe={kernelName:zu,backendName:"wasm",setupFunc:mk,kernelFunc:soe};function ooe(e){return fk(e,!1)}var loe={kernelName:Lu,backendName:"wasm",setupFunc:mk,kernelFunc:ooe},gk;function uoe(e){gk=e.wasm.cwrap(jo,null,["number","number","number","number","number","number","number","number","array","number","number"])}function doe(e){let{backend:t,inputs:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=a,{outputShape:o}=n,l=t.makeOutput(o,i.dtype);if(v.sizeFromShape(o)===0)return l;let{sliceRank:u,numUpdates:p,sliceSize:c,strides:d,outputSize:h}=C.calculateShapes(s,r,o),m=t.dataIdMap.get(r.dataId).id,f=t.dataIdMap.get(s.dataId).id,g=t.dataIdMap.get(i.dataId).id,y=new Uint8Array(new Int32Array(d).buffer),x=t.dataIdMap.get(l.dataId).id;return gk(m,f,s.shape.length,g,nt[i.dtype],u,p,c,y,h,x),l}var poe={kernelName:jo,backendName:"wasm",setupFunc:uoe,kernelFunc:doe};function coe(e){let{inputs:t,attrs:a,backend:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=v.parseAxisParam(i,r.shape)[0],l=C.prepareSplitSize(r,s,o),u=new Array(r.shape.length).fill(0),p=r.shape.slice();return l.map(c=>{let d=[...p];d[o]=c;let h=si({inputs:{x:r},attrs:{begin:u,size:d},backend:n});return u[o]+=c,h})}var hoe={kernelName:Du,backendName:"wasm",kernelFunc:coe},moe=Qe(Uo),foe=Qe(Cp),goe=!0,yoe=Gt(qo,goe),yk;function xoe(e){yk=e.wasm.cwrap(ps,null,["number","number","number","number"])}function Aoe(e){let{backend:t,inputs:a,attrs:n}=e,{alpha:r}=n,{x:s}=a,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return yk(i,r,nt[s.dtype],l),o}var boe={kernelName:ps,backendName:"wasm",setupFunc:xoe,kernelFunc:Aoe},xk;function voe(e){xk=e.wasm.cwrap(Xo,null,["number","array","number","array","array","array","array","array","number","number"])}function woe(e){let{backend:t,inputs:a,attrs:n}=e,{x:r}=a,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Nt.sliceInfo(r.shape,s,i,o,l,u,p,c,d),w;if(f)w=La({inputs:{x:r},backend:t,attrs:{shape:m}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let 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Noe(e){let{backend:t,inputs:a,attrs:n}=e,{input:r}=a,{numBuckets:s}=n,i=t.readSync(r.dataId),o=v3(i,s),l=t.makeOutput(r.shape,"int32");return t.typedArrayFromHeap(l).set(o),l}var Roe={kernelName:Rp,backendName:"wasm",kernelFunc:Noe},Eoe=!0,Moe=Gt(Ko,Eoe),Ak;function $oe(e){Ak=e.wasm.cwrap(Go,null,["number","number","number","number"])}function Poe(e){let{backend:t,inputs:a,attrs:n}=e,{axis:r,keepDims:s}=n,{x:i}=a,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:c,originalAxes:d,inputWasTransposed:h}=gs(i,r,t),m=c;if(h){let A=t.dataIdMap.get(p.dataId).id;A!==o&&(u=p,l=A,m=C.getInnerMostAxes(m.length,u.shape.length))}C.assertAxesAreInnerMostDims("sum",m,u.shape.length);let[f,g]=C.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),x=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;Ak(l,y,nt[x.dtype],A)}if(h&&t.disposeData(p.dataId),s){let A=C.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var _oe={kernelName:Go,backendName:"wasm",setupFunc:$oe,kernelFunc:Poe},Foe=Qe(Yo),Doe=Qe(Zo),bk;function Ooe(e){bk=e.wasm.cwrap(Fo,null,["number","number","number","number","number","number","array","number","number","number"])}function zoe(e){let{backend:t,inputs:a,attrs:n}=e,{tensor:r,indices:s,updates:i}=a,{}=n,o=t.makeOutput(r.shape,r.dtype);if(v.sizeFromShape(r.shape)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=jh.calculateShapes(i,s,r.shape),h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(i.dataId).id,f=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(c).buffer),y=t.dataIdMap.get(o.dataId).id;return bk(h,m,nt[i.dtype],l,u,p,g,d,y,f),o}var Loe={kernelName:Fo,backendName:"wasm",setupFunc:Ooe,kernelFunc:zoe},vk;function Woe(e){vk=e.wasm.cwrap(ds,null,["number","array","number","array","number","number"])}function Boe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,s=a.dataIdMap.get(r.dataId).id,{reps:i}=n,o=new Array(r.shape.length);for(let 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o;let{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=Ch.calculateShapes(i,s,r.shape),h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(i.dataId).id,f=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(c).buffer),y=t.dataIdMap.get(o.dataId).id;return ek(h,m,nt[i.dtype],l,u,p,g,d,y,f),o}var Loe={kernelName:wo,backendName:"wasm",setupFunc:Ooe,kernelFunc:zoe},tk;function Woe(e){tk=e.wasm.cwrap(us,null,["number","array","number","array","number","number"])}function Boe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,s=a.dataIdMap.get(r.dataId).id,{reps:i}=n,o=new Array(r.shape.length);for(let d=0;d{let{x:n}=e,{k:r,sorted:s}=a,i=t.dataIdMap.get(n.dataId).id,o=new Uint8Array(new Int32Array(n.shape).buffer),l=n.shape.slice();l[l.length-1]=r;let u=t.makeOutput(l,n.dtype),p=t.dataIdMap.get(u.dataId).id,c=t.makeOutput(l,"int32"),d=t.dataIdMap.get(c.dataId).id;return 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loop { @@ -5006,11 +5006,11 @@ return a / b;`,NJ=` } oldValue = res.old_value; } - }`,nu;(function(e){e[e.FROM_PIXELS=0]="FROM_PIXELS",e[e.DRAW=1]="DRAW"})(nu||(nu={}));var yle=(e,t,a,n,r)=>{let s={dtype:n.dtype,shape:n.shape},i=Ale(a,s,t),o=e.createShaderModule({code:i,label:t.constructor.name}),l=B().get("WEBGPU_PRINT_SHADER");if(l!==""){l=l.toLowerCase();let u=l.split(",");(l==="all"||u.some(p=>t.shaderKey.toLowerCase().includes(p)))&&(console.group(t.shaderKey),console.debug(i),console.groupEnd())}return r?e.createComputePipelineAsync({compute:{module:o,entryPoint:"_start"},label:t.constructor.name,layout:"auto"}):e.createComputePipeline({compute:{module:o,entryPoint:"_start"},label:t.constructor.name,layout:"auto"})},Xe=(e,t="f32")=>{switch(e){case 1:return`${t}`;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component ${t} is not supported.`)}};function Pt(e){if(e<=1)return"i32";if(e===2)return"vec2";if(e===3)return"vec3";if(e===4)return"vec4";if(e===5)return"vec5";if(e===6)return"vec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Cr(e){if(e===0)return"x";if(e===1)return"y";if(e===2)return"z";if(e===3)return"w";if(e===4)return"u";if(e===5)return"v";throw Error(`Index ${e} is not yet supported`)}function ue(...e){let t;switch(e.length){case 0:t=` + }`,Wl;(function(e){e[e.FROM_PIXELS=0]="FROM_PIXELS",e[e.DRAW=1]="DRAW"})(Wl||(Wl={}));var yle=(e,t,a,n,r)=>{let s={dtype:n.dtype,shape:n.shape},i=Ale(a,s,t),o=e.createShaderModule({code:i,label:t.constructor.name}),l=B().get("WEBGPU_PRINT_SHADER");if(l!==""){l=l.toLowerCase();let u=l.split(",");(l==="all"||u.some(p=>t.shaderKey.toLowerCase().includes(p)))&&(console.group(t.shaderKey),console.debug(i),console.groupEnd())}return r?e.createComputePipelineAsync({compute:{module:o,entryPoint:"_start"},label:t.constructor.name,layout:"auto"}):e.createComputePipeline({compute:{module:o,entryPoint:"_start"},label:t.constructor.name,layout:"auto"})},Xe=(e,t="f32")=>{switch(e){case 1:return`${t}`;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component ${t} is not supported.`)}};function Pt(e){if(e<=1)return"i32";if(e===2)return"vec2";if(e===3)return"vec3";if(e===4)return"vec4";if(e===5)return"vec5";if(e===6)return"vec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Cr(e){if(e===0)return"x";if(e===1)return"y";if(e===2)return"z";if(e===3)return"w";if(e===4)return"u";if(e===5)return"v";throw Error(`Index ${e} is not yet supported`)}function ue(...e){let t;switch(e.length){case 0:t=` fn main() `;break;case 1:t=` fn main(${e[0]} : i32) - `;break;default:throw Error("Unreachable")}return t}function aA(e,t){let a;return a=` + `;break;default:throw Error("Unreachable")}return t}function D5(e,t){let a;return a=` ${xle(t)} fn _start(@builtin(local_invocation_id) LocalId : vec3, @builtin(global_invocation_id) GlobalId : vec3, @@ -5036,12 +5036,12 @@ return a / b;`,NJ=` // Only used when the y/z dimension of workgroup size is 1. fn getGlobalIndex() -> i32 { - ${Ck(a)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + + ${ik(a)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + workgroupId.y * numWorkgroups.x + workgroupId.x) * ${r}u + localIndex); `} } - `),a.pixelsOpType!=null){let h=a.pixelsOpType===nu.FROM_PIXELS?`@group(0) @binding(0) var result: array<${Hs(t.dtype,a.outputComponent)}>;`:`@group(0) @binding(1) var inBuf : array<${Hs(e[0].dtype,a.outputComponent)}>;`,m=t.shape.length===3?"vec2":"i32";n.push(` + `),a.pixelsOpType!=null){let h=a.pixelsOpType===Wl.FROM_PIXELS?`@group(0) @binding(0) var result: array<${Gs(t.dtype,a.outputComponent)}>;`:`@group(0) @binding(1) var inBuf : array<${Gs(e[0].dtype,a.outputComponent)}>;`,m=t.shape.length===3?"vec2":"i32";n.push(` struct Uniform { outShapeStrides : ${m}, size : i32, @@ -5051,21 +5051,21 @@ return a / b;`,NJ=` ${h} @group(0) @binding(2) var uniforms: Uniform; - `);let f=rA(a);return[nA,n.join(` -`),lh(t.shape),a.getUserCode(),aA(f,a)].join(` + `);let f=z5(a);return[O5,n.join(` +`),Vc(t.shape),a.getUserCode(),D5(f,a)].join(` `)}let s,i,o="struct Uniforms { NAN : f32, INFINITY : f32, ";a.variableNames.forEach((h,m)=>{let f=Pt(e[m].shape.length);o+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${f}, `,s=e[m].shape.length-1,i=Pt(s),o+=`${h.charAt(0).toLowerCase()+h.slice(1)}ShapeStrides: ${i}, `});let l=Pt(t.shape.length);o+=`outShape : ${l}, `,s=t.shape.length-1,i=Pt(s),o+=` outShapeStrides: ${i}, `,a.size&&(o+="size : i32, "),a.uniforms&&(o+=a.uniforms),o+="};",o=Nle(o),n.push(o),a.atomic?n.push(` @group(0) @binding(0) var result: array>; `):n.push(` - @group(0) @binding(0) var result: array<${Hs(t.dtype,a.outputComponent)}>; + @group(0) @binding(0) var result: array<${Gs(t.dtype,a.outputComponent)}>; `),a.variableNames.forEach((h,m)=>{n.push(` - @group(0) @binding(${1+m}) var ${h}: array<${a.variableComponents?Hs(e[m].dtype,a.variableComponents[m]):Hs(e[m].dtype,a.outputComponent)}>; + @group(0) @binding(${1+m}) var ${h}: array<${a.variableComponents?Gs(e[m].dtype,a.variableComponents[m]):Gs(e[m].dtype,a.outputComponent)}>; `)}),o!==""&&n.push(` @group(0) @binding(${1+a.variableNames.length}) var uniforms: Uniforms; - `);let u=Sle(t.shape,a.dispatchLayout),p=[nA,n.join(` -`)+vle,lh(t.shape),u,Cle(t.shape.length)];a.atomic||p.push(Tle(t.shape,t.dtype,a.outputComponent)),a.variableNames.forEach((h,m)=>{p.push(`${lh(e[m].shape,h)}`)});let c=e.map((h,m)=>Ile(h,t.shape,a.variableComponents?a.variableComponents[m]:a.outputComponent,a.dispatchLayout.x.length===t.shape.length)).join(` -`);p.push(c),p.push(a.getUserCode());let d=rA(a);return p.push(aA(d,a)),p.join(` -`)}function ble(e,t,a){let n=e.shaderKey;if(e.pixelsOpType!=null)return n;let r=[],s=[];t.forEach(p=>{r.push(p.shape),s.push(p.dtype)}),r.push(a.shape),s.push(a.dtype);let i=t.map(p=>C.getBroadcastDims(p.shape,a.shape)),o=t.map(p=>v.arraysEqual(p.shape,a.shape)).join("_"),l=i.map(p=>p.join("_")).join(";"),u=Ck(e)?"flatDispatch":"";return n+="_"+(e.workgroupSize?e.workgroupSize.join(","):"")+r.map(p=>p.length).join(",")+s.join(",")+e.variableNames.join(",")+l+o+u,n}var nA=` + `);let u=Sle(t.shape,a.dispatchLayout),p=[O5,n.join(` +`)+vle,Vc(t.shape),u,Cle(t.shape.length)];a.atomic||p.push(Tle(t.shape,t.dtype,a.outputComponent)),a.variableNames.forEach((h,m)=>{p.push(`${Vc(e[m].shape,h)}`)});let c=e.map((h,m)=>Ile(h,t.shape,a.variableComponents?a.variableComponents[m]:a.outputComponent,a.dispatchLayout.x.length===t.shape.length)).join(` +`);p.push(c),p.push(a.getUserCode());let d=z5(a);return p.push(D5(d,a)),p.join(` +`)}function ble(e,t,a){let n=e.shaderKey;if(e.pixelsOpType!=null)return n;let r=[],s=[];t.forEach(p=>{r.push(p.shape),s.push(p.dtype)}),r.push(a.shape),s.push(a.dtype);let i=t.map(p=>C.getBroadcastDims(p.shape,a.shape)),o=t.map(p=>v.arraysEqual(p.shape,a.shape)).join("_"),l=i.map(p=>p.join("_")).join(";"),u=ik(e)?"flatDispatch":"";return n+="_"+(e.workgroupSize?e.workgroupSize.join(","):"")+r.map(p=>p.length).join(",")+s.join(",")+e.variableNames.join(",")+l+o+u,n}var O5=` struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32}; struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32}; @@ -5119,7 +5119,7 @@ return a / b;`,NJ=` fn isinf(val: f32) -> bool { return abs(val) == uniforms.INFINITY; } -`;function lh(e,t=""){let a=e.length,n=t!==""?`get${t.charAt(0).toUpperCase()+t.slice(1)}CoordsFromIndex`:"getCoordsFromIndex",r=t!==""?`${t.charAt(0).toLowerCase()+t.slice(1)}ShapeStrides`:"outShapeStrides";if(a<=1)return`fn ${n}(index : i32) -> i32 { return index; }`;let s=v.computeStrides(e),i=Pt(a),o=[];for(let u=0;u vec2 { +`;function Vc(e,t=""){let a=e.length,n=t!==""?`get${t.charAt(0).toUpperCase()+t.slice(1)}CoordsFromIndex`:"getCoordsFromIndex",r=t!==""?`${t.charAt(0).toLowerCase()+t.slice(1)}ShapeStrides`:"outShapeStrides";if(a<=1)return`fn ${n}(index : i32) -> i32 { return index; }`;let s=v.computeStrides(e),i=Pt(a),o=[];for(let u=0;u vec2 { let d0 = index / uniforms.${r}; let d1 = index - d0 * uniforms.${r}; return vec2(d0, d1); }`;let l;return l="var index2 = index;"+s.map((u,p)=>{let c=`let ${o[p]} = index2 / uniforms.${r}.${Cr(p)}`,d=p===s.length-1?`let ${o[p+1]} = index2 - ${o[p]} * uniforms.${r}.${Cr(p)}`:`index2 = index2 - ${o[p]} * uniforms.${r}.${Cr(p)}`;return`${c}; ${d};`}).join(""),` @@ -5205,7 +5205,7 @@ return a / b;`,NJ=` coords.u * uniforms.outShapeStrides.u + coords.v; } - `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Ck(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function Hs(e,t=1){if(e==="float32")return Xe(t,"f32");if(e==="int32"||e==="bool")return Xe(t,"i32");throw new Error(`type ${e} is not supported.`)}function Tle(e,t,a){let n=e.length,r=Hs(t,a),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Xe(a)}) { + `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function ik(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function Gs(e,t=1){if(e==="float32")return Xe(t,"f32");if(e==="int32"||e==="bool")return Xe(t,"i32");throw new Error(`type ${e} is not supported.`)}function Tle(e,t,a){let n=e.length,r=Gs(t,a),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Xe(a)}) { result[flatIndex] = ${r}(value); } @@ -5221,7 +5221,7 @@ return a / b;`,NJ=` let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")})); setOutputAtIndexI32(flatIndex${a===1?"":` / ${a}`}, value); } - `}return s}function Nle(e){let t=/(\w+)\s*:\s*vec(5|6)/g;e=e.replace(t,n=>"@align(16) "+n);let a=/vec(5|6)\s*,\s*(\w+)/g;return e=e.replace(a,(n,r,s)=>`vec${r}, @align(16) ${s}`),e}function rA(e){return!(e.dispatchLayout.hasOwnProperty("y")&&e.dispatchLayout.y.length!==0||e.dispatchLayout.hasOwnProperty("z")&&e.dispatchLayout.z.length!==0)}var Tk={};Ze(Tk,{GPUBytesPerElement:()=>q1,MatMulProgramType:()=>On,assertNotComplex:()=>q3,computeDispatch:()=>de,computeWorkPerThreadForConv2d:()=>H3,computeWorkgroupInfoForMatMul:()=>Nk,computeWorkgroupSizeForConv2d:()=>G3,flatDispatchLayout:()=>me,isWebGPUSupported:()=>j3,tilesFitEvenlyIntoShape:()=>Rle});var Xs=e=>{let t=1;for(let a=0;aa%e[n]===0)}function de(e,t,a=[1,1,1],n=[1,1,1]){let[r,s,i]=[Math.ceil(Xs(e.x.map(o=>t[o]))/(a[0]*n[0])),e.y?Math.ceil(Xs(e.y.map(o=>t[o]))/(a[1]*n[1])):1,e.z?Math.ceil(Xs(e.z.map(o=>t[o]))/(a[2]*n[2])):1];return[r,s,i]}function Nk(e,t,a,n=!1){let r=[8,8,1],s=[4,4,1];return n||(e<=8&&(s[1]=1),t<=16&&a<=16&&(r[0]=4)),{workgroupSize:r,elementsPerThread:s}}function G3(e,t,a=!1){if(a)return[8,8,1];let n=Xs(e.x.map(s=>t[s])),r=Xs(e.y.map(s=>t[s]));return n<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function H3(e,t,a=!1){if(a)return[4,4,1];let n=Xs(e.x.map(s=>t[s])),r=Xs(e.y.map(s=>t[s]));return n<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function me(e){return{x:e.map((t,a)=>a)}}function q1(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function j3(){return!!(typeof globalThis!="undefined"&&globalThis.navigator&&globalThis.navigator.gpu)}function q3(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var On;(function(e){e[e.MatMulReduceProgram=0]="MatMulReduceProgram",e[e.MatMulSplitKProgram=1]="MatMulSplitKProgram",e[e.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",e[e.MatMulPackedProgram=3]="MatMulPackedProgram",e[e.MatMulMax=4]="MatMulMax"})(On||(On={}));var Ele=B().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Mle=(e,t)=>{let a=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=a))return r;v.assert(r[0]>a&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(r[0]));return s>a?(s=Math.ceil(Math.cbrt(r[0])),v.assert(s<=a,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},X3=class Rk extends su{nextDataId(){return Rk.nextDataId++}constructor(t,a){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchCountInPass=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.queryResolveBuffer=null,this.querySet=null,this.querySetCount=2,this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,this.hasReadSyncWarned=!1,this.hasTimestampQueryWarned=!1,!j3())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=t,this.queue=t.queue,this.commandEncoder=null,this.computePassEncoder=null,this.adapterInfo=new hle(a),this.supportTimestampQuery=this.device.features.has("timestamp-query"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new mle(this.device),this.textureManager=new fle(this.device),this.tensorMap=new op(this,It()),B().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:t,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}disposeData(t,a=!1){if(!this.tensorMap.has(t))return!0;let n=this.tensorMap.get(t);return a?n.refCount=0:n.refCount--,n.refCount>0?!1:(n.complexTensorInfos!=null&&(this.disposeData(n.complexTensorInfos.real.dataId),this.disposeData(n.complexTensorInfos.imag.dataId)),this.commandQueueOwnedIds.has(t)?(this.tensorDataPendingDisposal.push(t),!0):(this.releaseResource(t),this.tensorMap.delete(t),!0))}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(t){let a=this.tensorMap.get(t);if(!(!a||!a.resource)){if(a.external){a.resource=null;return}a.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(a.resource):a.resource instanceof GPUTexture&&this.textureManager.releaseTexture(a.resource),a.resource=null}}refCount(t){return this.tensorMap.has(t)?this.tensorMap.get(t).refCount:0}incRef(t){let a=this.tensorMap.get(t);a.refCount++}decRef(t){if(this.tensorMap.has(t)){let a=this.tensorMap.get(t);a.refCount--}}write(t,a,n){if(n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.tensorMap.set(r,{dtype:n,shape:a,values:t,refCount:1}),r}move(t,a,n,r,s){if(r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(t,{dtype:r,shape:n,values:a,refCount:s})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(t=>{this.releaseResource(t),this.tensorMap.delete(t)}),this.uniformPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t)),this.stagingPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let t;try{t=await Promise.all(Object.values(this.pipelineCache))}catch(a){throw new Error(a.message)}Object.keys(this.pipelineCache).map((a,n)=>{this.pipelineCache[a]=t[n]})}async getBufferData(t){if(B().getBool("WEBGPU_ENGINE_COMPILE_ONLY"))return console.warn("The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false"),null;let a=t.size,n=this.bufferManager.acquireBuffer(a,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,n,0,a),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let r=n.getMappedRange().slice(0);return n.unmap(),n!=null&&this.bufferManager.releaseBuffer(n),B().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),r}convertAndCacheOnCPU(t,a){let n=this.tensorMap.get(t);return n.values=a,n.values}readSync(t){let a=this.tensorMap.get(t),{values:n,complexTensorInfos:r}=a;if(n!=null||a.dtype==="string")return n;if(a.dtype==="complex64"){let f=this.readSync(r.real.dataId),g=this.readSync(r.imag.dataId),y=v.convertBackendValuesAndArrayBuffer(C.mergeRealAndImagArrays(f,g).buffer,"float32");return this.convertAndCacheOnCPU(t,y),y}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn("The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead."));let s=["opaque","premultiplied"],i=a.resource,o=i.size;v.assert(o%4===0,()=>"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.");let l=o/4,u=new ArrayBuffer(o),p=256,c=256,d=s.map(f=>new OffscreenCanvas(p,c)),h=new OffscreenCanvas(p,c);this.endComputePassEncoder(),d.map((f,g)=>{let y=f.getContext("webgpu");return y.configure({device:this.device,format:"bgra8unorm",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),y.getCurrentTexture()}).map((f,g)=>{let y=p*4,x=(N,M,$)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:i,bytesPerRow:y,offset:$},{texture:f},{width:N,height:M}),this.submitQueue();let E=h.getContext("2d",{willReadFrequently:!0});E.clearRect(0,0,N,M),E.drawImage(d[g],0,0);let S=E.getImageData(0,0,N,M).data,_=s[g],O=new Uint8ClampedArray(u,$,N*M*4);for(let W=0;W0&&(x(b,w,I),I+=w*(p*4)),b=T%p,b>0&&x(b,1,I)});let m=v.convertBackendValuesAndArrayBuffer(u,a.dtype);return this.convertAndCacheOnCPU(t,m),m}async read(t){if(!this.tensorMap.has(t))throw new Error(`Tensor ${t} was not registered!`);let a=this.tensorMap.get(t),{values:n}=a;if(n!=null)return n;let r;if(a.dtype==="complex64"){let s=await Promise.all([this.read(a.complexTensorInfos.real.dataId),this.read(a.complexTensorInfos.imag.dataId)]),i=s[0],o=s[1];r=C.mergeRealAndImagArrays(i,o)}else{let s=await this.getBufferData(a.resource);r=v.convertBackendValuesAndArrayBuffer(s,a.dtype)}return this.convertAndCacheOnCPU(t,r),r}copyBuffer(t){let a=t.size,n=t.usage,r=this.bufferManager.acquireBuffer(a,n);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,r,0,a),this.submitQueue(),r}createTensorFromGPUData(t,a,n){let r=t.buffer;if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:n,shape:a,values:null,refCount:1,external:t.zeroCopy});let i=this.tensorMap.get(s),o=q1(i.dtype)*v.sizeFromShape(i.shape);if(t.buffer.sizev.decodeString(r));return _e(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return _e(t.shape,t.dtype,a)}async time(t){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --enable-dawn-features=allow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled."),this.hasTimestampQueryWarned=!0);let a=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=v.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=v.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=a,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},l=await Promise.all(s);return o.kernelMs=v.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}makeTensorInfo(t,a,n){return a==="string"&&n!=null&&n.length>0&&v.isString(n[0])&&(n=n.map(r=>v.encodeString(r))),{dataId:this.write(n,t,a),shape:t,dtype:a}}tensorToBinding(t){if(!t)return null;let a=this.tensorMap.get(t.dataId).resource;return a instanceof GPUBuffer?{buffer:a}:a instanceof GPUTexture?a.createView():a}uploadToGPU(t){let a=this.tensorMap.get(t);if(a.resource!=null)return;let n=q1(a.dtype)*v.sizeFromShape(a.shape),r,s=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(a.values){if(r=this.bufferManager.acquireBuffer(n,s,!0),r.mapState==="unmapped"){let i=this.bufferManager.acquireBuffer(n,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),o=i.getMappedRange();a.dtype==="int32"||a.dtype==="bool"?new Int32Array(o).set(a.values):new Float32Array(o).set(a.values),i.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(i,0,r,0,n),this.stagingPendingDisposal.push(i)}else{let i=r.getMappedRange();a.dtype==="int32"||a.dtype==="bool"?new Int32Array(i).set(a.values):new Float32Array(i).set(a.values),r.unmap()}a.values=null}else r=this.bufferManager.acquireBuffer(n,s);a.resource=r}makeUniforms(t){let a=0,n=0,r=[],s=1;t.forEach(l=>{l.data.length===0&&(l.data=[1]);let u;switch(l.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:v.assert(!1,()=>`Unsupported ${l.data.length}D shape`)}(n===5||n===6)&&(u=16),u>s&&(s=u),a=Math.ceil(a/u)*u,n=l.data.length,r.push(a),a+=l.data.length*4}),a=Math.ceil(a/s)*s;let i=new ArrayBuffer(a);t.forEach((l,u)=>{let p=r[u];l.type==="int32"?new Int32Array(i,p,l.data.length).set(l.data):l.type==="uint32"?new Uint32Array(i,p,l.data.length).set(l.data):new Float32Array(i,p,l.data.length).set(l.data)});let o=this.bufferManager.acquireBuffer(a,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(o,0,i,0,a),this.uniformPendingDisposal.push(o),{offset:0,size:a,buffer:o}}runWebGPUProgram(t,a,n,r,s){if(s||(s=this.makeTensorInfo(t.outputShape,n)),v.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=v.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),t.dispatch=Mle(this.device,t);let i=a.map((l,u)=>{if(l.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(l.dataId),{dtype:this.tensorMap.get(l.dataId).dtype,shape:l.shape,name:t.variableNames[u]}});t.shaderKey=ble(t,i,s);let o=B().getBool("WEBGPU_ENGINE_COMPILE_ONLY");return t.shaderKey in this.pipelineCache||(this.pipelineCache[t.shaderKey]=yle(this.device,t,i,s,o)),t.pipeline=this.pipelineCache[t.shaderKey],o||this.recordAndSubmit(t,s,a,r),s}recordAndSubmit(t,a,n,r){if(t.pipeline instanceof Promise)throw new Error("Please call checkCompileCompletionAsync to ensure parallel compilation is done!");let s=[],i=[],o="int32";if(t.pixelsOpType==null){s.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),i=n.concat(a).map(h=>h.shape);let d="int32";i.map(h=>{s.push({type:d,data:h});let m=v.computeStrides(h);s.push({type:d,data:m})})}else{let d=v.computeStrides(a.shape);s.push({type:o,data:d})}if(t.size){let d=v.sizeFromShape(t.outputShape);s.push({type:o,data:[t.outputComponent?d/t.outputComponent:d]})}r&&(s=[...s,...r]);let l=[this.tensorToBinding(a),...n.map(d=>this.tensorToBinding(d)),this.makeUniforms(s)];n.forEach(d=>{this.commandQueueOwnedIds.add(d.dataId)}),this.commandQueueOwnedIds.add(a.dataId);let u=this.device.createBindGroup({layout:t.pipeline.getBindGroupLayout(0),entries:l.map((d,h)=>({binding:h,resource:d}))}),p=this.activeTimers!=null;this.ensureCommandEncoderReady();let c={};p&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.querySetCount})),c.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:0,endOfPassWriteIndex:1},this.computePassEncoder=this.commandEncoder.beginComputePass(c)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(c)),this.computePassEncoder.setPipeline(t.pipeline),this.computePassEncoder.setBindGroup(0,u),this.computePassEncoder.dispatchWorkgroups(t.dispatch[0],t.dispatch[1],t.dispatch[2]),this.dispatchCountInPass++,(p||B().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchCountInPass||t.pixelsOpType===nu.DRAW)&&(this.endComputePassEncoder(),p?this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let t=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.querySetCount*8),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let a=new BigUint64Array(t.getMappedRange()),n=Number(a[1]-a[0])/1e6;return t.unmap(),this.bufferManager.releaseBuffer(t),n}shouldExecuteOnCPU(t,a=Ele){return B().getBool("WEBGPU_CPU_FORWARD")&&t.every(n=>this.tensorMap.get(n.dataId).resource==null&&v.sizeFromShape(n.shape){let e={powerPreference:B().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),a={},n=[];t.features.has("timestamp-query")&&n.push("timestamp-query"),t.features.has("bgra8unorm-storage")&&n.push(["bgra8unorm-storage"]),a.requiredFeatures=n;let r=t.limits;a.requiredLimits={maxComputeWorkgroupStorageSize:r.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:r.maxStorageBufferBindingSize,maxBufferSize:r.maxBufferSize,maxComputeWorkgroupSizeX:r.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:r.maxComputeInvocationsPerWorkgroup};let s=await t.requestDevice(a),i="info"in t?t.info:"requestAdapterInfo"in t?await t.requestAdapterInfo():void 0;return new X3(s,i)},3);var Pe;(function(e){e[e.ADD=0]="ADD",e[e.ATAN2=1]="ATAN2",e[e.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",e[e.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",e[e.DIV=4]="DIV",e[e.ELU_DER=5]="ELU_DER",e[e.EQUAL=6]="EQUAL",e[e.FLOOR_DIV=7]="FLOOR_DIV",e[e.GREATER=8]="GREATER",e[e.GREATER_EQUAL=9]="GREATER_EQUAL",e[e.LESS=10]="LESS",e[e.LESS_EQUAL=11]="LESS_EQUAL",e[e.LOGICAL_AND=12]="LOGICAL_AND",e[e.LOGICAL_OR=13]="LOGICAL_OR",e[e.MAX=14]="MAX",e[e.MIN=15]="MIN",e[e.MOD=16]="MOD",e[e.MUL=17]="MUL",e[e.NOT_EQUAL=18]="NOT_EQUAL",e[e.POW=19]="POW",e[e.PRELU=20]="PRELU",e[e.SQUARED_DIFFERENCE=21]="SQUARED_DIFFERENCE",e[e.SUB=22]="SUB"})(Pe||(Pe={}));var $le="let resultTemp = a + b;",Ple="let resultTemp = atan2(a, b);",_le="let resultTemp = areal * breal - aimag * bimag;",Fle="let resultTemp = areal * bimag + aimag * breal;",Dle="let resultTemp = a / b;",Ole="let resultTemp = select(a * (b + 1.0), a, b >= b - b);",zle=` + `}return s}function Nle(e){let t=/(\w+)\s*:\s*vec(5|6)/g;e=e.replace(t,n=>"@align(16) "+n);let a=/vec(5|6)\s*,\s*(\w+)/g;return e=e.replace(a,(n,r,s)=>`vec${r}, @align(16) ${s}`),e}function z5(e){return!(e.dispatchLayout.hasOwnProperty("y")&&e.dispatchLayout.y.length!==0||e.dispatchLayout.hasOwnProperty("z")&&e.dispatchLayout.z.length!==0)}var ok={};Ze(ok,{GPUBytesPerElement:()=>T1,MatMulProgramType:()=>On,assertNotComplex:()=>T3,computeDispatch:()=>de,computeWorkPerThreadForConv2d:()=>S3,computeWorkgroupInfoForMatMul:()=>lk,computeWorkgroupSizeForConv2d:()=>I3,flatDispatchLayout:()=>me,isWebGPUSupported:()=>C3,tilesFitEvenlyIntoShape:()=>Rle});var qs=e=>{let t=1;for(let a=0;aa%e[n]===0)}function de(e,t,a=[1,1,1],n=[1,1,1]){let[r,s,i]=[Math.ceil(qs(e.x.map(o=>t[o]))/(a[0]*n[0])),e.y?Math.ceil(qs(e.y.map(o=>t[o]))/(a[1]*n[1])):1,e.z?Math.ceil(qs(e.z.map(o=>t[o]))/(a[2]*n[2])):1];return[r,s,i]}function lk(e,t,a,n=!1){let r=[8,8,1],s=[4,4,1];return n||(e<=8&&(s[1]=1),t<=16&&a<=16&&(r[0]=4)),{workgroupSize:r,elementsPerThread:s}}function I3(e,t,a=!1){if(a)return[8,8,1];let n=qs(e.x.map(s=>t[s])),r=qs(e.y.map(s=>t[s]));return n<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function S3(e,t,a=!1){if(a)return[4,4,1];let n=qs(e.x.map(s=>t[s])),r=qs(e.y.map(s=>t[s]));return n<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function me(e){return{x:e.map((t,a)=>a)}}function T1(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function C3(){return!!(typeof globalThis!="undefined"&&globalThis.navigator&&globalThis.navigator.gpu)}function T3(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGPU backend.`)})}var On;(function(e){e[e.MatMulReduceProgram=0]="MatMulReduceProgram",e[e.MatMulSplitKProgram=1]="MatMulSplitKProgram",e[e.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",e[e.MatMulPackedProgram=3]="MatMulPackedProgram",e[e.MatMulMax=4]="MatMulMax"})(On||(On={}));var Ele=B().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Mle=(e,t)=>{let a=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=a))return r;v.assert(r[0]>a&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(r[0]));return s>a?(s=Math.ceil(Math.cbrt(r[0])),v.assert(s<=a,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},N3=class uk extends Vl{nextDataId(){return uk.nextDataId++}constructor(t,a){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchCountInPass=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.queryResolveBuffer=null,this.querySet=null,this.querySetCount=2,this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,this.hasReadSyncWarned=!1,this.hasTimestampQueryWarned=!1,!C3())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=t,this.queue=t.queue,this.commandEncoder=null,this.computePassEncoder=null,this.adapterInfo=new hle(a),this.supportTimestampQuery=this.device.features.has("timestamp-query"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new mle(this.device),this.textureManager=new fle(this.device),this.tensorMap=new Vd(this,It()),B().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:t,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}disposeData(t,a=!1){if(!this.tensorMap.has(t))return!0;let n=this.tensorMap.get(t);return a?n.refCount=0:n.refCount--,n.refCount>0?!1:(n.complexTensorInfos!=null&&(this.disposeData(n.complexTensorInfos.real.dataId),this.disposeData(n.complexTensorInfos.imag.dataId)),this.commandQueueOwnedIds.has(t)?(this.tensorDataPendingDisposal.push(t),!0):(this.releaseResource(t),this.tensorMap.delete(t),!0))}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(t){let a=this.tensorMap.get(t);if(!(!a||!a.resource)){if(a.external){a.resource=null;return}a.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(a.resource):a.resource instanceof GPUTexture&&this.textureManager.releaseTexture(a.resource),a.resource=null}}refCount(t){return this.tensorMap.has(t)?this.tensorMap.get(t).refCount:0}incRef(t){let a=this.tensorMap.get(t);a.refCount++}decRef(t){if(this.tensorMap.has(t)){let a=this.tensorMap.get(t);a.refCount--}}write(t,a,n){if(n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.tensorMap.set(r,{dtype:n,shape:a,values:t,refCount:1}),r}move(t,a,n,r,s){if(r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(t,{dtype:r,shape:n,values:a,refCount:s})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(t=>{this.releaseResource(t),this.tensorMap.delete(t)}),this.uniformPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t)),this.stagingPendingDisposal.forEach(t=>this.bufferManager.releaseBuffer(t,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let t;try{t=await Promise.all(Object.values(this.pipelineCache))}catch(a){throw new Error(a.message)}Object.keys(this.pipelineCache).map((a,n)=>{this.pipelineCache[a]=t[n]})}async getBufferData(t){if(B().getBool("WEBGPU_ENGINE_COMPILE_ONLY"))return console.warn("The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false"),null;let a=t.size,n=this.bufferManager.acquireBuffer(a,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,n,0,a),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let r=n.getMappedRange().slice(0);return n.unmap(),n!=null&&this.bufferManager.releaseBuffer(n),B().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),r}convertAndCacheOnCPU(t,a){let n=this.tensorMap.get(t);return n.values=a,n.values}readSync(t){let a=this.tensorMap.get(t),{values:n,complexTensorInfos:r}=a;if(n!=null||a.dtype==="string")return n;if(a.dtype==="complex64"){let f=this.readSync(r.real.dataId),g=this.readSync(r.imag.dataId),y=v.convertBackendValuesAndArrayBuffer(C.mergeRealAndImagArrays(f,g).buffer,"float32");return this.convertAndCacheOnCPU(t,y),y}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn("The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead."));let s=["opaque","premultiplied"],i=a.resource,o=i.size;v.assert(o%4===0,()=>"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.");let l=o/4,u=new ArrayBuffer(o),p=256,c=256,d=s.map(f=>new OffscreenCanvas(p,c)),h=new OffscreenCanvas(p,c);this.endComputePassEncoder(),d.map((f,g)=>{let y=f.getContext("webgpu");return y.configure({device:this.device,format:"bgra8unorm",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),y.getCurrentTexture()}).map((f,g)=>{let y=p*4,x=(N,M,$)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:i,bytesPerRow:y,offset:$},{texture:f},{width:N,height:M}),this.submitQueue();let E=h.getContext("2d",{willReadFrequently:!0});E.clearRect(0,0,N,M),E.drawImage(d[g],0,0);let S=E.getImageData(0,0,N,M).data,_=s[g],O=new Uint8ClampedArray(u,$,N*M*4);for(let W=0;W0&&(x(b,w,I),I+=w*(p*4)),b=T%p,b>0&&x(b,1,I)});let m=v.convertBackendValuesAndArrayBuffer(u,a.dtype);return this.convertAndCacheOnCPU(t,m),m}async read(t){if(!this.tensorMap.has(t))throw new Error(`Tensor ${t} was not registered!`);let a=this.tensorMap.get(t),{values:n}=a;if(n!=null)return n;let r;if(a.dtype==="complex64"){let s=await Promise.all([this.read(a.complexTensorInfos.real.dataId),this.read(a.complexTensorInfos.imag.dataId)]),i=s[0],o=s[1];r=C.mergeRealAndImagArrays(i,o)}else{let s=await this.getBufferData(a.resource);r=v.convertBackendValuesAndArrayBuffer(s,a.dtype)}return this.convertAndCacheOnCPU(t,r),r}copyBuffer(t){let a=t.size,n=t.usage,r=this.bufferManager.acquireBuffer(a,n);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(t,0,r,0,a),this.submitQueue(),r}createTensorFromGPUData(t,a,n){let r=t.buffer;if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. ");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:n,shape:a,values:null,refCount:1,external:t.zeroCopy});let i=this.tensorMap.get(s),o=T1(i.dtype)*v.sizeFromShape(i.shape);if(t.buffer.sizev.decodeString(r));return _e(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return _e(t.shape,t.dtype,a)}async time(t){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --enable-dawn-features=allow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled."),this.hasTimestampQueryWarned=!0);let a=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=v.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=v.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=a,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},l=await Promise.all(s);return o.kernelMs=v.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}makeTensorInfo(t,a,n){return a==="string"&&n!=null&&n.length>0&&v.isString(n[0])&&(n=n.map(r=>v.encodeString(r))),{dataId:this.write(n,t,a),shape:t,dtype:a}}tensorToBinding(t){if(!t)return null;let a=this.tensorMap.get(t.dataId).resource;return a instanceof GPUBuffer?{buffer:a}:a instanceof GPUTexture?a.createView():a}uploadToGPU(t){let a=this.tensorMap.get(t);if(a.resource!=null)return;let n=T1(a.dtype)*v.sizeFromShape(a.shape),r,s=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(a.values){if(r=this.bufferManager.acquireBuffer(n,s,!0),r.mapState==="unmapped"){let i=this.bufferManager.acquireBuffer(n,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),o=i.getMappedRange();a.dtype==="int32"||a.dtype==="bool"?new Int32Array(o).set(a.values):new Float32Array(o).set(a.values),i.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(i,0,r,0,n),this.stagingPendingDisposal.push(i)}else{let i=r.getMappedRange();a.dtype==="int32"||a.dtype==="bool"?new Int32Array(i).set(a.values):new Float32Array(i).set(a.values),r.unmap()}a.values=null}else r=this.bufferManager.acquireBuffer(n,s);a.resource=r}makeUniforms(t){let a=0,n=0,r=[],s=1;t.forEach(l=>{l.data.length===0&&(l.data=[1]);let u;switch(l.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:v.assert(!1,()=>`Unsupported ${l.data.length}D shape`)}(n===5||n===6)&&(u=16),u>s&&(s=u),a=Math.ceil(a/u)*u,n=l.data.length,r.push(a),a+=l.data.length*4}),a=Math.ceil(a/s)*s;let i=new ArrayBuffer(a);t.forEach((l,u)=>{let p=r[u];l.type==="int32"?new Int32Array(i,p,l.data.length).set(l.data):l.type==="uint32"?new Uint32Array(i,p,l.data.length).set(l.data):new Float32Array(i,p,l.data.length).set(l.data)});let o=this.bufferManager.acquireBuffer(a,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(o,0,i,0,a),this.uniformPendingDisposal.push(o),{offset:0,size:a,buffer:o}}runWebGPUProgram(t,a,n,r,s){if(s||(s=this.makeTensorInfo(t.outputShape,n)),v.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=v.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),t.dispatch=Mle(this.device,t);let i=a.map((l,u)=>{if(l.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(l.dataId),{dtype:this.tensorMap.get(l.dataId).dtype,shape:l.shape,name:t.variableNames[u]}});t.shaderKey=ble(t,i,s);let o=B().getBool("WEBGPU_ENGINE_COMPILE_ONLY");return t.shaderKey in this.pipelineCache||(this.pipelineCache[t.shaderKey]=yle(this.device,t,i,s,o)),t.pipeline=this.pipelineCache[t.shaderKey],o||this.recordAndSubmit(t,s,a,r),s}recordAndSubmit(t,a,n,r){if(t.pipeline instanceof Promise)throw new Error("Please call checkCompileCompletionAsync to ensure parallel compilation is done!");let s=[],i=[],o="int32";if(t.pixelsOpType==null){s.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),i=n.concat(a).map(h=>h.shape);let d="int32";i.map(h=>{s.push({type:d,data:h});let m=v.computeStrides(h);s.push({type:d,data:m})})}else{let d=v.computeStrides(a.shape);s.push({type:o,data:d})}if(t.size){let d=v.sizeFromShape(t.outputShape);s.push({type:o,data:[t.outputComponent?d/t.outputComponent:d]})}r&&(s=[...s,...r]);let l=[this.tensorToBinding(a),...n.map(d=>this.tensorToBinding(d)),this.makeUniforms(s)];n.forEach(d=>{this.commandQueueOwnedIds.add(d.dataId)}),this.commandQueueOwnedIds.add(a.dataId);let u=this.device.createBindGroup({layout:t.pipeline.getBindGroupLayout(0),entries:l.map((d,h)=>({binding:h,resource:d}))}),p=this.activeTimers!=null;this.ensureCommandEncoderReady();let c={};p&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.querySetCount})),c.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:0,endOfPassWriteIndex:1},this.computePassEncoder=this.commandEncoder.beginComputePass(c)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(c)),this.computePassEncoder.setPipeline(t.pipeline),this.computePassEncoder.setBindGroup(0,u),this.computePassEncoder.dispatchWorkgroups(t.dispatch[0],t.dispatch[1],t.dispatch[2]),this.dispatchCountInPass++,(p||B().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchCountInPass||t.pixelsOpType===Wl.DRAW)&&(this.endComputePassEncoder(),p?this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let t=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,t,0,this.querySetCount*8),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let a=new BigUint64Array(t.getMappedRange()),n=Number(a[1]-a[0])/1e6;return t.unmap(),this.bufferManager.releaseBuffer(t),n}shouldExecuteOnCPU(t,a=Ele){return B().getBool("WEBGPU_CPU_FORWARD")&&t.every(n=>this.tensorMap.get(n.dataId).resource==null&&v.sizeFromShape(n.shape){let e={powerPreference:B().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),a={},n=[];t.features.has("timestamp-query")&&n.push("timestamp-query"),t.features.has("bgra8unorm-storage")&&n.push(["bgra8unorm-storage"]),a.requiredFeatures=n;let r=t.limits;a.requiredLimits={maxComputeWorkgroupStorageSize:r.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:r.maxStorageBufferBindingSize,maxBufferSize:r.maxBufferSize,maxComputeWorkgroupSizeX:r.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:r.maxComputeInvocationsPerWorkgroup};let s=await t.requestDevice(a),i="info"in t?t.info:"requestAdapterInfo"in t?await t.requestAdapterInfo():void 0;return new N3(s,i)},3);var Pe;(function(e){e[e.ADD=0]="ADD",e[e.ATAN2=1]="ATAN2",e[e.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",e[e.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",e[e.DIV=4]="DIV",e[e.ELU_DER=5]="ELU_DER",e[e.EQUAL=6]="EQUAL",e[e.FLOOR_DIV=7]="FLOOR_DIV",e[e.GREATER=8]="GREATER",e[e.GREATER_EQUAL=9]="GREATER_EQUAL",e[e.LESS=10]="LESS",e[e.LESS_EQUAL=11]="LESS_EQUAL",e[e.LOGICAL_AND=12]="LOGICAL_AND",e[e.LOGICAL_OR=13]="LOGICAL_OR",e[e.MAX=14]="MAX",e[e.MIN=15]="MIN",e[e.MOD=16]="MOD",e[e.MUL=17]="MUL",e[e.NOT_EQUAL=18]="NOT_EQUAL",e[e.POW=19]="POW",e[e.PRELU=20]="PRELU",e[e.SQUARED_DIFFERENCE=21]="SQUARED_DIFFERENCE",e[e.SUB=22]="SUB"})(Pe||(Pe={}));var $le="let resultTemp = a + b;",Ple="let resultTemp = atan2(a, b);",_le="let resultTemp = areal * breal - aimag * bimag;",Fle="let resultTemp = areal * bimag + aimag * breal;",Dle="let resultTemp = a / b;",Ole="let resultTemp = select(a * (b + 1.0), a, b >= b - b);",zle=` let zero = sign(a) * 0 + 0; let one = sign(b) * 0 + 1; let resultTemp = select(zero, one, a == b); @@ -5306,7 +5306,7 @@ return a / b;`,NJ=` `,nue="if (a < 0.0) { return b * a; } return a;",rue=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`,sue="let resultTemp = (a - b) * (a - b);",iue="let resultTemp = a - b;";function K3(e,t){let a;do{switch(e){case Pe.ATAN2:a=Ple;break;case Pe.MAX:a=Xle;break;case Pe.MIN:a=Kle;break;case Pe.MOD:a=t?Zle:Yle;break;case Pe.NOT_EQUAL:a=t?eue:Qle;break;case Pe.POW:a=t?aue:tue;break;default:continue}let n,r,s;return t?(n="isnanVec4",r="vec4",s="vec4"):(n="isnan",r="f32",s="bool"),` +`,sue="let resultTemp = (a - b) * (a - b);",iue="let resultTemp = a - b;";function R3(e,t){let a;do{switch(e){case Pe.ATAN2:a=Ple;break;case Pe.MAX:a=Xle;break;case Pe.MIN:a=Kle;break;case Pe.MOD:a=t?Zle:Yle;break;case Pe.NOT_EQUAL:a=t?eue:Qle;break;case Pe.POW:a=t?aue:tue;break;default:continue}let n,r,s;return t?(n="isnanVec4",r="vec4",s="vec4"):(n="isnan",r="f32",s="bool"),` let aIsNaN = ${n}(a); let aPostLegalization = select(a, ${r}(42), aIsNaN); let bIsNaN = ${n}(b); @@ -5430,17 +5430,17 @@ return a / b;`,NJ=` `,Kue="return tan(a);",Yue=` let e2x = exp(-2.0 * abs(a)); return sign(a) * (1.0 - e2x) / (1.0 + e2x); -`,Zue="return f32(i32((a)));";function Ws(e,t){switch(e){case le.ABS:return oue;case le.ACOS:return lue;case le.ACOSH:return uue;case le.ASIN:return due;case le.ASINH:return pue;case le.ATAN:return cue;case le.ATANH:return hue;case le.COS:return fue;case le.COSH:return gue;case le.CEIL:return mue;case le.ELU:return t?Aue:xue;case le.ERF:return bue;case le.EXP:return vue;case le.EXPM1:return yue;case le.FLOOR:return wue;case le.IS_FINITE:return kue;case le.IS_INF:return Iue;case le.IS_NAN:return Sue;case le.LINEAR:return Cue;case le.LOG:return Tue;case le.LOG1P:return Nue;case le.LOGICAL_NOT:return Rue;case le.NEG:return Eue;case le.LEAKYRELU:return t?$ue:Mue;case le.RECIPROCAL:return Pue;case le.RELU:return t?Oue:_ue;case le.RELU6:return t?Due:Fue;case le.ROUND:return zue;case le.RSQRT:return Lue;case le.SELU:return Wue;case le.SIGMOID:return Bue;case le.SIGN:return Vue;case le.SIN:return Uue;case le.SINH:return Gue;case le.SOFTPLUS:return Hue;case le.SQRT:return jue;case le.SQUARE:return que;case le.STEP:return Xue;case le.TAN:return Kue;case le.TANH:return Yue;case le.TO_INT:return Zue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Pr(e,t=!1,a=!1,n=3){if(e===null)return"";let r="";if(e==="linear")r=Ws(le.LINEAR);else if(e==="relu")r=Ws(le.RELU,a);else if(e==="elu")r=Ws(le.ELU,a);else if(e==="relu6")r=Ws(le.RELU6,a);else if(e==="prelu")r=K3(Pe.PRELU,a);else if(e==="sigmoid")r=Ws(le.SIGMOID,a);else if(e==="leakyrelu")r=Ws(le.LEAKYRELU,a);else throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);let s=Xe(a?4:1),i="";return t?i=` +`,Zue="return f32(i32((a)));";function Ls(e,t){switch(e){case le.ABS:return oue;case le.ACOS:return lue;case le.ACOSH:return uue;case le.ASIN:return due;case le.ASINH:return pue;case le.ATAN:return cue;case le.ATANH:return hue;case le.COS:return fue;case le.COSH:return gue;case le.CEIL:return mue;case le.ELU:return t?Aue:xue;case le.ERF:return bue;case le.EXP:return vue;case le.EXPM1:return yue;case le.FLOOR:return wue;case le.IS_FINITE:return kue;case le.IS_INF:return Iue;case le.IS_NAN:return Sue;case le.LINEAR:return Cue;case le.LOG:return Tue;case le.LOG1P:return Nue;case le.LOGICAL_NOT:return Rue;case le.NEG:return Eue;case le.LEAKYRELU:return t?$ue:Mue;case le.RECIPROCAL:return Pue;case le.RELU:return t?Oue:_ue;case le.RELU6:return t?Due:Fue;case le.ROUND:return zue;case le.RSQRT:return Lue;case le.SELU:return Wue;case le.SIGMOID:return Bue;case le.SIGN:return Vue;case le.SIN:return Uue;case le.SINH:return Gue;case le.SOFTPLUS:return Hue;case le.SQRT:return jue;case le.SQUARE:return que;case le.STEP:return Xue;case le.TAN:return Kue;case le.TANH:return Yue;case le.TO_INT:return Zue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Pr(e,t=!1,a=!1,n=3){if(e===null)return"";let r="";if(e==="linear")r=Ls(le.LINEAR);else if(e==="relu")r=Ls(le.RELU,a);else if(e==="elu")r=Ls(le.ELU,a);else if(e==="relu6")r=Ls(le.RELU6,a);else if(e==="prelu")r=R3(Pe.PRELU,a);else if(e==="sigmoid")r=Ls(le.SIGMOID,a);else if(e==="leakyrelu")r=Ls(le.LEAKYRELU,a);else throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);let s=Xe(a?4:1),i="";return t?i=` fn activation(a : ${s}, coords : vec${n}) -> ${s} { let b = getPreluActivationWeightsByOutputCoords(coords); ${r} }`:i=` fn activation(a : ${s}, coords : vec${n}) -> ${s} { ${r} - }`,i}function ll(e,t){return` + }`,i}function Ho(e,t){return` ${e?"value = value + getBiasByOutputCoords(coords);":""} ${t?"value = activation(value, coords);":""} - `}function Ek(e,t,a=!1,n=!1,r=!1,s=1){v.assert(e&&s===1||!e,()=>`transposeA ${e} is not compatible with component size ${s}`);let i=` + `}function dk(e,t,a=!1,n=!1,r=!1,s=1){v.assert(e&&s===1||!e,()=>`transposeA ${e} is not compatible with component size ${s}`);let i=` ${e?"value = getA(batch, col, row);":"value = getA(batch, row, col);"} `,o=t?"value = getB(batch, col, row);":"value = getB(batch, row, col);";return` @@ -5460,14 +5460,14 @@ return a / b;`,NJ=` ${o} return value; } - `}function Y3(e,t,a,n,r=!1,s=!1,i=!1,o=1){return` - ${Ek(a,n,r,s,i,o)} + `}function E3(e,t,a,n,r=!1,s=!1,i=!1,o=1){return` + ${dk(a,n,r,s,i,o)} fn mm_write(batch: i32, row: i32, col: i32, valueIn: ${Xe(o)}) { ${r&&s?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} { var value = valueIn; let coords = vec3(batch, row, col); - ${ll(e,t)} + ${Ho(e,t)} setOutputAtCoords(coords[0], coords[1], coords[2], value); } } @@ -5493,7 +5493,7 @@ return a / b;`,NJ=` let ACached = mm_Asub[tileRow + i][k]; ${s} } - }`}};function d0(e,t,a=!1,n=32,r=!1,s=32,i=!1){let o=t[1]*e[1],l=t[0]*e[0],u=a?o:n,p=a?n:o,c=u/t[0],d=n/t[1],h=e[1],m=e[0];return v.assert((a&&c===4&&e[1]===4||!a&&(c===3||c===4))&&u%t[0]===0&&n%t[1]===0&&e[0]===4,()=>`If transposeA ${a} is true, innerElementSize ${c} and workPerThread[1] ${e[1]} must be 4. + }`}};function Gh(e,t,a=!1,n=32,r=!1,s=32,i=!1){let o=t[1]*e[1],l=t[0]*e[0],u=a?o:n,p=a?n:o,c=u/t[0],d=n/t[1],h=e[1],m=e[0];return v.assert((a&&c===4&&e[1]===4||!a&&(c===3||c===4))&&u%t[0]===0&&n%t[1]===0&&e[0]===4,()=>`If transposeA ${a} is true, innerElementSize ${c} and workPerThread[1] ${e[1]} must be 4. Otherwise, innerElementSize ${c} must be 3 or 4. tileAWidth ${u} must be divisible by workgroupSize[0]${t[0]}. tileInner ${n} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`),` var mm_Asub : array, ${u/c}>, ${p}>; @@ -5543,7 +5543,7 @@ return a / b;`,NJ=` for (var innerRow = 0; innerRow < ${h}; innerRow++) { mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); } - }`}var sA=e=>e?` + }`}var L5=e=>e?` mm_Asub[inputRow][inputCol] = mm_readA(batchA, kStart + inputRow, globalRowStart + inputCol); @@ -5551,7 +5551,7 @@ return a / b;`,NJ=` mm_Asub[inputRow][inputCol] = mm_readA(batchA, globalRowStart + inputRow, kStart + inputCol); - `,ede=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function p0(e,t,a=!1,n=32,r=!1,s=32,i=!1,o=!1){let l=e[1]*t[1],u=e[0]*t[0],p=a?l:n,c=a?n:l;v.assert(c%t[1]===0&&p%t[0]===0&&n%t[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}, tileInner ${n} must be divisible by workgroupSize[1]${t[1]}`);let d=c/t[1],h=p/t[0],m=n/t[1],f=e[1],g=e[0],y=i?` + `,ede=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Hh(e,t,a=!1,n=32,r=!1,s=32,i=!1,o=!1){let l=e[1]*t[1],u=e[0]*t[0],p=a?l:n,c=a?n:l;v.assert(c%t[1]===0&&p%t[0]===0&&n%t[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}, tileInner ${n} must be divisible by workgroupSize[1]${t[1]}`);let d=c/t[1],h=p/t[0],m=n/t[1],f=e[1],g=e[0],y=i?` let localRow = i32(localId.y); let localCol = i32(localId.x); let globalRowStart = i32(workgroupId.y) * ${l}; @@ -5562,7 +5562,7 @@ return a / b;`,NJ=` // Load one tile of A into local memory. for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${t[1]}) { for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${t[0]}) { - ${sA(a)} + ${L5(a)} } } // Load one tile of B into local memory. @@ -5617,7 +5617,7 @@ return a / b;`,NJ=` for (var innerCol = 0; innerCol < ${h}; innerCol++) { let inputRow = tileRowA + innerRow; let inputCol = tileColA + innerCol; - ${sA(a)} + ${L5(a)} } } @@ -5729,10 +5729,10 @@ return a / b;`,NJ=` mm_write(batch, globalRow, globalCol, acc); } - `}var nde=class{constructor(e,t,a=!1,n=!1,r=null,s=null,i=null,o=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=a?e[1]:e[2];if(this.isVec4=(l%4===0&&!a||t[1]%4===0&&a)&&t[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=t[1]===1&&!a,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let c=Nk(t[1],l,t[2],a);this.workgroupSize=c.workgroupSize,this.elementsPerThread=c.elementsPerThread}this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let u=r!=null,p=i!=null;u&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=o,this.transposeA=a,this.transposeB=n,this.addBias=u,this.activation=s,this.hasPreluActivationWeights=p,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],l),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${a}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,a){let n=this.workgroupSize[1]*this.elementsPerThread[1],r=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=r;let s=e%n===0,i=t%r===0,o=a%this.tileInner===0;return[s,i,o]}getUserCode(){return` + `}var nde=class{constructor(e,t,a=!1,n=!1,r=null,s=null,i=null,o=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=a?e[1]:e[2];if(this.isVec4=(l%4===0&&!a||t[1]%4===0&&a)&&t[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=t[1]===1&&!a,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let c=lk(t[1],l,t[2],a);this.workgroupSize=c.workgroupSize,this.elementsPerThread=c.elementsPerThread}this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let u=r!=null,p=i!=null;u&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=o,this.transposeA=a,this.transposeB=n,this.addBias=u,this.activation=s,this.hasPreluActivationWeights=p,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],l),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${a}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,a){let n=this.workgroupSize[1]*this.elementsPerThread[1],r=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=r;let s=e%n===0,i=t%r===0,o=a%this.tileInner===0;return[s,i,o]}getUserCode(){return` ${Pr(this.activation,this.hasPreluActivationWeights,this.isVec4)} - ${Y3(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} - ${this.isVec4?d0(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?ade(this.workgroupSize,this.transposeA):p0(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)} + ${E3(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} + ${this.isVec4?Gh(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?ade(this.workgroupSize,this.transposeA):Hh(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)} `}};function rde(e){return` var sumValues : array; ${ue()} { @@ -5768,7 +5768,7 @@ return a / b;`,NJ=` } `}var sde=class{constructor(e,t=!1,a=!1,n=null,r=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=a,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${a}`}getUserCode(){return` ${Pr(this.activation,this.hasPreluActivationWeights)} - ${Y3(this.addBias,this.activation,this.transposeA,this.transposeB)} + ${E3(this.addBias,this.activation,this.transposeA,this.transposeB)} ${rde(this.workgroupSize[0])} `}};function ide(e){let t=e[1],a=e[0],n=t>a?t:a;return` var mm_Asub : array, ${t}>; @@ -5824,10 +5824,10 @@ return a / b;`,NJ=` } `}var ode=class{constructor(e,t,a,n=!1,r=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=a,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(a[2]/this.workgroupSize[0]),Math.ceil(a[1]/this.workgroupSize[1]),a[0]];let l=s!=null;l&&this.variableNames.push("bias");let u=o!=null;u&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${r}`}getUserCode(){return` ${Pr(this.activation,this.hasPreluActivationWeights)} - ${Y3(this.addBias,this.activation,this.transposeA,this.transposeB)} + ${E3(this.addBias,this.activation,this.transposeA,this.transposeB)} ${ide(this.workgroupSize)} `}},lde=class{constructor(e,t,a=!1,n=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,v.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let r=(a&&this.outputShape[1]%4===0||!a&&t%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=r?4:1,r||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=de(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=a,this.transposeB=n,this.shaderKey=`matMulSplitK_${a}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let e=this.outputComponent;return` - ${Ek(!1,this.transposeB,!1,!1,!1,e)} + ${dk(!1,this.transposeB,!1,!1,!1,e)} fn mm_write(batch: i32, row : i32, col : i32, value : ${Xe(e)}) { if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { let coords = vec3(batch, row, col); @@ -5835,18 +5835,18 @@ return a / b;`,NJ=` // The problem is that we should initialize output to zero before using. // Otherwise, the original value will be added to the result. for (var i = 0; i < ${e}; i = i + 1) { - ${xs("&result[flatIndex + i]",`${e>1?"value[i]":"value"}`,"float32")} + ${ys("&result[flatIndex + i]",`${e>1?"value[i]":"value"}`,"float32")} } } } - ${e===4?d0(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):p0(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} + ${e===4?Gh(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Hh(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} `}},ude=class{constructor(e,t=null,a=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${a}`}getUserCode(){return` ${Pr(this.activation,this.hasPreluActivationWeights)} ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var value = getXByOutputIndex(index); - ${ll(this.addBias,this.activation)} + ${Ho(this.addBias,this.activation)} setOutputAtIndex(index, value); } } @@ -5856,10 +5856,10 @@ return a / b;`,NJ=` setOutputAtIndex(index, uniforms.value); } } - `}};function Wa(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new dde(n),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],s,o)}}var pde={kernelName:bu,backendName:"webgpu",kernelFunc:Wa};function ke(e){let{inputs:t,attrs:a}=e,{x:n}=t,{shape:r}=a,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(r,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var cde={kernelName:Eu,backendName:"webgpu",kernelFunc:ke};function c0({a:e,b:t,transposeA:a,transposeB:n,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,c=a?e.shape[u-2]:e.shape[u-1],d=n?t.shape[p-1]:t.shape[p-2],h=a?e.shape[u-1]:e.shape[u-2],m=n?t.shape[p-2]:t.shape[p-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(f),x=v.sizeFromShape(g),A=nl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,m]);v.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],w=n?[x,m,d]:[x,d,m],I=ke({inputs:{x:e},backend:r,attrs:{shape:b}}),T=ke({inputs:{x:t},backend:r,attrs:{shape:w}}),N=[I,T],M=Math.max(y,x),$=[I,T],E=[{type:"int32",data:[h]},{type:"int32",data:[m]},{type:"int32",data:[c]}],S,_,O=[M,h,m],W=B().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(W<0){let U=B().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),G=U>0?U:r.thresholdToIncreaseWorkgroups,q=M*Math.ceil(h/32)*Math.ceil(m/32);q<=G||h<=8&&q<=G*2?M*h*m<=128?W=On.MatMulReduceProgram:M===1&&d>=2e3?W=On.MatMulSplitKProgram:W=On.MatMulSmallOutputSizeProgram:W=On.MatMulPackedProgram}switch(W){case On.MatMulReduceProgram:S=new sde(O,a,n,s,l,i);break;case On.MatMulSplitKProgram:{if(_=Wa({backend:r,attrs:{shape:O,value:0,dtype:e.dtype}}),S=new lde(O,d,a,n),s||l){_=r.runWebGPUProgram(S,$,e.dtype,E,_);let G=new ude(_.shape,s,l,i),q=null,H=[_];s&&H.push(s),i&&H.push(i),l==="leakyrelu"&&(q=[{type:"float32",data:[o]}],G.uniforms+=" alpha : f32,");let V=r.runWebGPUProgram(G,H,_.dtype,q);N.push(_);let Z=ke({inputs:{x:V},backend:r,attrs:{shape:A}});N.push(V);for(let X of N)r.disposeData(X.dataId);return Z}break}case On.MatMulSmallOutputSizeProgram:S=new ode(b,w,O,a,n,s,l,i);break;case On.MatMulPackedProgram:let U=r.adapterInfo.isIntel();S=new nde(b,O,a,n,s,l,i,U);break;default:throw new Error(`Unsupported MatMulProgramType ${W}.`)}s&&$.push(s),i&&$.push(i),l==="leakyrelu"&&(E.push({type:"float32",data:[o]}),S.uniforms+=" alpha : f32,"),_=r.runWebGPUProgram(S,$,e.dtype,E,_);let P=ke({inputs:{x:_},backend:r,attrs:{shape:A}});N.push(_);for(let U of N)r.disposeData(U.dataId);return P}function hde(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return c0({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var mde={kernelName:Zr,backendName:"webgpu",kernelFunc:hde},iA=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,a),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` + `}};function Wa(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new dde(n),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],s,o)}}var pde={kernelName:au,backendName:"webgpu",kernelFunc:Wa};function ke(e){let{inputs:t,attrs:a}=e,{x:n}=t,{shape:r}=a,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(r,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var cde={kernelName:pu,backendName:"webgpu",kernelFunc:ke};function jh({a:e,b:t,transposeA:a,transposeB:n,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,p=t.shape.length,c=a?e.shape[u-2]:e.shape[u-1],d=n?t.shape[p-1]:t.shape[p-2],h=a?e.shape[u-1]:e.shape[u-2],m=n?t.shape[p-2]:t.shape[p-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(f),x=v.sizeFromShape(g),A=Wo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,m]);v.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],w=n?[x,m,d]:[x,d,m],I=ke({inputs:{x:e},backend:r,attrs:{shape:b}}),T=ke({inputs:{x:t},backend:r,attrs:{shape:w}}),N=[I,T],M=Math.max(y,x),$=[I,T],E=[{type:"int32",data:[h]},{type:"int32",data:[m]},{type:"int32",data:[c]}],S,_,O=[M,h,m],W=B().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(W<0){let U=B().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),G=U>0?U:r.thresholdToIncreaseWorkgroups,q=M*Math.ceil(h/32)*Math.ceil(m/32);q<=G||h<=8&&q<=G*2?M*h*m<=128?W=On.MatMulReduceProgram:M===1&&d>=2e3?W=On.MatMulSplitKProgram:W=On.MatMulSmallOutputSizeProgram:W=On.MatMulPackedProgram}switch(W){case On.MatMulReduceProgram:S=new sde(O,a,n,s,l,i);break;case On.MatMulSplitKProgram:{if(_=Wa({backend:r,attrs:{shape:O,value:0,dtype:e.dtype}}),S=new lde(O,d,a,n),s||l){_=r.runWebGPUProgram(S,$,e.dtype,E,_);let G=new ude(_.shape,s,l,i),q=null,H=[_];s&&H.push(s),i&&H.push(i),l==="leakyrelu"&&(q=[{type:"float32",data:[o]}],G.uniforms+=" alpha : f32,");let V=r.runWebGPUProgram(G,H,_.dtype,q);N.push(_);let Z=ke({inputs:{x:V},backend:r,attrs:{shape:A}});N.push(V);for(let X of N)r.disposeData(X.dataId);return Z}break}case On.MatMulSmallOutputSizeProgram:S=new ode(b,w,O,a,n,s,l,i);break;case On.MatMulPackedProgram:let U=r.adapterInfo.isIntel();S=new nde(b,O,a,n,s,l,i,U);break;default:throw new Error(`Unsupported MatMulProgramType ${W}.`)}s&&$.push(s),i&&$.push(i),l==="leakyrelu"&&(E.push({type:"float32",data:[o]}),S.uniforms+=" alpha : f32,"),_=r.runWebGPUProgram(S,$,e.dtype,E,_);let P=ke({inputs:{x:_},backend:r,attrs:{shape:A}});N.push(_);for(let U of N)r.disposeData(U.dataId);return P}function hde(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:c}=n;return jh({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:p})}var mde={kernelName:Zr,backendName:"webgpu",kernelFunc:hde},W5=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,a),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { - ${K3(this.op,!1)} + ${R3(this.op,!1)} } ${ue("index")} { @@ -5871,9 +5871,9 @@ return a / b;`,NJ=` setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag)); } } - `}},Th=class{constructor(e,t,a){if(this.size=!0,this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,a),this.dispatchLayout=me(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&a.length>1&&t[0]<128,this.useSharedMemoryWithB=a.length<=1&&t.length>1&&a[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?a[0]:t[0],this.shaderKey=`binary_${e}_${this.lastDimensionSize}`,this.type="shared",this.workgroupSize=[256,1,1];else{let n=t.length>0&&t[t.length-1]%4===0,r=a.length>0&&a[a.length-1]%4===0;n&&r?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(v.isScalarShape(a)||a[a.length-1]===1)||r&&(v.isScalarShape(t)||t[t.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type="nonshared",this.shaderKey=`binary_${e}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let e,t=this.outputComponent===4?"vec4":"f32",a=` + `}},oh=class{constructor(e,t,a){if(this.size=!0,this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,a),this.dispatchLayout=me(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&a.length>1&&t[0]<128,this.useSharedMemoryWithB=a.length<=1&&t.length>1&&a[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?a[0]:t[0],this.shaderKey=`binary_${e}_${this.lastDimensionSize}`,this.type="shared",this.workgroupSize=[256,1,1];else{let n=t.length>0&&t[t.length-1]%4===0,r=a.length>0&&a[a.length-1]%4===0;n&&r?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(v.isScalarShape(a)||a[a.length-1]===1)||r&&(v.isScalarShape(t)||t[t.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type="nonshared",this.shaderKey=`binary_${e}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let e,t=this.outputComponent===4?"vec4":"f32",a=` fn binaryOperation(a : ${t}, b : ${t}) -> ${t} { - ${K3(this.op,this.outputComponent===4)} + ${R3(this.op,this.outputComponent===4)} }; `;if(this.type==="shared"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",r=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index); let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}]; @@ -5904,9 +5904,9 @@ return a / b;`,NJ=` setOutputAtIndex(index, binaryOperation(a, b)); } } - `;return e}};function tn(e){let{inputs:t}=e,{x:a}=t;return e.backend.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var fde={kernelName:qi,backendName:"webgpu",kernelFunc:tn};function ul(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.tensorMap.get(s.dataId),o=tn({inputs:{x:n},backend:a}),l=tn({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var gde={kernelName:cp,backendName:"webgpu",kernelFunc:ul},td=class{constructor(e,t,a=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,a!==""&&(this.uniforms=a),this.shaderKey=`unary_${t}`}getUserCode(){return` + `;return e}};function tn(e){let{inputs:t}=e,{x:a}=t;return e.backend.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var fde={kernelName:Wi,backendName:"webgpu",kernelFunc:tn};function jo(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.tensorMap.get(s.dataId),o=tn({inputs:{x:n},backend:a}),l=tn({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var gde={kernelName:qd,backendName:"webgpu",kernelFunc:jo},Du=class{constructor(e,t,a=""){this.variableNames=["A"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,a!==""&&(this.uniforms=a),this.shaderKey=`unary_${t}`}getUserCode(){return` fn unaryOperation(a : f32) -> f32 { - ${Ws(this.op,!1)} + ${Ls(this.op,!1)} } ${ue("index")} { if (index < uniforms.size) { @@ -5914,7 +5914,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, unaryOperation(a)); } } - `}};function at({opType:e,cpuKernelImpl:t,dtype:a}){return({inputs:n,backend:r})=>{let{x:s}=n,i=r,o=a||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),p=t(u.values,o);return i.makeTensorInfo(s.shape,o,p)}let l=new td(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function aa({opType:e,cpuKernelImpl:t,supportsComplex:a=!1,dtype:n}){return({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(a&&i.dtype==="complex64"){let c=l.tensorMap.get(i.dataId),d=l.tensorMap.get(o.dataId),h,m;if(e!==Pe.MUL)[h,m]=[[c.complexTensorInfos.real,d.complexTensorInfos.real],[c.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[y,x]=g,A={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:x.dataId,dtype:x.dtype,shape:o.shape},w=new Th(e,i.shape,o.shape);return l.runWebGPUProgram(w,[A,b],ca(y.dtype,x.dtype))});else{let g=new iA(Pe.COMPLEX_MULTIPLY_REAL,i.shape,o.shape),y=new iA(Pe.COMPLEX_MULTIPLY_IMAG,i.shape,o.shape),x=[{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:i.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=l.runWebGPUProgram(g,x,"float32"),m=l.runWebGPUProgram(y,x,"float32")}let f=ul({inputs:{real:h,imag:m},backend:l});return l.disposeData(h.dataId),l.disposeData(m.dataId),f}let u=n||ca(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let c=l.tensorMap.get(i.dataId).values,d=l.tensorMap.get(o.dataId).values,h=i.dtype==="string"?C.fromUint8ToStringArray(c):c,m=i.dtype==="string"?C.fromUint8ToStringArray(d):d,[f,g]=t(i.shape,o.shape,h,m,u);return l.makeTensorInfo(g,u,f)}let p=new Th(e,i.shape,o.shape);return l.runWebGPUProgram(p,[i,o],u)}}var{addImpl:yde,castImpl:xde,ceilImpl:Ade,concatImpl:bde,equalImpl:vde,expImpl:wde,expm1Impl:kde,floorImpl:Ide,floorDivImpl:Sde,gatherNdImpl:Cde,gatherV2Impl:Tde,greaterEqualImpl:Nde,greaterImpl:Rde,lessEqualImpl:Ede,lessImpl:Mde,logImpl:$de,maxImpl:Pde,maximumImpl:_de,minimumImpl:Fde,multiplyImpl:Dde,negImpl:Ode,notEqualImpl:zde,prodImpl:Lde,rangeImpl:Wde,rsqrtImpl:Bde,scatterImpl:Vde,simpleAbsImpl:Ude,sliceImpl:Gde,stridedSliceImpl:Hde,stringNGramsImpl:jde,subImpl:qde,tileImpl:Xde,topKImpl:Kde,transposeImpl:Yde,uniqueImpl:cye}=t0,Zde=at({opType:le.ABS,cpuKernelImpl:Ude}),Jde={kernelName:ou,backendName:"webgpu",kernelFunc:Zde},Qde=at({opType:le.ACOS}),epe={kernelName:oi,backendName:"webgpu",kernelFunc:Qde},tpe=at({opType:le.ACOSH}),ape={kernelName:li,backendName:"webgpu",kernelFunc:tpe},npe=aa({opType:Pe.ADD,cpuKernelImpl:yde,supportsComplex:!0}),rpe={kernelName:ls,backendName:"webgpu",kernelFunc:npe},spe=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(a=>{e.push(`let v${a} = get${a}ByOutputCoords(coords);`)});let t=this.variableNames.map(a=>`v${a}`).join(" + ");return` + `}};function at({opType:e,cpuKernelImpl:t,dtype:a}){return({inputs:n,backend:r})=>{let{x:s}=n,i=r,o=a||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),p=t(u.values,o);return i.makeTensorInfo(s.shape,o,p)}let l=new Du(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function aa({opType:e,cpuKernelImpl:t,supportsComplex:a=!1,dtype:n}){return({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(a&&i.dtype==="complex64"){let c=l.tensorMap.get(i.dataId),d=l.tensorMap.get(o.dataId),h,m;if(e!==Pe.MUL)[h,m]=[[c.complexTensorInfos.real,d.complexTensorInfos.real],[c.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[y,x]=g,A={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:x.dataId,dtype:x.dtype,shape:o.shape},w=new oh(e,i.shape,o.shape);return l.runWebGPUProgram(w,[A,b],ca(y.dtype,x.dtype))});else{let g=new W5(Pe.COMPLEX_MULTIPLY_REAL,i.shape,o.shape),y=new W5(Pe.COMPLEX_MULTIPLY_IMAG,i.shape,o.shape),x=[{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:i.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=l.runWebGPUProgram(g,x,"float32"),m=l.runWebGPUProgram(y,x,"float32")}let f=jo({inputs:{real:h,imag:m},backend:l});return l.disposeData(h.dataId),l.disposeData(m.dataId),f}let u=n||ca(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let c=l.tensorMap.get(i.dataId).values,d=l.tensorMap.get(o.dataId).values,h=i.dtype==="string"?C.fromUint8ToStringArray(c):c,m=i.dtype==="string"?C.fromUint8ToStringArray(d):d,[f,g]=t(i.shape,o.shape,h,m,u);return l.makeTensorInfo(g,u,f)}let p=new oh(e,i.shape,o.shape);return l.runWebGPUProgram(p,[i,o],u)}}var{addImpl:yde,castImpl:xde,ceilImpl:Ade,concatImpl:bde,equalImpl:vde,expImpl:wde,expm1Impl:kde,floorImpl:Ide,floorDivImpl:Sde,gatherNdImpl:Cde,gatherV2Impl:Tde,greaterEqualImpl:Nde,greaterImpl:Rde,lessEqualImpl:Ede,lessImpl:Mde,logImpl:$de,maxImpl:Pde,maximumImpl:_de,minimumImpl:Fde,multiplyImpl:Dde,negImpl:Ode,notEqualImpl:zde,prodImpl:Lde,rangeImpl:Wde,rsqrtImpl:Bde,scatterImpl:Vde,simpleAbsImpl:Ude,sliceImpl:Gde,stridedSliceImpl:Hde,stringNGramsImpl:jde,subImpl:qde,tileImpl:Xde,topKImpl:Kde,transposeImpl:Yde,uniqueImpl:cye}=Fh,Zde=at({opType:le.ABS,cpuKernelImpl:Ude}),Jde={kernelName:"Abs",backendName:"webgpu",kernelFunc:Zde},Qde=at({opType:le.ACOS}),epe={kernelName:ii,backendName:"webgpu",kernelFunc:Qde},tpe=at({opType:le.ACOSH}),ape={kernelName:oi,backendName:"webgpu",kernelFunc:tpe},npe=aa({opType:Pe.ADD,cpuKernelImpl:yde,supportsComplex:!0}),rpe={kernelName:"Add",backendName:"webgpu",kernelFunc:npe},spe=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(a=>{e.push(`let v${a} = get${a}ByOutputCoords(coords);`)});let t=this.variableNames.map(a=>`v${a}`).join(" + ");return` ${ue("index")} { for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; @@ -5926,7 +5926,7 @@ return a / b;`,NJ=` } } } - `}};function ipe(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return tn({inputs:{x:n[0]},backend:a});let r=n.map(o=>o.dtype).reduce((o,l)=>ca(o,l)),s=n.map(o=>o.shape),i=new spe(s);return a.runWebGPUProgram(i,n,r)}var ope={kernelName:ui,backendName:"webgpu",kernelFunc:ipe},lpe=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[16,16,1];let a=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return` + `}};function ipe(e){let{inputs:t,backend:a}=e,n=t;if(n.length===1)return tn({inputs:{x:n[0]},backend:a});let r=n.map(o=>o.dtype).reduce((o,l)=>ca(o,l)),s=n.map(o=>o.shape),i=new spe(s);return a.runWebGPUProgram(i,n,r)}var ope={kernelName:li,backendName:"webgpu",kernelFunc:ipe},lpe=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[16,16,1];let a=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return` var tile : array, ${this.workgroupSize[0]}>; ${ue()} { var x = i32(workgroupId.x) * ${e} + i32(localId.x); @@ -5945,7 +5945,7 @@ return a / b;`,NJ=` [localId.y]); } } - `}},upe=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=new Array(t);for(let n=0;n=32768&&a>=512?this.workgroupSize=[512,1,1]:e.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0",a=this.workgroupSize[0];this.reduceType==="min"||this.reduceType==="max"?(e=` + `}};function pk(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let a=new Array(t);for(let n=0;n=32768&&a>=512?this.workgroupSize=[512,1,1]:e.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0",a=this.workgroupSize[0];this.reduceType==="min"||this.reduceType==="max"?(e=` if (isnan(candidate)) { bestValue = uniforms.NAN; } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue) @@ -6004,7 +6004,7 @@ return a / b;`,NJ=` ${n} } } - `}},cpe={mean:"float32",all:"bool",any:"bool"};function dl(e,t,a,n,r){let s=e.shape.length,i=[],o=v.parseAxisParam(t,e.shape),l=o,u=C.getAxesPermutation(l,s),p=e;u!=null&&(p=sr({inputs:{x:e},attrs:{perm:u},backend:r}),l=C.getInnerMostAxes(l.length,s),i.push(p)),C.assertAxesAreInnerMostDims(n,l,s);let[c,d]=C.computeOutAndReduceShapes(p.shape,l),h=c;a&&(h=C.expandShapeToKeepDim(c,o));let m;if((n==="max"||n==="prod")&&r.shouldExecuteOnCPU([p])){let f=r.tensorMap.get(p.dataId).values;switch(n){case"max":let g=Pde(f,v.sizeFromShape(d),h,e.dtype);m=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:x,outDtype:A}=Lde(p.shape,p.dtype,f,l);m=r.makeTensorInfo(x,A,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let f=v.sizeFromShape(d),g=v.sizeFromShape(p.shape)/f,y={windowSize:f,inSize:f,batchSize:g,outSize:1},x=cpe[n]||_p(e.dtype),A=[{type:"int32",data:[f]}],b=new ppe(y,n,r.device.limits.maxComputeWorkgroupSizeX),w=r.runWebGPUProgram(b,[p],x,A);i.push(w),m=ke({inputs:{x:w},attrs:{shape:h},backend:r})}return i.forEach(f=>r.disposeData(f.dataId)),m}function hpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return dl(r,i,s,"all",a)}var mpe={kernelName:di,backendName:"webgpu",kernelFunc:hpe};function fpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return dl(r,i,s,"any",a)}var gpe={kernelName:pi,backendName:"webgpu",kernelFunc:fpe},$k=class{constructor(e,t,a){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=a==="min"?"<":">";let[r,s]=C.computeOutAndReduceShapes(e,n);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=me(this.outputShape),v.sizeFromShape(s)<32?(this.type="plain",this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=de(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Cr(this.inputShape.length-1)}`,a=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let r=0;rr.disposeData(f.dataId)),m}function hpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return qo(r,i,s,"all",a)}var mpe={kernelName:"All",backendName:"webgpu",kernelFunc:hpe};function fpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return qo(r,i,s,"any",a)}var gpe={kernelName:"Any",backendName:"webgpu",kernelFunc:fpe},ck=class{constructor(e,t,a){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=a==="min"?"<":">";let[r,s]=C.computeOutAndReduceShapes(e,n);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=me(this.outputShape),v.sizeFromShape(s)<32?(this.type="plain",this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=de(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Cr(this.inputShape.length-1)}`,a=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let r=0;r u32 { return ((a - 1u) / b + 1u); } @@ -6070,7 +6070,7 @@ return a / b;`,NJ=` setOutputAtIndexI32(index, bestIndex); } } - `}};function ype(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=sr({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=new $k(l.shape,i[0],"max"),c=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var xpe={kernelName:lu,backendName:"webgpu",kernelFunc:ype};function Ape(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=sr({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=new $k(l.shape,i[0],"min"),c=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var bpe={kernelName:uu,backendName:"webgpu",kernelFunc:Ape},vpe=at({opType:le.ASIN}),wpe={kernelName:ci,backendName:"webgpu",kernelFunc:vpe},kpe=at({opType:le.ASINH}),Ipe={kernelName:hi,backendName:"webgpu",kernelFunc:kpe},Spe=at({opType:le.ATAN}),Cpe={kernelName:mi,backendName:"webgpu",kernelFunc:Spe},Tpe=aa({opType:Pe.ATAN2}),Npe={kernelName:gi,backendName:"webgpu",kernelFunc:Tpe},Rpe=at({opType:le.ATANH}),Epe={kernelName:fi,backendName:"webgpu",kernelFunc:Rpe},Mpe=class{constructor(e){this.variableNames=["x"],this.uniforms="strides : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` + `}};function ype(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=sr({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=new ck(l.shape,i[0],"max"),c=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var xpe={kernelName:Gl,backendName:"webgpu",kernelFunc:ype};function Ape(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=sr({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=new ck(l.shape,i[0],"min"),c=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=a.runWebGPUProgram(p,[l],"int32",c);return u.forEach(h=>a.disposeData(h.dataId)),d}var bpe={kernelName:Hl,backendName:"webgpu",kernelFunc:Ape},vpe=at({opType:le.ASIN}),wpe={kernelName:ui,backendName:"webgpu",kernelFunc:vpe},kpe=at({opType:le.ASINH}),Ipe={kernelName:di,backendName:"webgpu",kernelFunc:kpe},Spe=at({opType:le.ATAN}),Cpe={kernelName:pi,backendName:"webgpu",kernelFunc:Spe},Tpe=aa({opType:Pe.ATAN2}),Npe={kernelName:hi,backendName:"webgpu",kernelFunc:Tpe},Rpe=at({opType:le.ATANH}),Epe={kernelName:ci,backendName:"webgpu",kernelFunc:Rpe},Mpe=class{constructor(e){this.variableNames=["x"],this.uniforms="strides : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -6085,7 +6085,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, value); } } - `}},sp=class{constructor(e,t,a=!1,n=!1,r=!1){if(this.variableNames=["x"],this.uniforms="strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=a,this.flattenPositions=n,this.includeBatchIndex=r,this.shaderKey=`pool2D_${t}_${a}_${n}_${r}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue = resultValue + value; count = count + 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); + `}},Wd=class{constructor(e,t,a=!1,n=!1,r=!1){if(this.variableNames=["x"],this.uniforms="strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=a,this.flattenPositions=n,this.includeBatchIndex=r,this.shaderKey=`pool2D_${t}_${a}_${n}_${r}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue = resultValue + value; count = count + 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); if (value >= currMaxValue) { maxValue = value; maxValueFound = 1.0; @@ -6126,7 +6126,7 @@ return a / b;`,NJ=` ${this.computePositions?"setOutputAtIndexI32(index, maxPosition);":`setOutputAtIndex(index, ${t});`} } } - `}},Z3=class{constructor(e,t,a=!1,n=!1,r=!1){if(this.variableNames=["x"],this.uniforms="strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=a,this.flattenPositions=n,this.includeBatchIndex=r,this.shaderKey=`pool3D_${t}_${a}_${n}_${r}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue += value; count += 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); + `}},M3=class{constructor(e,t,a=!1,n=!1,r=!1){if(this.variableNames=["x"],this.uniforms="strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,",this.workgroupSize=[128,1,1],this.size=!0,t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=a,this.flattenPositions=n,this.includeBatchIndex=r,this.shaderKey=`pool3D_${t}_${a}_${n}_${r}`}getUserCode(){let e;this.poolType==="avg"?e="resultValue += value; count += 1.0;":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound); if (value >= currMaxValue) { maxValue = value; maxValueFound = 1.0; @@ -6175,7 +6175,7 @@ return a / b;`,NJ=` ${this.computePositions?"setOutputAtIndexI32(index, maxPosition);":`setOutputAtIndex(index, ${t});`} } } - `}};function Pk(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n;return dl(r,s,i,"max",a)}var $pe={kernelName:oo,backendName:"webgpu",kernelFunc:Pk};function _k(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return dl(r,i,s,"mean",a)}var Ppe={kernelName:po,backendName:"webgpu",kernelFunc:_k};function Fk(e,t,a,n){if(t.filterWidth===1&&t.filterHeight===1&&v.arraysEqual(t.inShape,t.outShape))return tn({inputs:{x:e},backend:n});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let i=e.shape.length,o=ke({inputs:{x:e},backend:n,attrs:{shape:[e.shape[i-3]*e.shape[i-2],e.shape[i-1]]}}),l;a==="avg"?l=_k({inputs:{x:o},backend:n,attrs:{axis:0,keepDims:!1}}):(v.assert(a==="max",()=>`Invalid pool type ${a}`),l=Pk({inputs:{x:o},backend:n,attrs:{reductionIndices:0,keepDims:!1}}));let u=ke({inputs:{x:l},backend:n,attrs:{shape:t.outShape}});return n.disposeData(o.dataId),n.disposeData(l.dataId),u}let r,s=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?r=new Mpe(t):(a==="avg"?r=new sp(t,"avg"):(v.assert(a==="max",()=>`Invalid pool type ${a}`),r=new sp(t,"max")),s.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),n.runWebGPUProgram(r,[e],e.dtype,s)}function _pe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=C.computePool2DInfo(r.shape,s,i,1,o,l);return Fk(r,u,"avg",a)}var Fpe={kernelName:yi,backendName:"webgpu",kernelFunc:_pe};function Dpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=C.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new Z3(c,"avg"),h=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.front,c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.inDepth,c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]}];return a.runWebGPUProgram(d,[r],r.dtype,h)}var Ope={kernelName:du,backendName:"webgpu",kernelFunc:Dpe},zpe=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, + `}};function hk(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n;return qo(r,s,i,"max",a)}var $pe={kernelName:"Max",backendName:"webgpu",kernelFunc:hk};function mk(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return qo(r,i,s,"mean",a)}var Ppe={kernelName:eo,backendName:"webgpu",kernelFunc:mk};function fk(e,t,a,n){if(t.filterWidth===1&&t.filterHeight===1&&v.arraysEqual(t.inShape,t.outShape))return tn({inputs:{x:e},backend:n});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let i=e.shape.length,o=ke({inputs:{x:e},backend:n,attrs:{shape:[e.shape[i-3]*e.shape[i-2],e.shape[i-1]]}}),l;a==="avg"?l=mk({inputs:{x:o},backend:n,attrs:{axis:0,keepDims:!1}}):(v.assert(a==="max",()=>`Invalid pool type ${a}`),l=hk({inputs:{x:o},backend:n,attrs:{reductionIndices:0,keepDims:!1}}));let u=ke({inputs:{x:l},backend:n,attrs:{shape:t.outShape}});return n.disposeData(o.dataId),n.disposeData(l.dataId),u}let r,s=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?r=new Mpe(t):(a==="avg"?r=new Wd(t,"avg"):(v.assert(a==="max",()=>`Invalid pool type ${a}`),r=new Wd(t,"max")),s.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),n.runWebGPUProgram(r,[e],e.dtype,s)}function _pe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=C.computePool2DInfo(r.shape,s,i,1,o,l);return fk(r,u,"avg",a)}var Fpe={kernelName:mi,backendName:"webgpu",kernelFunc:_pe};function Dpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=C.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new M3(c,"avg"),h=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.front,c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.inDepth,c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]}];return a.runWebGPUProgram(d,[r],r.dtype,h)}var Ope={kernelName:jl,backendName:"webgpu",kernelFunc:Dpe},zpe=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool2DBackprop"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -6263,7 +6263,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, dotProd); } } - `}};function Wpe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=C.computePool3DInfo(i.shape,o,l,1,u,p),d=new Lpe(c),h=1/(c.filterDepth*c.filterHeight*c.filterWidth),m=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.effectiveFilterDepth-1-c.padInfo.front,c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:"int32",data:[c.outDepth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"float32",data:[h]}];return a.runWebGPUProgram(d,[r],i.dtype,m)}var Bpe={kernelName:pp,backendName:"webgpu",kernelFunc:Wpe};function Vpe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;q3([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=C.computePool2DInfo(i.shape,o,l,1,u),c=new zpe(p),d=1/(p.filterHeight*p.filterWidth),h=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.effectiveFilterHeight-1-p.padInfo.top,p.effectiveFilterWidth-1-p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]},{type:"int32",data:[p.outHeight]},{type:"int32",data:[p.outWidth]},{type:"float32",data:[d]}];return a.runWebGPUProgram(c,[r],i.dtype,h)}var Upe={kernelName:dp,backendName:"webgpu",kernelFunc:Vpe};function Gpe(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return c0({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var Hpe={kernelName:xi,backendName:"webgpu",kernelFunc:Gpe},jpe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Pt(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Pt(this.rank),t=qpe(this.rank),a;return this.start.length===1?a=this.outputShape.map((n,r)=>"sourceLoc = uniforms.start + coords;"):a=this.outputShape.map((n,r)=>`sourceLoc.${X1[r]} = uniforms.start.${Cr(r)} + coords.${X1[r]};`),` + `}};function Wpe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=C.computePool3DInfo(i.shape,o,l,1,u,p),d=new Lpe(c),h=1/(c.filterDepth*c.filterHeight*c.filterWidth),m=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.effectiveFilterDepth-1-c.padInfo.front,c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:"int32",data:[c.outDepth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"float32",data:[h]}];return a.runWebGPUProgram(d,[r],i.dtype,m)}var Bpe={kernelName:jd,backendName:"webgpu",kernelFunc:Wpe};function Vpe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;T3([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,p=C.computePool2DInfo(i.shape,o,l,1,u),c=new zpe(p),d=1/(p.filterHeight*p.filterWidth),h=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.effectiveFilterHeight-1-p.padInfo.top,p.effectiveFilterWidth-1-p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]},{type:"int32",data:[p.outHeight]},{type:"int32",data:[p.outWidth]},{type:"float32",data:[d]}];return a.runWebGPUProgram(c,[r],i.dtype,h)}var Upe={kernelName:Hd,backendName:"webgpu",kernelFunc:Vpe};function Gpe(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return jh({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var Hpe={kernelName:fi,backendName:"webgpu",kernelFunc:Gpe},jpe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Pt(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Pt(this.rank),t=qpe(this.rank),a;return this.start.length===1?a=this.outputShape.map((n,r)=>"sourceLoc = uniforms.start + coords;"):a=this.outputShape.map((n,r)=>`sourceLoc.${N1[r]} = uniforms.start.${Cr(r)} + coords.${N1[r]};`),` ${ue("index")} { if (index < uniforms.size) { var sourceLoc : ${e}; @@ -6273,15 +6273,15 @@ return a / b;`,NJ=` setOutputAtIndex(index, getSource(${t})); } } - `}},X1=["x","y","z","w","u","v"];function qpe(e){if(e===1)return"sourceLoc";if(e<=6)return X1.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function ad(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=Nt.parseSliceParams(r,s,i);if(Nt.assertParamsValid(r,o,l),a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.tensorMap.get(r.dataId),d=Gde(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}if(v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);let u=new jpe(o,l),p=[{type:"int32",data:o}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var Xpe={kernelName:_u,backendName:"webgpu",kernelFunc:ad},Kpe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=C.getReshaped(r.shape,s,o),u=C.getPermuted(l.length,s.length),p=C.getReshapedPermuted(r.shape,s,o),c=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(p,i,s.length),h=[],m=ke({inputs:{x:r},backend:a,attrs:{shape:l}}),f=sr({inputs:{x:m},backend:a,attrs:{perm:u}}),g=ke({inputs:{x:f},backend:a,attrs:{shape:p}}),y=ad({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(x=>a.disposeData(x.dataId)),y},Ype={kernelName:pu,backendName:"webgpu",kernelFunc:Kpe},Zpe=` + `}},N1=["x","y","z","w","u","v"];function qpe(e){if(e===1)return"sourceLoc";if(e<=6)return N1.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function Ou(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=Nt.parseSliceParams(r,s,i);if(Nt.assertParamsValid(r,o,l),a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.tensorMap.get(r.dataId),d=Gde(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,d)}if(v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);let u=new jpe(o,l),p=[{type:"int32",data:o}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var Xpe={kernelName:fu,backendName:"webgpu",kernelFunc:Ou},Kpe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=C.getReshaped(r.shape,s,o),u=C.getPermuted(l.length,s.length),p=C.getReshapedPermuted(r.shape,s,o),c=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(p,i,s.length),h=[],m=ke({inputs:{x:r},backend:a,attrs:{shape:l}}),f=sr({inputs:{x:m},backend:a,attrs:{perm:u}}),g=ke({inputs:{x:f},backend:a,attrs:{shape:p}}),y=Ou({inputs:{x:g},backend:a,attrs:{begin:c,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(x=>a.disposeData(x.dataId)),y},Ype={kernelName:ql,backendName:"webgpu",kernelFunc:Kpe},Zpe=` fn bincount_write(index: i32, value: f32) { - ${xs("&result[index]","value","float32")} + ${ys("&result[index]","value","float32")} } `,Jpe=` fn bincount_write(index: i32, value: f32) { atomicStore(&result[index], bitcast(value)); } -`,Dk=class{constructor(e,t,a=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=a,a&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` +`,gk=class{constructor(e,t,a=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=a,a&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` ${this.binaryOutput?Jpe:Zpe} ${ue("index")} { ${this.rank===1?`if (index < uniforms.xShape) { @@ -6299,7 +6299,7 @@ return a / b;`,NJ=` } }`} } - `}};function Qpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=v.sizeFromShape(r.shape),l=v.sizeFromShape(s.shape)>0,u=[i],p=s.dtype,c=Wa({backend:a,attrs:{shape:u,value:0,dtype:p}}),d=new Dk([o],l),h=[{type:"int32",data:[i]}],m=l?[r,s]:[r];return a.runWebGPUProgram(d,m,p,h,c)}var ece={kernelName:Ai,backendName:"webgpu",kernelFunc:Qpe},tce=class{constructor(e){this.outputShape=[],this.variableNames=["s0","s1"],this.uniforms="s0Size : i32, s1Size : i32, ",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="broadcastArgs"}getUserCode(){return` + `}};function Qpe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=v.sizeFromShape(r.shape),l=v.sizeFromShape(s.shape)>0,u=[i],p=s.dtype,c=Wa({backend:a,attrs:{shape:u,value:0,dtype:p}}),d=new gk([o],l),h=[{type:"int32",data:[i]}],m=l?[r,s]:[r];return a.runWebGPUProgram(d,m,p,h,c)}var ece={kernelName:gi,backendName:"webgpu",kernelFunc:Qpe},tce=class{constructor(e){this.outputShape=[],this.variableNames=["s0","s1"],this.uniforms="s0Size : i32, s1Size : i32, ",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="broadcastArgs"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { var s0 = 1.0; @@ -6324,7 +6324,7 @@ return a / b;`,NJ=` } } } - `}};function ace(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t;if(a.shouldExecuteOnCPU([n,r])){let p=a.tensorMap.get(n.dataId),c=a.tensorMap.get(r.dataId),d=p.values,h=c.values,m=C.assertAndGetBroadcastShape(Array.from(d),Array.from(h));return a.makeTensorInfo([m.length],"int32",Int32Array.from(m))}let s=v.sizeFromShape(n.shape),i=v.sizeFromShape(r.shape),o=Math.max(s,i),l=new tce(o),u=[{type:"int32",data:[s]},{type:"int32",data:[i]}];return a.runWebGPUProgram(l,[n,r],"int32",u)}var nce={kernelName:hu,backendName:"webgpu",kernelFunc:ace},Ok=aa({opType:Pe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:zde}),rce={kernelName:xo,backendName:"webgpu",kernelFunc:Ok};function tc(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return tn({inputs:{x:r.complexTensorInfos.real},backend:a})}var sce={kernelName:Ip,backendName:"webgpu",kernelFunc:tc};function ice(e,t){let a=new td(e.shape,le.TO_INT),n=t.runWebGPUProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function K1(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return tn({inputs:{x:r},backend:a});let i=yn(r.shape),o=K1({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=ul({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeData(o.dataId),l}if(r.dtype==="complex64"){let i=tc({inputs:{input:r},backend:a}),o=K1({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeData(i.dataId),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=tn({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.tensorMap.get(r.dataId).values,[o,l,u]=xde(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return ice(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=Ok({inputs:{a:r,b:i},backend:a});return a.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var oce={kernelName:bi,backendName:"webgpu",kernelFunc:K1},lce=at({opType:le.CEIL,cpuKernelImpl:Ade}),uce={kernelName:vi,backendName:"webgpu",kernelFunc:lce},dce=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` + `}};function ace(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t;if(a.shouldExecuteOnCPU([n,r])){let p=a.tensorMap.get(n.dataId),c=a.tensorMap.get(r.dataId),d=p.values,h=c.values,m=C.assertAndGetBroadcastShape(Array.from(d),Array.from(h));return a.makeTensorInfo([m.length],"int32",Int32Array.from(m))}let s=v.sizeFromShape(n.shape),i=v.sizeFromShape(r.shape),o=Math.max(s,i),l=new tce(o),u=[{type:"int32",data:[s]},{type:"int32",data:[i]}];return a.runWebGPUProgram(l,[n,r],"int32",u)}var nce={kernelName:Kl,backendName:"webgpu",kernelFunc:ace},yk=aa({opType:Pe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:zde}),rce={kernelName:so,backendName:"webgpu",kernelFunc:yk};function Fp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return tn({inputs:{x:r.complexTensorInfos.real},backend:a})}var sce={kernelName:rp,backendName:"webgpu",kernelFunc:Fp};function ice(e,t){let a=new Du(e.shape,le.TO_INT),n=t.runWebGPUProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function R1(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return tn({inputs:{x:r},backend:a});let i=yn(r.shape),o=R1({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=jo({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeData(o.dataId),l}if(r.dtype==="complex64"){let i=Fp({inputs:{input:r},backend:a}),o=R1({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeData(i.dataId),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=tn({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.tensorMap.get(r.dataId).values,[o,l,u]=xde(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return ice(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=yk({inputs:{a:r,b:i},backend:a});return a.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var oce={kernelName:yi,backendName:"webgpu",kernelFunc:R1},lce=at({opType:le.CEIL,cpuKernelImpl:Ade}),uce={kernelName:xi,backendName:"webgpu",kernelFunc:lce},dce=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` ${ue("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); @@ -6345,7 +6345,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal)); } } - `}};function cce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return v.sizeFromShape(r.shape)%4===0?o=new dce(r.shape):o=new pce(r.shape),a.runWebGPUProgram(o,[r],r.dtype,l)}var hce={kernelName:us,backendName:"webgpu",kernelFunc:cce},mce=class{constructor(e){this.outputShape=[],this.variableNames=["real","imag"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="complexAbs"}getUserCode(){return` + `}};function cce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return v.sizeFromShape(r.shape)%4===0?o=new dce(r.shape):o=new pce(r.shape),a.runWebGPUProgram(o,[r],r.dtype,l)}var hce={kernelName:ls,backendName:"webgpu",kernelFunc:cce},mce=class{constructor(e){this.outputShape=[],this.variableNames=["real","imag"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="complexAbs"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let re = abs(getRealByOutputIndex(index)); @@ -6357,7 +6357,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0)); } } - `}};function oA(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function fce(e){let{inputs:t,backend:a}=e,{x:n}=t,r=a.tensorMap.get(n.dataId),s=new mce(n.shape),i=[oA(n,r.complexTensorInfos.real),oA(n,r.complexTensorInfos.imag)];return a.runWebGPUProgram(s,i,i[0].dtype)}var gce={kernelName:hp,backendName:"webgpu",kernelFunc:fce},yce=class{constructor(e){this.uniforms="",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;n`T${a}`),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;ntc({inputs:{input:A},backend:a})),f=e.map(A=>h0({inputs:{input:A},backend:a})),g=_d(m,t,a),y=_d(f,t,a),x=ul({inputs:{real:g,imag:y},backend:a});return m.forEach(A=>a.disposeData(A.dataId)),f.forEach(A=>a.disposeData(A.dataId)),a.disposeData(g.dataId),a.disposeData(y.dataId),x}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let m=e.map(w=>{let I=[-1,v.sizeFromShape(w.shape.slice(t))];return ke({inputs:{x:w},backend:a,attrs:{shape:I}})}),f=m.map(w=>({vals:a.readSync(w.dataId),shape:w.shape})),g=C.computeOutShape(m.map(w=>w.shape),1),y=m[0].shape[0]===1,x=bde(f,g,n,y),A=C.computeOutShape(e.map(w=>w.shape),t),b=a.makeTensorInfo(A,n,x);return m.forEach(w=>a.disposeData(w.dataId)),b}let s=a.device.limits.maxStorageBuffersPerShaderStage-1;if(e.length>s){let m=[];for(let g=0;gm.shape),u=new yce(l),p=[],c=new Array(l.length-1);if(c.length>0){c[0]=l[0][1],p.push({type:"int32",data:[c[0]]});for(let m=1;ma.disposeData(m.dataId));let h=ke({inputs:{x:d},backend:a,attrs:{shape:o}});return a.disposeData(d.dataId),h}function Ace(e,t,a){let n=C.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ke({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape.slice(0,t)),v.sizeFromShape(r.shape.slice(t))]}})),outShape:n}}function zk(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);C.assertParamsConsistent(i,s);let o=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?tn({inputs:{x:l[0]},backend:a}):_d(l,s,a)}var bce={kernelName:mu,backendName:"webgpu",kernelFunc:zk};function vce(e,t,a,n,r=!1,s=null,i=!1,o=4,l=4,u=4){let p=N=>{switch(N){case 1:return"resData = f32(x[xIndex]);";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = vec4(x[xIndex / 4]);";default:throw new Error(`innerElementSize ${N} is not supported.`)}},c=N=>{switch(N){case 1:return"return f32(W[row * uniforms.wShape[3] + col]);";case 4:return"return vec4(W[(row * uniforms.wShape[3] + col) / 4]);";default:throw new Error(`innerElementSize ${N} is not supported.`)}},d=e?` + `}};function qh(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return tn({inputs:{x:r.complexTensorInfos.imag},backend:a})}var xce={kernelName:tp,backendName:"webgpu",kernelFunc:qh};function fd(e,t,a){let n=e[0].dtype;if(n==="complex64"){let m=e.map(A=>Fp({inputs:{input:A},backend:a})),f=e.map(A=>qh({inputs:{input:A},backend:a})),g=fd(m,t,a),y=fd(f,t,a),x=jo({inputs:{real:g,imag:y},backend:a});return m.forEach(A=>a.disposeData(A.dataId)),f.forEach(A=>a.disposeData(A.dataId)),a.disposeData(g.dataId),a.disposeData(y.dataId),x}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let m=e.map(w=>{let I=[-1,v.sizeFromShape(w.shape.slice(t))];return ke({inputs:{x:w},backend:a,attrs:{shape:I}})}),f=m.map(w=>({vals:a.readSync(w.dataId),shape:w.shape})),g=C.computeOutShape(m.map(w=>w.shape),1),y=m[0].shape[0]===1,x=bde(f,g,n,y),A=C.computeOutShape(e.map(w=>w.shape),t),b=a.makeTensorInfo(A,n,x);return m.forEach(w=>a.disposeData(w.dataId)),b}let s=a.device.limits.maxStorageBuffersPerShaderStage-1;if(e.length>s){let m=[];for(let g=0;gm.shape),u=new yce(l),p=[],c=new Array(l.length-1);if(c.length>0){c[0]=l[0][1],p.push({type:"int32",data:[c[0]]});for(let m=1;ma.disposeData(m.dataId));let h=ke({inputs:{x:d},backend:a,attrs:{shape:o}});return a.disposeData(d.dataId),h}function Ace(e,t,a){let n=C.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ke({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape.slice(0,t)),v.sizeFromShape(r.shape.slice(t))]}})),outShape:n}}function xk(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);C.assertParamsConsistent(i,s);let o=C.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?tn({inputs:{x:l[0]},backend:a}):fd(l,s,a)}var bce={kernelName:Yl,backendName:"webgpu",kernelFunc:xk};function vce(e,t,a,n,r=!1,s=null,i=!1,o=4,l=4,u=4){let p=N=>{switch(N){case 1:return"resData = f32(x[xIndex]);";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = vec4(x[xIndex / 4]);";default:throw new Error(`innerElementSize ${N} is not supported.`)}},c=N=>{switch(N){case 1:return"return f32(W[row * uniforms.wShape[3] + col]);";case 4:return"return vec4(W[(row * uniforms.wShape[3] + col) / 4]);";default:throw new Error(`innerElementSize ${N} is not supported.`)}},d=e?` let coord = vec4(batch, xRow, xCol, xCh); `:` let coord = vec4(batch, xCh, xRow, xCol); @@ -6432,10 +6432,10 @@ return a / b;`,NJ=` var value = valueIn; let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"}; ${h} - ${ll(r,s)} + ${Ho(r,s)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } - }`}var wce=class{constructor(e,t,a,n,r=!1,s=null,i=!1,o=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=G3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=H3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),r&&(this.variableNames.push("bias"),this.variableComponents.push(4)),i&&(this.variableNames.push("preluActivationWeights"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],r&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=o,this.addBias=r,this.activation=s,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=a%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?d0(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):p0(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` + }`}var wce=class{constructor(e,t,a,n,r=!1,s=null,i=!1,o=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=I3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=S3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),r&&(this.variableNames.push("bias"),this.variableComponents.push(4)),i&&(this.variableNames.push("preluActivationWeights"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],r&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=o,this.addBias=r,this.activation=s,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=a%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?Gh(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Hh(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` ${vce(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])} ${e} `}},kce=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=a,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` @@ -6460,7 +6460,7 @@ return a / b;`,NJ=` let coords = ${this.isChannelsLast?"vec4(batch, row, col, chan);":"vec4(batch, chan, row, col);"} if (coordsInBounds4D(coords, uniforms.outShape)) { var value = valueIn; - ${ll(this.addBias,this.activation)} + ${Ho(this.addBias,this.activation)} setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value); } } @@ -6508,7 +6508,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, value); } } - `}};function Nh(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function Sce({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a.dataFormat==="channelsLast",u=!l,p=!1,c=l&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=[],h,m;if(c){let y=a.inHeight*a.inWidth*a.inChannels;h=ke({inputs:{x:e},backend:n,attrs:{shape:[1,a.batchSize,y]}}),m=ke({inputs:{x:t},backend:n,attrs:{shape:[1,y,a.outChannels]}})}else h=ke({inputs:{x:e},backend:n,attrs:{shape:l?[a.batchSize,a.inHeight*a.inWidth,a.inChannels]:[a.batchSize,a.inChannels,a.inHeight*a.inWidth]}}),m=ke({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});if(d.push(h),d.push(m),s!=null){let y=Nh(s.shape,l);y!=null&&(s=ke({inputs:{x:s},backend:n,attrs:{shape:y}}),d.push(s))}if(r!=null){let y=Nh(r.shape,l);y!=null&&(r=ke({inputs:{x:r},backend:n,attrs:{shape:y}}),d.push(r))}let f=c0({a:l?h:m,b:l?m:h,transposeA:u,transposeB:p,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=ke({inputs:{x:f},backend:n,attrs:{shape:a.outShape}});d.push(f);for(let y of d)n.disposeData(y.dataId);return g}function Cce({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,strideWidth:c,strideHeight:d,padInfo:h,outWidth:m,outHeight:f,dilationWidth:g,dilationHeight:y,dataFormat:x}=a,A=x==="channelsLast",b=l*u*p,w=f*m,I=A?[a.batchSize,w,b]:[a.batchSize,b,w],T=new Ice(I,A),N=[{type:"int32",data:[h.top,h.left]},{type:"int32",data:[d,c]},{type:"int32",data:[y,g]},{type:"int32",data:[m]},{type:"int32",data:[p*l]},{type:"int32",data:[p]}],M=n.runWebGPUProgram(T,[e],e.dtype,N),$=[];$.push(M);let E=ke({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});if($.push(E),s!=null){let O=Nh(s.shape,A);O!=null&&(s=ke({inputs:{x:s},backend:n,attrs:{shape:O}}),$.push(s))}if(r!=null){let O=Nh(r.shape,A);O!=null&&(r=ke({inputs:{x:r},backend:n,attrs:{shape:O}}),$.push(r))}let S=c0({a:A?M:E,b:A?E:M,transposeA:!A,transposeB:!1,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),_=ke({inputs:{x:S},backend:n,attrs:{shape:a.outShape}});$.push(S);for(let O of $)n.disposeData(O.dataId);return _}function Lk({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r!=null,u=s!=null,p=a.dataFormat==="channelsLast",c=p&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=B().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!d&&(c||a.filterHeight===1&&a.filterWidth===1&&a.dilationHeight===1&&a.dilationWidth===1&&a.strideHeight===1&&a.strideWidth===1&&(a.padInfo.type==="SAME"||a.padInfo.type==="VALID")))return Sce({x:e,filter:t,convInfo:a,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});let h=B().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),m=h>-1?h:n.thresholdToIncreaseWorkgroups,f=a.batchSize*Math.ceil(a.outHeight*a.outWidth/32)*Math.ceil(a.outChannels/32);if(B().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||f<=m)return Cce({x:e,filter:t,convInfo:a,backend:n,bias:r,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let g,y=[a.padInfo.top,a.padInfo.left],x=[{type:"int32",data:[a.filterHeight,a.filterWidth]},{type:"int32",data:[...y]},{type:"int32",data:[a.strideHeight,a.strideWidth]},{type:"int32",data:[a.dilationHeight,a.dilationWidth]}];if(d)g=new kce(a,l,o,u);else{let I=p?a.outHeight*a.outWidth:a.outChannels,T=p?a.outChannels:a.outHeight*a.outWidth,N=a.filterHeight*a.filterWidth*a.inChannels;x.push({type:"int32",data:[I]},{type:"int32",data:[T]},{type:"int32",data:[N]});let M=n.adapterInfo.isIntel();g=new wce(a,I,T,N,l,o,u,M)}let A=[],b=[e,t];l&&(!p&&r.shape.length===1&&(r=ke({inputs:{x:r},backend:n,attrs:{shape:[r.shape[0],1,1]}}),A.push(r)),b.push(r)),u&&(!p&&s.shape.length===1&&(s=ke({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}}),A.push(s)),b.push(s)),o==="leakyrelu"&&(x.push({type:"float32",data:[i]}),g.uniforms+=" alpha : f32,");let w=n.runWebGPUProgram(g,b,e.dtype,x);for(let I of A)n.disposeData(I.dataId);return w}function Tce(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,c=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c);return Lk({x:r,filter:s,convInfo:d,backend:n})}var Nce={kernelName:wi,backendName:"webgpu",kernelFunc:Tce},Rce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=e.inShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=this.isChannelsLast&&e.outChannels%4===0&&e.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?3:1,n=` + `}};function lh(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function Sce({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a.dataFormat==="channelsLast",u=!l,p=!1,c=l&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=[],h,m;if(c){let y=a.inHeight*a.inWidth*a.inChannels;h=ke({inputs:{x:e},backend:n,attrs:{shape:[1,a.batchSize,y]}}),m=ke({inputs:{x:t},backend:n,attrs:{shape:[1,y,a.outChannels]}})}else h=ke({inputs:{x:e},backend:n,attrs:{shape:l?[a.batchSize,a.inHeight*a.inWidth,a.inChannels]:[a.batchSize,a.inChannels,a.inHeight*a.inWidth]}}),m=ke({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});if(d.push(h),d.push(m),s!=null){let y=lh(s.shape,l);y!=null&&(s=ke({inputs:{x:s},backend:n,attrs:{shape:y}}),d.push(s))}if(r!=null){let y=lh(r.shape,l);y!=null&&(r=ke({inputs:{x:r},backend:n,attrs:{shape:y}}),d.push(r))}let f=jh({a:l?h:m,b:l?m:h,transposeA:u,transposeB:p,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=ke({inputs:{x:f},backend:n,attrs:{shape:a.outShape}});d.push(f);for(let y of d)n.disposeData(y.dataId);return g}function Cce({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:p,strideWidth:c,strideHeight:d,padInfo:h,outWidth:m,outHeight:f,dilationWidth:g,dilationHeight:y,dataFormat:x}=a,A=x==="channelsLast",b=l*u*p,w=f*m,I=A?[a.batchSize,w,b]:[a.batchSize,b,w],T=new Ice(I,A),N=[{type:"int32",data:[h.top,h.left]},{type:"int32",data:[d,c]},{type:"int32",data:[y,g]},{type:"int32",data:[m]},{type:"int32",data:[p*l]},{type:"int32",data:[p]}],M=n.runWebGPUProgram(T,[e],e.dtype,N),$=[];$.push(M);let E=ke({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});if($.push(E),s!=null){let O=lh(s.shape,A);O!=null&&(s=ke({inputs:{x:s},backend:n,attrs:{shape:O}}),$.push(s))}if(r!=null){let O=lh(r.shape,A);O!=null&&(r=ke({inputs:{x:r},backend:n,attrs:{shape:O}}),$.push(r))}let S=jh({a:A?M:E,b:A?E:M,transposeA:!A,transposeB:!1,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),_=ke({inputs:{x:S},backend:n,attrs:{shape:a.outShape}});$.push(S);for(let O of $)n.disposeData(O.dataId);return _}function Ak({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r!=null,u=s!=null,p=a.dataFormat==="channelsLast",c=p&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",d=B().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!d&&(c||a.filterHeight===1&&a.filterWidth===1&&a.dilationHeight===1&&a.dilationWidth===1&&a.strideHeight===1&&a.strideWidth===1&&(a.padInfo.type==="SAME"||a.padInfo.type==="VALID")))return Sce({x:e,filter:t,convInfo:a,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});let h=B().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),m=h>-1?h:n.thresholdToIncreaseWorkgroups,f=a.batchSize*Math.ceil(a.outHeight*a.outWidth/32)*Math.ceil(a.outChannels/32);if(B().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||f<=m)return Cce({x:e,filter:t,convInfo:a,backend:n,bias:r,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let g,y=[a.padInfo.top,a.padInfo.left],x=[{type:"int32",data:[a.filterHeight,a.filterWidth]},{type:"int32",data:[...y]},{type:"int32",data:[a.strideHeight,a.strideWidth]},{type:"int32",data:[a.dilationHeight,a.dilationWidth]}];if(d)g=new kce(a,l,o,u);else{let I=p?a.outHeight*a.outWidth:a.outChannels,T=p?a.outChannels:a.outHeight*a.outWidth,N=a.filterHeight*a.filterWidth*a.inChannels;x.push({type:"int32",data:[I]},{type:"int32",data:[T]},{type:"int32",data:[N]});let M=n.adapterInfo.isIntel();g=new wce(a,I,T,N,l,o,u,M)}let A=[],b=[e,t];l&&(!p&&r.shape.length===1&&(r=ke({inputs:{x:r},backend:n,attrs:{shape:[r.shape[0],1,1]}}),A.push(r)),b.push(r)),u&&(!p&&s.shape.length===1&&(s=ke({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}}),A.push(s)),b.push(s)),o==="leakyrelu"&&(x.push({type:"float32",data:[i]}),g.uniforms+=" alpha : f32,");let w=n.runWebGPUProgram(g,b,e.dtype,x);for(let I of A)n.disposeData(I.dataId);return w}function Tce(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,c=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,c);return Ak({x:r,filter:s,convInfo:d,backend:n})}var Nce={kernelName:Ai,backendName:"webgpu",kernelFunc:Tce},Rce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=e.inShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=this.isChannelsLast&&e.outChannels%4===0&&e.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?3:1,n=` ${ue()} { let batch = i32(globalId.z) / uniforms.outShape[1]; let r = i32(globalId.z) % uniforms.outShape[1]; @@ -6787,7 +6787,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, dotProd); } } - `}};function Pce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new Ece(d),m=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]},{type:"int32",data:[d.inHeight]},{type:"int32",data:[d.inWidth]}];return a.runWebGPUProgram(h,[r,s],r.dtype,m)}var _ce={kernelName:mp,backendName:"webgpu",kernelFunc:Pce};function Fce(e=4){let t=n=>{switch(n){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` + `}};function Pce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=n,c=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(r.shape,p,i,1,o,u,!1,c),h=new Ece(d),m=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]},{type:"int32",data:[d.inHeight]},{type:"int32",data:[d.inWidth]}];return a.runWebGPUProgram(h,[r,s],r.dtype,m)}var _ce={kernelName:Kd,backendName:"webgpu",kernelFunc:Pce};function Fce(e=4){let t=n=>{switch(n){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` let coord1 = vec4(coordX, coordY, col + 1, rowInner); let coord2 = vec4(coordX, coordY, col + 2, rowInner); let coord3 = vec4(coordX, coordY, col + 3, rowInner); @@ -6847,10 +6847,10 @@ return a / b;`,NJ=` col); result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${e}] = value; } - }`}var Dce=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=G3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=H3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?d0(this.elementsPerThread,this.workgroupSize):p0(this.elementsPerThread,this.workgroupSize);return` + }`}var Dce=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=I3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=S3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?Gh(this.elementsPerThread,this.workgroupSize):Hh(this.elementsPerThread,this.workgroupSize);return` ${Fce(this.isVec4?4:1)} ${e} - `}};function Oce(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],m;if(B().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||d.dataFormat!=="channelsLast")m=new Rce(d);else{m=new Dce(d);let f=d.inHeight*d.inWidth,g=d.inChannels,y=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[f]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return a.runWebGPUProgram(m,[r,s],"float32",h)}var zce={kernelName:ki,backendName:"webgpu",kernelFunc:Oce},Lce=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3dnaive"}getUserCode(){return` + `}};function Oce(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=n,c=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(i,s.shape,o,1,l,p,!1,c),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],m;if(B().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||d.dataFormat!=="channelsLast")m=new Rce(d);else{m=new Dce(d);let f=d.inHeight*d.inWidth,g=d.inChannels,y=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[f]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return a.runWebGPUProgram(m,[r,s],"float32",h)}var zce={kernelName:bi,backendName:"webgpu",kernelFunc:Oce},Lce=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="conv3dnaive"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); @@ -6932,7 +6932,7 @@ return a / b;`,NJ=` } setOutputAtIndex(index, dotProd); } - }`}};function Wce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeConv3DInfo(r.shape,s.shape,i,l,o),p=[u.padInfo.front,u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],d=new Lce(u),h=ca(r.dtype,s.dtype);return a.runWebGPUProgram(d,[r,s],h,c)}var Bce={kernelName:Ii,backendName:"webgpu",kernelFunc:Wce};function Vce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=C.computeConv3DInfo(r.shape,l,i,1,o),p=new Mce(u),c=[{type:"int32",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.batchSize]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.inDepth]},{type:"int32",data:[u.inHeight]},{type:"int32",data:[u.inWidth]}];return a.runWebGPUProgram(p,[r,s],s.dtype,c)}var Uce={kernelName:fu,backendName:"webgpu",kernelFunc:Vce};function Gce(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,pad:o,inputShape:l}=n,u=C.computeConv3DInfo(l,s.shape,i,1,o),p=new $ce(u),c=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.outChannels]}];return a.runWebGPUProgram(p,[r,s],r.dtype,c)}var Hce={kernelName:Si,backendName:"webgpu",kernelFunc:Gce},jce=at({opType:le.COS}),qce={kernelName:Ci,backendName:"webgpu",kernelFunc:jce},Xce=at({opType:le.COSH}),Kce={kernelName:Ti,backendName:"webgpu",kernelFunc:Xce},Yce=class{constructor(e,t,a,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,a[0],a[1],e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[a,n,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` + }`}};function Wce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeConv3DInfo(r.shape,s.shape,i,l,o),p=[u.padInfo.front,u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],d=new Lce(u),h=ca(r.dtype,s.dtype);return a.runWebGPUProgram(d,[r,s],h,c)}var Bce={kernelName:vi,backendName:"webgpu",kernelFunc:Wce};function Vce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=C.computeConv3DInfo(r.shape,l,i,1,o),p=new Mce(u),c=[{type:"int32",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.batchSize]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.inDepth]},{type:"int32",data:[u.inHeight]},{type:"int32",data:[u.inWidth]}];return a.runWebGPUProgram(p,[r,s],s.dtype,c)}var Uce={kernelName:Zl,backendName:"webgpu",kernelFunc:Vce};function Gce(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,pad:o,inputShape:l}=n,u=C.computeConv3DInfo(l,s.shape,i,1,o),p=new $ce(u),c=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.outChannels]}];return a.runWebGPUProgram(p,[r,s],r.dtype,c)}var Hce={kernelName:wi,backendName:"webgpu",kernelFunc:Gce},jce=at({opType:le.COS}),qce={kernelName:"Cos",backendName:"webgpu",kernelFunc:jce},Xce=at({opType:le.COSH}),Kce={kernelName:ki,backendName:"webgpu",kernelFunc:Xce},Yce=class{constructor(e,t,a,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,a[0],a[1],e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[a,n,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -6988,23 +6988,23 @@ return a / b;`,NJ=` } } } - `}},Zce=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Yce(r.shape[3],s.shape,o,l),c=[{type:"float32",data:[u]}];return a.runWebGPUProgram(p,[r,s,i],"float32",c)},Jce={kernelName:Ei,backendName:"webgpu",kernelFunc:Zce},ip;(function(e){e.Prod="*",e.Sum="+"})(ip||(ip={}));var lA=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=a,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===ip.Prod?"1.0":"0.0",a=this.exclusive?t:`getX(${uA(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],r="",s="";return this.exclusive?(r=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),` + `}},Zce=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,p=new Yce(r.shape[3],s.shape,o,l),c=[{type:"float32",data:[u]}];return a.runWebGPUProgram(p,[r,s,i],"float32",c)},Jce={kernelName:Ci,backendName:"webgpu",kernelFunc:Zce},Bd;(function(e){e.Prod="*",e.Sum="+"})(Bd||(Bd={}));var V5=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=a,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Bd.Prod?"1.0":"0.0",a=this.exclusive?t:`getX(${U5(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],r="",s="";return this.exclusive?(r=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),` ${ue("index")} { if (index < uniforms.size) { var coords = getCoordsFromIndex(index); - let end = ${dA(e,"coords",this.op)}; + let end = ${G5(e,"coords",this.op)}; var val = ${a}; let pow2 = i32(pow(2.0, uniforms.index)); if (${r}) { let idx = ${s}; - ${dA(e,"coords",this.op)} = idx; - val ${this.op}= getX(${uA(e,"coords",this.op)}); + ${G5(e,"coords",this.op)} = idx; + val ${this.op}= getX(${U5(e,"coords",this.op)}); } setOutputAtIndex(index, val); } } - `}};function uA(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function dA(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function Wk(e,t,a,n,r,s){let i=t.shape.length,o=C.getAxesPermutation([n],i),l=t;o!=null&&(l=sr({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=C.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=tn({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new lA(e,l.shape,!1,s),m=c,f=[{type:"float32",data:[d]}];c=a.runWebGPUProgram(h,[c],c.dtype,f),a.disposeData(m.dataId)}if(r){let d=new lA(e,l.shape,r,s),h=c,m=[{type:"float32",data:[0]}];c=a.runWebGPUProgram(d,[c],c.dtype,m),a.disposeData(h.dataId)}if(o!=null){let d=C.getUndoAxesPermutation(o),h=sr({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeData(c.dataId),a.disposeData(l.dataId),h}return c}function Qce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return Wk(ip.Prod,r,a,s,i,o)}var ehe={kernelName:Ni,backendName:"webgpu",kernelFunc:Qce};function the(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return Wk(ip.Sum,r,a,s,i,o)}var ahe={kernelName:Ri,backendName:"webgpu",kernelFunc:the};function nhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n,l=r.shape.length===1,u=v.sizeFromShape(s.shape)>0,p=s.dtype,c=l?[r.shape[0]]:[r.shape[0],r.shape[1]],d=l?[i]:[r.shape[0],i],h=Wa({backend:a,attrs:{shape:d,value:0,dtype:p}}),m=new Dk(c,u,o),f=[{type:"int32",data:[i]}],g=u?[r,s]:[r];return a.runWebGPUProgram(m,g,p,f,h)}var rhe={kernelName:gu,backendName:"webgpu",kernelFunc:nhe},she=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return` + `}};function U5(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function G5(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function bk(e,t,a,n,r,s){let i=t.shape.length,o=C.getAxesPermutation([n],i),l=t;o!=null&&(l=sr({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=C.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let p=l.shape[u],c=tn({inputs:{x:l},backend:a});for(let d=0;d<=Math.ceil(Math.log2(p))-1;d++){let h=new V5(e,l.shape,!1,s),m=c,f=[{type:"float32",data:[d]}];c=a.runWebGPUProgram(h,[c],c.dtype,f),a.disposeData(m.dataId)}if(r){let d=new V5(e,l.shape,r,s),h=c,m=[{type:"float32",data:[0]}];c=a.runWebGPUProgram(d,[c],c.dtype,m),a.disposeData(h.dataId)}if(o!=null){let d=C.getUndoAxesPermutation(o),h=sr({inputs:{x:c},backend:a,attrs:{perm:d}});return a.disposeData(c.dataId),a.disposeData(l.dataId),h}return c}function Qce(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return bk(Bd.Prod,r,a,s,i,o)}var ehe={kernelName:Ii,backendName:"webgpu",kernelFunc:Qce};function the(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return bk(Bd.Sum,r,a,s,i,o)}var ahe={kernelName:Si,backendName:"webgpu",kernelFunc:the};function nhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n,l=r.shape.length===1,u=v.sizeFromShape(s.shape)>0,p=s.dtype,c=l?[r.shape[0]]:[r.shape[0],r.shape[1]],d=l?[i]:[r.shape[0],i],h=Wa({backend:a,attrs:{shape:d,value:0,dtype:p}}),m=new gk(c,u,o),f=[{type:"int32",data:[i]}],g=u?[r,s]:[r];return a.runWebGPUProgram(m,g,p,f,h)}var rhe={kernelName:Jl,backendName:"webgpu",kernelFunc:nhe},she=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7024,7 +7024,7 @@ return a / b;`,NJ=` let rlt = ${this.getInputSamplingString()}; setOutputAtIndex(index, rlt); } - }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function ihe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),m=i==="NHWC"?[o,c,d,h]:[o,h,c,d],f=[{type:"int32",data:[s]}],g=new she(m,i);return a.runWebGPUProgram(g,[r],r.dtype,f)}var ohe={kernelName:Mi,backendName:"webgpu",kernelFunc:ihe},lhe=class{constructor(e,t,a,n=!1,r=null,s=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=r,this.hasPreluActivation=s,this.filterHeight=t,this.filterWidth=a,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],a=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` + }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function ihe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,d=u*s,h=p/(s*s),m=i==="NHWC"?[o,c,d,h]:[o,h,c,d],f=[{type:"int32",data:[s]}],g=new she(m,i);return a.runWebGPUProgram(g,[r],r.dtype,f)}var ohe={kernelName:Ti,backendName:"webgpu",kernelFunc:ihe},lhe=class{constructor(e,t,a,n=!1,r=null,s=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=r,this.hasPreluActivation=s,this.filterHeight=t,this.filterWidth=a,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],a=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` ${Pr(this.activation,this.hasPreluActivation,!1,4)} var mm_Asub : array, ${a}>; @@ -7081,12 +7081,12 @@ return a / b;`,NJ=` value = fma(xVal, wVal, value); } } - ${ll(this.addBias,this.activation)} + ${Ho(this.addBias,this.activation)} if (coordsInBounds4D(coords, uniforms.outShape)) { setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } - `}},Bk=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2, virtualWidth : i32,",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=e.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let r=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=me(r),this.dispatch=de(this.dispatchLayout,r,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${a}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,a=this.convInfo.strideWidth;return` + `}},vk=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="pads : vec2, inDims : vec2, virtualWidth : i32,",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=e.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let r=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=me(r),this.dispatch=de(this.dispatchLayout,r,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${a}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,a=this.convInfo.strideWidth;return` ${Pr(this.activation,this.hasPreluActivation,!0,4)} fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 { var value = vec4(0.0); @@ -7136,12 +7136,12 @@ return a / b;`,NJ=` let coords = vec4(batch, r, c + i, d1); if (coordsInBounds4D(coords, uniforms.outShape)) { var value = dotProd[i]; - ${ll(this.addBias,this.activation)} + ${Ho(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } } - `}},Vk=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32, + `}},wk=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32, filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` ${Pr(this.activation,this.hasPreluActivation,!1,4)} @@ -7204,11 +7204,11 @@ return a / b;`,NJ=` } } } - ${ll(this.addBias,this.activation)} + ${Ho(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } - `}};function uhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=C.convertConv2DDataFormat(l),d=u;d==null&&(d=[1,1]);let h=C.computeConv2DInfo(r.shape,s.shape,i,d,o,p,!0,c),m=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],f=h.dataFormat==="channelsLast",g;return!f&&h.inHeight>16&&h.inWidth>16&&h.strideHeight===1&&h.strideWidth===1&&h.dilationWidth===1&&h.dilationHeight===1&&h.inChannels===h.outChannels?g=new lhe(h.outShape,h.filterHeight,h.filterWidth):f&&h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(g=new Bk(h),m.push({type:"int32",data:[g.virtualWidth]})):(g=new Vk(h),m.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),a.runWebGPUProgram(g,[r,s],r.dtype,m)}var dhe={kernelName:$i,backendName:"webgpu",kernelFunc:uhe},phe=class{constructor(e){this.variableNames=["x","dy"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, + `}};function uhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=n,c=C.convertConv2DDataFormat(l),d=u;d==null&&(d=[1,1]);let h=C.computeConv2DInfo(r.shape,s.shape,i,d,o,p,!0,c),m=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],f=h.dataFormat==="channelsLast",g;return!f&&h.inHeight>16&&h.inWidth>16&&h.strideHeight===1&&h.strideWidth===1&&h.dilationWidth===1&&h.dilationHeight===1&&h.inChannels===h.outChannels?g=new lhe(h.outShape,h.filterHeight,h.filterWidth):f&&h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(g=new vk(h),m.push({type:"int32",data:[g.virtualWidth]})):(g=new wk(h),m.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),a.runWebGPUProgram(g,[r,s],r.dtype,m)}var dhe={kernelName:Ni,backendName:"webgpu",kernelFunc:uhe},phe=class{constructor(e){this.variableNames=["x","dy"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="depthwise_conv2d_backprop_filter"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -7287,7 +7287,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, dotProd); } } - `}};function hhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=C.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new phe(c),h=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"int32",data:[c.inHeight]},{type:"int32",data:[c.inWidth]},{type:"int32",data:[c.batchSize]},{type:"int32",data:[c.outChannels/c.inChannels]}];return a.runWebGPUProgram(d,[r,s],"float32",h)}var mhe={kernelName:fp,backendName:"webgpu",kernelFunc:hhe};function fhe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=C.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new che(c),h=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.filterHeight-1-c.padInfo.top,c.filterWidth-1-c.padInfo.left]},{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"int32",data:[c.outChannels/c.inChannels]}];return a.runWebGPUProgram(d,[r,s],r.dtype,h)}var ghe={kernelName:gp,backendName:"webgpu",kernelFunc:fhe},yhe=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return` + `}};function hhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=n,c=C.computeConv2DInfo(r.shape,p,i,o,l,u,!0),d=new phe(c),h=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"int32",data:[c.inHeight]},{type:"int32",data:[c.inWidth]},{type:"int32",data:[c.batchSize]},{type:"int32",data:[c.outChannels/c.inChannels]}];return a.runWebGPUProgram(d,[r,s],"float32",h)}var mhe={kernelName:Yd,backendName:"webgpu",kernelFunc:hhe};function fhe(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=n,c=C.computeConv2DInfo(p,s.shape,i,o,l,u,!0),d=new che(c),h=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.filterHeight-1-c.padInfo.top,c.filterWidth-1-c.padInfo.left]},{type:"int32",data:[c.filterHeight,c.filterWidth]},{type:"int32",data:[c.outHeight]},{type:"int32",data:[c.outWidth]},{type:"int32",data:[c.outChannels/c.inChannels]}];return a.runWebGPUProgram(d,[r,s],r.dtype,h)}var ghe={kernelName:Zd,backendName:"webgpu",kernelFunc:fhe},yhe=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="diag"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); @@ -7295,7 +7295,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, value); } } - `}};function xhe(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=ke({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new yhe(s),l=a.runWebGPUProgram(o,[i],i.dtype),u=ke({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var Ahe={kernelName:yu,backendName:"webgpu",kernelFunc:xhe},bhe=class{constructor(e){this.variableNames=["x","w"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return` + `}};function xhe(e){let{inputs:t,backend:a}=e,{x:n}=t,r=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=ke({inputs:{x:n},backend:a,attrs:{shape:[s]}}),o=new yhe(s),l=a.runWebGPUProgram(o,[i],i.dtype),u=ke({inputs:{x:l},backend:a,attrs:{shape:r}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var Ahe={kernelName:Ql,backendName:"webgpu",kernelFunc:xhe},bhe=class{constructor(e){this.variableNames=["x","w"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="dilation2d"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let neg_infinity = -3.4e38; @@ -7327,7 +7327,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, curVal); } } - `}};function vhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=[u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],d=new bhe(u);return a.runWebGPUProgram(d,[r,s],r.dtype,c)}var whe={kernelName:Pi,backendName:"webgpu",kernelFunc:vhe},khe=class{constructor(e,t){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.inShape,this.dispatchLayout=me(e.outShape),this.dispatch=de(this.dispatchLayout,e.outShape,this.workgroupSize),t!=="float32"&&t!=="int32")throw new Error(`Dilation2DBackpropInput only supports float32 and int32 + `}};function vhe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=C.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p=[u.padInfo.top,u.padInfo.left],c=[{type:"int32",data:[u.filterHeight,u.filterWidth]},{type:"int32",data:[...p]},{type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]}],d=new bhe(u);return a.runWebGPUProgram(d,[r,s],r.dtype,c)}var whe={kernelName:Ri,backendName:"webgpu",kernelFunc:vhe},khe=class{constructor(e,t){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.inShape,this.dispatchLayout=me(e.outShape),this.dispatch=de(this.dispatchLayout,e.outShape,this.workgroupSize),t!=="float32"&&t!=="int32")throw new Error(`Dilation2DBackpropInput only supports float32 and int32 types, does not support ${t} type.`);this.type=t,this.shaderKey="dilation2DBackpropInput"}getUserCode(){return` ${ue("index")} { if (index < uniforms.dySize) { @@ -7368,7 +7368,7 @@ return a / b;`,NJ=` let flatIndexIn = d + uniforms.xShape[3] * (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b)); let value = getDy(b, r, c, d); - ${xs("&result[flatIndexIn]","value",this.type)} + ${ys("&result[flatIndexIn]","value",this.type)} } } `}},Ihe=class{constructor(e,t,a){if(this.variableNames=["x","w","dy"],this.uniforms="filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.filterShape,this.dispatchLayout=me(e.outShape),this.dispatch=de(this.dispatchLayout,e.outShape,this.workgroupSize),a!=="float32"&&a!=="int32")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32 @@ -7411,10 +7411,10 @@ return a / b;`,NJ=` let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]); let value = getDy(b, r, c, d); - ${xs("&result[flatIndexIn]","value",this.type)} + ${ys("&result[flatIndexIn]","value",this.type)} } } - `}};function She(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n,p=C.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=s.dtype,d=new Ihe(p,s.shape,c),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[v.sizeFromShape(p.outShape)]}],m=Wa({backend:a,attrs:{shape:s.shape,value:0,dtype:c}});return a.runWebGPUProgram(d,[r,s,i],c,h,m)}var Che={kernelName:Xl,backendName:"webgpu",kernelFunc:She};function The(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n,p=C.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=r.dtype,d=new khe(p,c),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[v.sizeFromShape(p.outShape)]}],m=Wa({backend:a,attrs:{shape:p.inShape,value:0,dtype:c}});return a.runWebGPUProgram(d,[r,s,i],c,h,m)}var Nhe={kernelName:ql,backendName:"webgpu",kernelFunc:The},Rhe=class{constructor(e,t,a){this.variableNames=["Image"],this.uniforms="alpha: f32,",this.workgroupSize=[64,1,1],this.pixelsOpType=nu.DRAW,this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.type=t,this.textureFormat=a,this.shaderKey=`draw_${t}_${a}`}getUserCode(){let e,t=this.type==="float32"?"value":"value / 255.0";return e=` + `}};function She(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n,p=C.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=s.dtype,d=new Ihe(p,s.shape,c),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[v.sizeFromShape(p.outShape)]}],m=Wa({backend:a,attrs:{shape:s.shape,value:0,dtype:c}});return a.runWebGPUProgram(d,[r,s,i],c,h,m)}var Che={kernelName:Ml,backendName:"webgpu",kernelFunc:She};function The(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=n,p=C.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),c=r.dtype,d=new khe(p,c),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[v.sizeFromShape(p.outShape)]}],m=Wa({backend:a,attrs:{shape:p.inShape,value:0,dtype:c}});return a.runWebGPUProgram(d,[r,s,i],c,h,m)}var Nhe={kernelName:El,backendName:"webgpu",kernelFunc:The},Rhe=class{constructor(e,t,a){this.variableNames=["Image"],this.uniforms="alpha: f32,",this.workgroupSize=[64,1,1],this.pixelsOpType=Wl.DRAW,this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.type=t,this.textureFormat=a,this.shaderKey=`draw_${t}_${a}`}getUserCode(){let e,t=this.type==="float32"?"value":"value / 255.0";return e=` if (uniforms.numChannels == 1) { rgba[0] = ${t}; rgba[1] = ${t}; @@ -7437,7 +7437,7 @@ return a / b;`,NJ=` textureStore(outImage, vec2(coords.yx), rgba); } } - `}};function Ehe(e){let{inputs:t,backend:a,attrs:n}=e,{image:r}=t,{canvas:s,options:i}=n,[o,l]=r.shape.slice(0,2),{imageOptions:u}=i||{},p=(u==null?void 0:u.alpha)||1,c=a.device.features.has("bgra8unorm-storage")?"bgra8unorm":"rgba8unorm",d=[o,l],h=new Rhe(d,r.dtype,c);s.width=l,s.height=o;let m="webgpu",f=s.getContext(m),g;f||(g=new OffscreenCanvas(l,o),f=g.getContext(m));let y=r.shape.length===3?r.shape[2]:1;f.configure({device:a.device,format:c,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:"premultiplied"});let x="int32",A=a.makeTensorInfo(d,x),b=a.tensorMap.get(A.dataId);b.resource=f.getCurrentTexture(),b.external=!0;let w=[{type:"uint32",data:[y]},{type:"float32",data:[p]}];if(a.runWebGPUProgram(h,[r],x,w,A),g){let I=s.getContext("2d");if(!I)throw new Error("Please make sure this canvas has only been used for 2d or webgpu context!");I.drawImage(g,0,0)}return a.disposeData(A.dataId),r}var Mhe={kernelName:yp,backendName:"webgpu",kernelFunc:Ehe},Uk=aa({opType:Pe.MUL,cpuKernelImpl:Dde,supportsComplex:!0}),$he={kernelName:yo,backendName:"webgpu",kernelFunc:Uk};function Gk(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return dl(r,s,i,"sum",a)}var Phe={kernelName:Go,backendName:"webgpu",kernelFunc:Gk};function _he(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(r,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=C.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,m=[];for(let f=0;f=0&&(d=Gk({inputs:{x:d},backend:a,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(d)),h--)}for(let f of m)f!==d&&a.disposeData(f.dataId);return d}var Fhe={kernelName:xp,backendName:"webgpu",kernelFunc:_he},Dhe=at({opType:le.ELU}),Ohe={kernelName:Fi,backendName:"webgpu",kernelFunc:Dhe},zhe=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=new Th(Pe.ELU_DER,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],n.dtype)},Lhe={kernelName:xu,backendName:"webgpu",kernelFunc:zhe},Whe=aa({opType:Pe.EQUAL,dtype:"bool",cpuKernelImpl:vde}),Bhe={kernelName:Oi,backendName:"webgpu",kernelFunc:Whe},Vhe=at({opType:le.ERF}),Uhe={kernelName:Di,backendName:"webgpu",kernelFunc:Vhe},Ghe=at({opType:le.EXP,cpuKernelImpl:wde,dtype:"float32"}),Hhe={kernelName:zi,backendName:"webgpu",kernelFunc:Ghe};function Y1(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ke({inputs:{x:s},backend:n,attrs:{shape:o}})}var jhe={kernelName:Au,backendName:"webgpu",kernelFunc:Y1},qhe=at({opType:le.EXPM1,cpuKernelImpl:kde}),Xhe={kernelName:Li,backendName:"webgpu",kernelFunc:qhe},pA=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return` + `}};function Ehe(e){let{inputs:t,backend:a,attrs:n}=e,{image:r}=t,{canvas:s,options:i}=n,[o,l]=r.shape.slice(0,2),{imageOptions:u}=i||{},p=(u==null?void 0:u.alpha)||1,c=a.device.features.has("bgra8unorm-storage")?"bgra8unorm":"rgba8unorm",d=[o,l],h=new Rhe(d,r.dtype,c);s.width=l,s.height=o;let m="webgpu",f=s.getContext(m),g;f||(g=new OffscreenCanvas(l,o),f=g.getContext(m));let y=r.shape.length===3?r.shape[2]:1;f.configure({device:a.device,format:c,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:"premultiplied"});let x="int32",A=a.makeTensorInfo(d,x),b=a.tensorMap.get(A.dataId);b.resource=f.getCurrentTexture(),b.external=!0;let w=[{type:"uint32",data:[y]},{type:"float32",data:[p]}];if(a.runWebGPUProgram(h,[r],x,w,A),g){let I=s.getContext("2d");if(!I)throw new Error("Please make sure this canvas has only been used for 2d or webgpu context!");I.drawImage(g,0,0)}return a.disposeData(A.dataId),r}var Mhe={kernelName:Jd,backendName:"webgpu",kernelFunc:Ehe},kk=aa({opType:Pe.MUL,cpuKernelImpl:Dde,supportsComplex:!0}),$he={kernelName:ro,backendName:"webgpu",kernelFunc:kk};function Ik(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return qo(r,s,i,"sum",a)}var Phe={kernelName:"Sum",backendName:"webgpu",kernelFunc:Ik};function _he(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(r,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=C.getEinsumComputePath(o,l),c=p.length,d=null,h=i.length,m=[];for(let f=0;f=0&&(d=Ik({inputs:{x:d},backend:a,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(d)),h--)}for(let f of m)f!==d&&a.disposeData(f.dataId);return d}var Fhe={kernelName:Qd,backendName:"webgpu",kernelFunc:_he},Dhe=at({opType:le.ELU}),Ohe={kernelName:"Elu",backendName:"webgpu",kernelFunc:Dhe},zhe=e=>{let{inputs:t,backend:a}=e,{dy:n,y:r}=t,s=new oh(Pe.ELU_DER,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],n.dtype)},Lhe={kernelName:eu,backendName:"webgpu",kernelFunc:zhe},Whe=aa({opType:Pe.EQUAL,dtype:"bool",cpuKernelImpl:vde}),Bhe={kernelName:Mi,backendName:"webgpu",kernelFunc:Whe},Vhe=at({opType:le.ERF}),Uhe={kernelName:"Erf",backendName:"webgpu",kernelFunc:Vhe},Ghe=at({opType:le.EXP,cpuKernelImpl:wde,dtype:"float32"}),Hhe={kernelName:"Exp",backendName:"webgpu",kernelFunc:Ghe};function E1(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ke({inputs:{x:s},backend:n,attrs:{shape:o}})}var jhe={kernelName:tu,backendName:"webgpu",kernelFunc:E1},qhe=at({opType:le.EXPM1,cpuKernelImpl:kde}),Xhe={kernelName:$i,backendName:"webgpu",kernelFunc:qhe},H5=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return` fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 { ${this.component==="real"?"return real * expR - imag * expI;":"return real * expI + imag * expR;"} } @@ -7470,7 +7470,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, mulMatDFT(coords[0], coords[1])); } } - `}};function Hk(e,t,a){let n=a.tensorMap.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=[],l=ke({inputs:{x:e},backend:a,attrs:{shape:[i,s]}});o.push(l);let u=l.shape,p=new pA("real",u),c=new pA("imag",u),d=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],h=t?2*Math.PI:-2*Math.PI,m=t?u[1]:1,f=[{type:"float32",data:[h]},{type:"float32",data:[m]}],g=a.runWebGPUProgram(p,d,"float32",f);o.push(g);let y=a.runWebGPUProgram(c,d,"float32",f);o.push(y);let x=ul({inputs:{real:g,imag:y},backend:a});o.push(x);let A=ke({inputs:{x},backend:a,attrs:{shape:e.shape}});return o.forEach(b=>a.disposeData(b.dataId)),A}function Khe(e){let{inputs:t,backend:a}=e,{input:n}=t;return Hk(n,!1,a)}var Yhe={kernelName:Ap,backendName:"webgpu",kernelFunc:Khe},Zhe=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` + `}};function Sk(e,t,a){let n=a.tensorMap.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=[],l=ke({inputs:{x:e},backend:a,attrs:{shape:[i,s]}});o.push(l);let u=l.shape,p=new H5("real",u),c=new H5("imag",u),d=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],h=t?2*Math.PI:-2*Math.PI,m=t?u[1]:1,f=[{type:"float32",data:[h]},{type:"float32",data:[m]}],g=a.runWebGPUProgram(p,d,"float32",f);o.push(g);let y=a.runWebGPUProgram(c,d,"float32",f);o.push(y);let x=jo({inputs:{real:g,imag:y},backend:a});o.push(x);let A=ke({inputs:{x},backend:a,attrs:{shape:e.shape}});return o.forEach(b=>a.disposeData(b.dataId)),A}function Khe(e){let{inputs:t,backend:a}=e,{input:n}=t;return Sk(n,!1,a)}var Yhe={kernelName:"FFT",backendName:"webgpu",kernelFunc:Khe},Zhe=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7479,7 +7479,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, outputValue); } } - `}},Jhe={kernelName:Wi,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new Zhe(a.shape);return n.runWebGPUProgram(r,[a],a.dtype)}},Qhe=at({opType:le.FLOOR,cpuKernelImpl:Ide}),e0e={kernelName:Bi,backendName:"webgpu",kernelFunc:Qhe},t0e=aa({opType:Pe.FLOOR_DIV,cpuKernelImpl:Sde,dtype:"int32"}),a0e={kernelName:Vi,backendName:"webgpu",kernelFunc:t0e},n0e=class{constructor(e,t,a=!1){this.pixelsOpType=nu.FROM_PIXELS,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=a,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` + `}},Jhe={kernelName:Pi,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new Zhe(a.shape);return n.runWebGPUProgram(r,[a],a.dtype)}},Qhe=at({opType:le.FLOOR,cpuKernelImpl:Ide}),e0e={kernelName:_i,backendName:"webgpu",kernelFunc:Qhe},t0e=aa({opType:Pe.FLOOR_DIV,cpuKernelImpl:Sde,dtype:"int32"}),a0e={kernelName:Fi,backendName:"webgpu",kernelFunc:t0e},n0e=class{constructor(e,t,a=!1){this.pixelsOpType=Wl.FROM_PIXELS,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=a,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` @binding(1) @group(0) var src: ${this.importVideo?"texture_external":"texture_2d"}; ${ue("index")} { let flatIndex = index * uniforms.numChannels; @@ -7491,7 +7491,7 @@ return a / b;`,NJ=` } } } - `}},r0e={kernelName:Wd,backendName:"webgpu",kernelFunc:s0e},Dl,e1=B().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function s0e(e){let{inputs:t,backend:a,attrs:n}=e,{pixels:r}=t,{numChannels:s}=n;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[p,c]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[c,p,s],h=B().getBool("WEBGPU_IMPORT_EXTERNAL_TEXTURE")&&i,m=i||o;if(u||l||m){let x;if(h)x=a.device.importExternalTexture({source:r});else{if(m){let S=B().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Dl==null||S!==e1)&&(e1=S,Dl=document.createElement("canvas").getContext("2d",{willReadFrequently:e1})),Dl.canvas.width=p,Dl.canvas.height=c,Dl.drawImage(r,0,0,p,c),r=Dl.canvas}let $=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,E=a.textureManager.acquireTexture(d[1],d[0],"rgba8unorm",$);a.queue.copyExternalImageToTexture({source:r},{texture:E},[d[1],d[0]]),x=E}let A=v.sizeFromShape(d),b=v.computeStrides(d),w=new n0e(d,s,h),I=[{type:"uint32",data:[A]},{type:"uint32",data:[s]},{type:"uint32",data:[...b]}],T=a.makeTensorInfo([c,p],"int32"),N=a.tensorMap.get(T.dataId);N.resource=x;let M=a.runWebGPUProgram(w,[T],"int32",I);return a.disposeData(T.dataId),M}let f=r.data,g=f;if(s!=null&&s!==4){g=new Uint8Array(r.width*r.height*s);let x=f.length,A=0;for(let b=0;b(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); } } - `}},o0e={kernelName:Ui,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n,scale:r,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=a,p=[n,i,o],c=null;s!=null&&(c=s.shape,p.push(s));let d=null;r!=null&&(d=r.shape,p.push(r));let h=new i0e(n.shape,i.shape,o.shape,c,d),m=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,p,n.dtype,m)}};function l0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=C.convertConv2DDataFormat(p),g=C.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!1,f);return Lk({x:r,filter:s,convInfo:g,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:m,activation:h})}var u0e={kernelName:Jr,backendName:"webgpu",kernelFunc:l0e};function d0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:c,activation:d,leakyreluAlpha:h}=n,m=p;m==null&&(m=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let f=C.computeConv2DInfo(r.shape,s.shape,l,m,u,c,!0),g=[r,s],y=i!=null,x=o!=null;y&&g.push(i),x&&g.push(o);let A=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.inHeight,f.inWidth]}],b;return f.outHeight>4&&f.outWidth>4&&f.strideWidth<=2&&f.inChannels===f.outChannels&&f.dilationHeight===1&&f.dilationWidth===1&&f.inChannels%4===0?(b=new Bk(f,y,d,x),A.push({type:"int32",data:[b.virtualWidth]})):(b=new Vk(f,y,d,x),A.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]})),d==="leakyrelu"&&(A.push({type:"float32",data:[h]}),b.uniforms+=" alpha : f32,"),a.runWebGPUProgram(b,g,"float32",A)}var p0e={kernelName:Qr,backendName:"webgpu",kernelFunc:d0e},c0e=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Pt(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",` + `}},o0e={kernelName:Di,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n,scale:r,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=a,p=[n,i,o],c=null;s!=null&&(c=s.shape,p.push(s));let d=null;r!=null&&(d=r.shape,p.push(r));let h=new i0e(n.shape,i.shape,o.shape,c,d),m=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,p,n.dtype,m)}};function l0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=C.convertConv2DDataFormat(p),g=C.computeConv2DInfo(r.shape,s.shape,l,c,u,d,!1,f);return Ak({x:r,filter:s,convInfo:g,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:m,activation:h})}var u0e={kernelName:Jr,backendName:"webgpu",kernelFunc:l0e};function d0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dimRoundingMode:c,activation:d,leakyreluAlpha:h}=n,m=p;m==null&&(m=[1,1]),v.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let f=C.computeConv2DInfo(r.shape,s.shape,l,m,u,c,!0),g=[r,s],y=i!=null,x=o!=null;y&&g.push(i),x&&g.push(o);let A=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.inHeight,f.inWidth]}],b;return f.outHeight>4&&f.outWidth>4&&f.strideWidth<=2&&f.inChannels===f.outChannels&&f.dilationHeight===1&&f.dilationWidth===1&&f.inChannels%4===0?(b=new vk(f,y,d,x),A.push({type:"int32",data:[b.virtualWidth]})):(b=new wk(f,y,d,x),A.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]})),d==="leakyrelu"&&(A.push({type:"float32",data:[h]}),b.uniforms+=" alpha : f32,"),a.runWebGPUProgram(b,g,"float32",A)}var p0e={kernelName:Qr,backendName:"webgpu",kernelFunc:d0e},c0e=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Pt(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7518,7 +7518,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, getA(flattenIndex, coords[1])); } } - `}};function h0e(e){let{inputs:t,backend:a}=e,{params:n,indices:r}=t,s=r.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,p,c]=C.prepareAndValidate(n,r),d=ke({inputs:{x:r},backend:a,attrs:{shape:[u,i]}}),h=ke({inputs:{x:n},backend:a,attrs:{shape:[v.sizeFromShape(n.shape)/p,p]}});if(a.shouldExecuteOnCPU([n,r])||n.dtype==="string"){let x=a.readSync(r.dataId),A=a.bufferSync(n),b=Cde(x,A,n.dtype,u,i,p,c,n.shape,o);return a.makeTensorInfo(l,n.dtype,b.values)}let m=new c0e(i,[u,p]),f=[{type:"int32",data:[i]},{type:"int32",data:c}],g=a.runWebGPUProgram(m,[h,d],h.dtype,f),y=ke({inputs:{x:g},backend:a,attrs:{shape:l}});return a.disposeData(d.dataId),a.disposeData(h.dataId),a.disposeData(g.dataId),y}var m0e={kernelName:Gi,backendName:"webgpu",kernelFunc:h0e},f0e=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let e=g0e(this.aShape);return` + `}};function h0e(e){let{inputs:t,backend:a}=e,{params:n,indices:r}=t,s=r.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,p,c]=C.prepareAndValidate(n,r),d=ke({inputs:{x:r},backend:a,attrs:{shape:[u,i]}}),h=ke({inputs:{x:n},backend:a,attrs:{shape:[v.sizeFromShape(n.shape)/p,p]}});if(a.shouldExecuteOnCPU([n,r])||n.dtype==="string"){let x=a.readSync(r.dataId),A=a.bufferSync(n),b=Cde(x,A,n.dtype,u,i,p,c,n.shape,o);return a.makeTensorInfo(l,n.dtype,b.values)}let m=new c0e(i,[u,p]),f=[{type:"int32",data:[i]},{type:"int32",data:c}],g=a.runWebGPUProgram(m,[h,d],h.dtype,f),y=ke({inputs:{x:g},backend:a,attrs:{shape:l}});return a.disposeData(d.dataId),a.disposeData(h.dataId),a.disposeData(g.dataId),y}var m0e={kernelName:Oi,backendName:"webgpu",kernelFunc:h0e},f0e=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let e=g0e(this.aShape);return` ${ue("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); @@ -7527,13 +7527,13 @@ return a / b;`,NJ=` setOutputAtIndex(index, inBounds * getA(${e})); } } - `}};function g0e(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let n=0;na.disposeData(T.dataId)),a.makeTensorInfo(u.outputShape,I.dtype,I.values)}let f=new f0e(d.shape,m),g=a.runWebGPUProgram(f,[d,h],d.dtype);c.push(g);let y=ke({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(x=>a.disposeData(x.dataId)),y}var y0e={kernelName:vu,backendName:"webgpu",kernelFunc:jk},x0e=aa({opType:Pe.GREATER,cpuKernelImpl:Rde,dtype:"bool"}),A0e={kernelName:Hi,backendName:"webgpu",kernelFunc:x0e},b0e=aa({opType:Pe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:Nde}),v0e={kernelName:ji,backendName:"webgpu",kernelFunc:b0e};function w0e(e){let{inputs:t,backend:a}=e,{input:n}=t;return Hk(n,!0,a)}var k0e={kernelName:bp,backendName:"webgpu",kernelFunc:w0e},I0e=at({opType:le.IS_FINITE,dtype:"bool"}),S0e={kernelName:Xi,backendName:"webgpu",kernelFunc:I0e},C0e=at({opType:le.IS_INF,dtype:"bool"}),T0e={kernelName:Ki,backendName:"webgpu",kernelFunc:C0e},N0e=at({opType:le.IS_NAN,dtype:"bool"}),R0e={kernelName:Yi,backendName:"webgpu",kernelFunc:N0e};function E0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new td(r.shape,le.LEAKYRELU,"alpha : f32,");return a.runWebGPUProgram(o,[r],"float32",i)}var M0e={kernelName:Zi,backendName:"webgpu",kernelFunc:E0e},$0e=aa({opType:Pe.LESS,dtype:"bool",cpuKernelImpl:Mde}),P0e={kernelName:Ji,backendName:"webgpu",kernelFunc:$0e},_0e=aa({opType:Pe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Ede}),F0e={kernelName:Qi,backendName:"webgpu",kernelFunc:_0e},D0e=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return` + `}};function g0e(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let n=0;na.disposeData(T.dataId)),a.makeTensorInfo(u.outputShape,I.dtype,I.values)}let f=new f0e(d.shape,m),g=a.runWebGPUProgram(f,[d,h],d.dtype);c.push(g);let y=ke({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(x=>a.disposeData(x.dataId)),y}var y0e={kernelName:nu,backendName:"webgpu",kernelFunc:Ck},x0e=aa({opType:Pe.GREATER,cpuKernelImpl:Rde,dtype:"bool"}),A0e={kernelName:zi,backendName:"webgpu",kernelFunc:x0e},b0e=aa({opType:Pe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:Nde}),v0e={kernelName:Li,backendName:"webgpu",kernelFunc:b0e};function w0e(e){let{inputs:t,backend:a}=e,{input:n}=t;return Sk(n,!0,a)}var k0e={kernelName:ep,backendName:"webgpu",kernelFunc:w0e},I0e=at({opType:le.IS_FINITE,dtype:"bool"}),S0e={kernelName:Bi,backendName:"webgpu",kernelFunc:I0e},C0e=at({opType:le.IS_INF,dtype:"bool"}),T0e={kernelName:Vi,backendName:"webgpu",kernelFunc:C0e},N0e=at({opType:le.IS_NAN,dtype:"bool"}),R0e={kernelName:Ui,backendName:"webgpu",kernelFunc:N0e};function E0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new Du(r.shape,le.LEAKYRELU,"alpha : f32,");return a.runWebGPUProgram(o,[r],"float32",i)}var M0e={kernelName:Gi,backendName:"webgpu",kernelFunc:E0e},$0e=aa({opType:Pe.LESS,dtype:"bool",cpuKernelImpl:Mde}),P0e={kernelName:Hi,backendName:"webgpu",kernelFunc:$0e},_0e=aa({opType:Pe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Ede}),F0e={kernelName:ji,backendName:"webgpu",kernelFunc:_0e},D0e=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="start : f32, step : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="linSpace"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step); } } - `}};function O0e(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=(r-n)/(s-1),o=new D0e(s),l=[{type:"float32",data:[n]},{type:"float32",data:[i]}];return t.runWebGPUProgram(o,[],"float32",l)}var z0e={kernelName:eo,backendName:"webgpu",kernelFunc:O0e},L0e=at({opType:le.LOG,cpuKernelImpl:$de}),W0e={kernelName:to,backendName:"webgpu",kernelFunc:L0e},B0e=at({opType:le.LOG1P}),V0e={kernelName:ao,backendName:"webgpu",kernelFunc:B0e},U0e=aa({opType:Pe.LOGICAL_AND,dtype:"bool"}),G0e={kernelName:no,backendName:"webgpu",kernelFunc:U0e},H0e=at({opType:le.LOGICAL_NOT}),j0e={kernelName:ro,backendName:"webgpu",kernelFunc:H0e},q0e=aa({opType:Pe.LOGICAL_OR}),X0e={kernelName:so,backendName:"webgpu",kernelFunc:q0e},qk=` + `}};function O0e(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=(r-n)/(s-1),o=new D0e(s),l=[{type:"float32",data:[n]},{type:"float32",data:[i]}];return t.runWebGPUProgram(o,[],"float32",l)}var z0e={kernelName:qi,backendName:"webgpu",kernelFunc:O0e},L0e=at({opType:le.LOG,cpuKernelImpl:$de}),W0e={kernelName:"Log",backendName:"webgpu",kernelFunc:L0e},B0e=at({opType:le.LOG1P}),V0e={kernelName:Xi,backendName:"webgpu",kernelFunc:B0e},U0e=aa({opType:Pe.LOGICAL_AND,dtype:"bool"}),G0e={kernelName:Ki,backendName:"webgpu",kernelFunc:U0e},H0e=at({opType:le.LOGICAL_NOT}),j0e={kernelName:Yi,backendName:"webgpu",kernelFunc:H0e},q0e=aa({opType:Pe.LOGICAL_OR}),X0e={kernelName:Zi,backendName:"webgpu",kernelFunc:q0e},Tk=` var powValue = 0.0; let basis = uniforms.bias + uniforms.alpha * sum; if (uniforms.beta == 0.5) { @@ -7561,7 +7561,7 @@ return a / b;`,NJ=` sum = sum + z * z; } } - ${qk} + ${Tk} setOutputAtIndex(index, x * powValue); } @@ -7594,11 +7594,11 @@ return a / b;`,NJ=` let z = lrnSub[index + i]; sum = sum + z * z; } - ${qk} + ${Tk} setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue); } - } `}};function Z0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u;s>16?u=new K0e(r.shape):u=new Y0e(r.shape,s);let p=[{type:"int32",data:[s]},{type:"float32",data:[i]},{type:"float32",data:[o]},{type:"float32",data:[l]}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var J0e={kernelName:io,backendName:"webgpu",kernelFunc:Z0e},Q0e=class{constructor(e){this.outputShape=[],this.variableNames=["inputImage","outputImage","dy"],this.uniforms="depthRadius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn_grad"}getUserCode(){return` + } `}};function Z0e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u;s>16?u=new K0e(r.shape):u=new Y0e(r.shape,s);let p=[{type:"int32",data:[s]},{type:"float32",data:[i]},{type:"float32",data:[o]},{type:"float32",data:[l]}];return a.runWebGPUProgram(u,[r],r.dtype,p)}var J0e={kernelName:"LRN",backendName:"webgpu",kernelFunc:Z0e},Q0e=class{constructor(e){this.outputShape=[],this.variableNames=["inputImage","outputImage","dy"],this.uniforms="depthRadius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn_grad"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); @@ -7648,7 +7648,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, result); } } - `}};function eme(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new Q0e(r.shape),d=[{type:"int32",data:[o]},{type:"float32",data:[l]},{type:"float32",data:[u]},{type:"float32",data:[p]}];return a.runWebGPUProgram(c,[r,s,i],r.dtype,d)}var tme={kernelName:wu,backendName:"webgpu",kernelFunc:eme},ame=aa({opType:Pe.MAX,cpuKernelImpl:_de}),nme={kernelName:lo,backendName:"webgpu",kernelFunc:ame};function rme(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=C.computePool2DInfo(r.shape,s,i,1,o,l);return Fk(r,u,"max",a)}var sme={kernelName:uo,backendName:"webgpu",kernelFunc:rme};function ime(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=C.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new Z3(c,"max"),h=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.front,c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.inDepth,c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]}];return a.runWebGPUProgram(d,[r],r.dtype,h)}var ome={kernelName:ku,backendName:"webgpu",kernelFunc:ime},lme=class{constructor(e){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, + `}};function eme(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=n,c=new Q0e(r.shape),d=[{type:"int32",data:[o]},{type:"float32",data:[l]},{type:"float32",data:[u]},{type:"float32",data:[p]}];return a.runWebGPUProgram(c,[r,s,i],r.dtype,d)}var tme={kernelName:ru,backendName:"webgpu",kernelFunc:eme},ame=aa({opType:Pe.MAX,cpuKernelImpl:_de}),nme={kernelName:Ji,backendName:"webgpu",kernelFunc:ame};function rme(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=C.computePool2DInfo(r.shape,s,i,1,o,l);return fk(r,u,"max",a)}var sme={kernelName:Qi,backendName:"webgpu",kernelFunc:rme};function ime(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,p=[1,1,1],c=C.computePool3DInfo(r.shape,s,i,p,o,u,l),d=new M3(c,"max"),h=[{type:"int32",data:[c.strideDepth,c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.front,c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.inDepth,c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth]}];return a.runWebGPUProgram(d,[r],r.dtype,h)}var ome={kernelName:su,backendName:"webgpu",kernelFunc:ime},lme=class{constructor(e){this.variableNames=["dy","maxPos"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="maxPool2DBackprop"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -7751,7 +7751,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, dotProd); } } - `}};function dme(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,c,u,p),h=new Z3(d,"max",!0),m=[{type:"int32",data:[d.strideDepth,d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.front,d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.inDepth,d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth]}],f=a.runWebGPUProgram(h,[i],"int32",m),g=new ume(d);m=[{type:"int32",data:[d.strideDepth,d.strideHeight,d.strideWidth]},{type:"int32",data:[d.effectiveFilterDepth-1-d.padInfo.front,d.effectiveFilterHeight-1-d.padInfo.top,d.effectiveFilterWidth-1-d.padInfo.left]},{type:"int32",data:[d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth]},{type:"int32",data:[d.outDepth]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]}];let y=a.runWebGPUProgram(g,[r,f],i.dtype,m);return a.disposeData(f.dataId),y}var pme={kernelName:kp,backendName:"webgpu",kernelFunc:dme};function cme(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;q3([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=C.computePool2DInfo(o.shape,l,u,1,p,c),h=new sp(d,"max",!0),m=[{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]}],f=a.runWebGPUProgram(h,[o],"int32",m),g=new lme(d);m=[{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.effectiveFilterHeight-1-d.padInfo.top,d.effectiveFilterWidth-1-d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]}];let y=a.runWebGPUProgram(g,[r,f],o.dtype,m);return a.disposeData(f.dataId),y}var hme={kernelName:wp,backendName:"webgpu",kernelFunc:cme};function mme(e){let{inputs:t,backend:a,attrs:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=n,{x:l}=t;v.assert(l.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${l.shape.length}.`);let u=[1,1];v.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=C.computePool2DInfo(l.shape,r,s,u,i),c=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]}],d=new sp(p,"max",!1),h=a.runWebGPUProgram(d,[l],l.dtype,c);d=new sp(p,"max",!0,!0,o);let m=a.runWebGPUProgram(d,[l],"int32",c);return[h,m]}var fme={kernelName:Iu,backendName:"webgpu",kernelFunc:mme};function gme(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return dl(r,s,i,"min",a)}var yme={kernelName:co,backendName:"webgpu",kernelFunc:gme},xme=aa({opType:Pe.MIN,cpuKernelImpl:Fde}),Ame={kernelName:ho,backendName:"webgpu",kernelFunc:xme},bme=class{constructor(e,t,a){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,r)=>n[0]+e[r]+n[1]),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,r)=>{this.uniforms+=` pad${r} : vec2,`}),this.offset=a==="reflect"?0:1,this.shaderKey=`mirrorPad_${a}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),a=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",r=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=Pt(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` + `}};function dme(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=n,c=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,c,u,p),h=new M3(d,"max",!0),m=[{type:"int32",data:[d.strideDepth,d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.front,d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.inDepth,d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth]}],f=a.runWebGPUProgram(h,[i],"int32",m),g=new ume(d);m=[{type:"int32",data:[d.strideDepth,d.strideHeight,d.strideWidth]},{type:"int32",data:[d.effectiveFilterDepth-1-d.padInfo.front,d.effectiveFilterHeight-1-d.padInfo.top,d.effectiveFilterWidth-1-d.padInfo.left]},{type:"int32",data:[d.effectiveFilterDepth,d.effectiveFilterHeight,d.effectiveFilterWidth]},{type:"int32",data:[d.outDepth]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]}];let y=a.runWebGPUProgram(g,[r,f],i.dtype,m);return a.disposeData(f.dataId),y}var pme={kernelName:np,backendName:"webgpu",kernelFunc:dme};function cme(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;T3([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:c}=n,d=C.computePool2DInfo(o.shape,l,u,1,p,c),h=new Wd(d,"max",!0),m=[{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]}],f=a.runWebGPUProgram(h,[o],"int32",m),g=new lme(d);m=[{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.effectiveFilterHeight-1-d.padInfo.top,d.effectiveFilterWidth-1-d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]},{type:"int32",data:[d.outHeight]},{type:"int32",data:[d.outWidth]}];let y=a.runWebGPUProgram(g,[r,f],o.dtype,m);return a.disposeData(f.dataId),y}var hme={kernelName:ap,backendName:"webgpu",kernelFunc:cme};function mme(e){let{inputs:t,backend:a,attrs:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=n,{x:l}=t;v.assert(l.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${l.shape.length}.`);let u=[1,1];v.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=C.computePool2DInfo(l.shape,r,s,u,i),c=[{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]},{type:"int32",data:[p.effectiveFilterHeight,p.effectiveFilterWidth]}],d=new Wd(p,"max",!1),h=a.runWebGPUProgram(d,[l],l.dtype,c);d=new Wd(p,"max",!0,!0,o);let m=a.runWebGPUProgram(d,[l],"int32",c);return[h,m]}var fme={kernelName:iu,backendName:"webgpu",kernelFunc:mme};function gme(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return qo(r,s,i,"min",a)}var yme={kernelName:"Min",backendName:"webgpu",kernelFunc:gme},xme=aa({opType:Pe.MIN,cpuKernelImpl:Fde}),Ame={kernelName:to,backendName:"webgpu",kernelFunc:xme},bme=class{constructor(e,t,a){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,r)=>n[0]+e[r]+n[1]),this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,r)=>{this.uniforms+=` pad${r} : vec2,`}),this.offset=a==="reflect"?0:1,this.shaderKey=`mirrorPad_${a}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),a=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",r=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=Pt(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` ${ue("index")} { if (index < uniforms.size) { let start = ${i}(${t}); @@ -7768,7 +7768,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, getX(${o})); } } - `}},vme={kernelName:mo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{paddings:r,mode:s}=t,i=a,o=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new bme(n.shape,r,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}},wme=aa({opType:Pe.MOD}),kme={kernelName:fo,backendName:"webgpu",kernelFunc:wme},Ime=class{constructor(e,t){this.variableNames=["probs"],this.outputShape=[],this.uniforms="seed : f32, numOutcomes: i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="multinomial"}getUserCode(){return` + `}},vme={kernelName:ao,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{paddings:r,mode:s}=t,i=a,o=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new bme(n.shape,r,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}},wme=aa({opType:Pe.MOD}),kme={kernelName:"Mod",backendName:"webgpu",kernelFunc:wme},Ime=class{constructor(e,t){this.variableNames=["probs"],this.outputShape=[],this.uniforms="seed : f32, numOutcomes: i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="multinomial"}getUserCode(){return` //Based on the work of Dave Hoskins //https://www.shadertoy.com/view/4djSRW fn random (seed : f32, resultUV : vec2) -> f32 { @@ -7860,7 +7860,7 @@ return a / b;`,NJ=` setOutputAtCoords(row, col, value); } } - `}};function Xk(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=ke({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape)/r.shape[s],r.shape[s]]}}),o=new Sme(i.shape),l=a.runWebGPUProgram(o,[i],r.dtype),u=ke({inputs:{x:l},backend:a,attrs:{shape:r.shape}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var Cme={kernelName:Ho,backendName:"webgpu",kernelFunc:Xk};function Tme(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:Xk({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new Ime(u,s),d=[{type:"float32",data:[i]},{type:"int32",data:[p]}],h=a.runWebGPUProgram(c,[l],"int32",d);return o||a.disposeData(l.dataId),h}var Nme={kernelName:go,backendName:"webgpu",kernelFunc:Tme};function Rme(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.tensorMap.get(n.dataId),[i,o]=Ode(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r=new td(n.shape,le.NEG);return a.runWebGPUProgram(r,[n],n.dtype)}var Eme={kernelName:Su,backendName:"webgpu",kernelFunc:Rme};function Mme(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),{selectedIndices:c}=En.nonMaxSuppressionV3Impl(u,p,i,o,l);return a.makeTensorInfo([c.length],"int32",new Int32Array(c))}var $me={kernelName:Ao,backendName:"webgpu",kernelFunc:Mme};function Pme(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),d=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=En.nonMaxSuppressionV5Impl(p,c,d,h,m,f);return[a.makeTensorInfo([g.length],"int32",new Int32Array(g)),a.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var _me={kernelName:bo,backendName:"webgpu",kernelFunc:Pme},Fme=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` + `}};function Nk(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=ke({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape)/r.shape[s],r.shape[s]]}}),o=new Sme(i.shape),l=a.runWebGPUProgram(o,[i],r.dtype),u=ke({inputs:{x:l},backend:a,attrs:{shape:r.shape}});return a.disposeData(i.dataId),a.disposeData(l.dataId),u}var Cme={kernelName:Eo,backendName:"webgpu",kernelFunc:Nk};function Tme(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?r:Nk({inputs:{logits:r},backend:a,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],c=new Ime(u,s),d=[{type:"float32",data:[i]},{type:"int32",data:[p]}],h=a.runWebGPUProgram(c,[l],"int32",d);return o||a.disposeData(l.dataId),h}var Nme={kernelName:no,backendName:"webgpu",kernelFunc:Tme};function Rme(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.tensorMap.get(n.dataId),[i,o]=Ode(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r=new Du(n.shape,le.NEG);return a.runWebGPUProgram(r,[n],n.dtype)}var Eme={kernelName:"Neg",backendName:"webgpu",kernelFunc:Rme};function Mme(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=a.readSync(r.dataId),p=a.readSync(s.dataId),{selectedIndices:c}=En.nonMaxSuppressionV3Impl(u,p,i,o,l);return a.makeTensorInfo([c.length],"int32",new Int32Array(c))}var $me={kernelName:io,backendName:"webgpu",kernelFunc:Mme};function Pme(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,p=a.readSync(r.dataId),c=a.readSync(s.dataId),d=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=En.nonMaxSuppressionV5Impl(p,c,d,h,m,f);return[a.makeTensorInfo([g.length],"int32",new Int32Array(g)),a.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var _me={kernelName:oo,backendName:"webgpu",kernelFunc:Pme},Fme=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` ${ue("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7868,7 +7868,7 @@ return a / b;`,NJ=` f32(i32(round(getX(coords.x))) == coords.y))); } } - `}};function Dme(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new Fme(u,i),c=ke({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=[{type:"float32",data:[o]},{type:"float32",data:[l]}],h=a.runWebGPUProgram(p,[c],s,d);a.disposeData(c.dataId);let m=[...r.shape,i],f=ke({inputs:{x:h},backend:a,attrs:{shape:m}});return a.disposeData(h.dataId),f}var Ome={kernelName:vo,backendName:"webgpu",kernelFunc:Dme};function Rh(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=tc({inputs:{input:n},backend:a}),s=Rh({inputs:{x:r},backend:a}),i=h0({inputs:{input:n},backend:a}),o=Rh({inputs:{x:i},backend:a}),l=ul({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Wa({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var zme={kernelName:Vu,backendName:"webgpu",kernelFunc:Rh};function Kk(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=tc({inputs:{input:n},backend:a}),s=Kk({inputs:{x:r},backend:a}),i=h0({inputs:{input:n},backend:a}),o=Rh({inputs:{x:i},backend:a}),l=ul({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Wa({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var Lme={kernelName:Tu,backendName:"webgpu",kernelFunc:Kk};function Wme(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return Y1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=Y1({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=zk({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeData(p.dataId)),u}var Bme={kernelName:Nu,backendName:"webgpu",kernelFunc:Wme};function Yk(e,t=!1){let a=e.length,n=Pt(a),r=e.map((c,d)=>`uniforms.pad${d}[0]`).join(","),s=e.map((c,d)=>`uniforms.pad${d}[0] + uniforms.xShape${a>1?`[${d}]`:""}`).join(","),i=a>1?`${n}(${r})`:`${r}`,o=a>1?`${n}(${s})`:`${s}`,l=a>1?"any(paddedCoords < start)":"paddedCoords < start",u=a>1?"any(paddedCoords >= end)":"paddedCoords >= end",p=a>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a):"coords";return` + `}};function Dme(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=v.sizeFromShape(r.shape),p=new Fme(u,i),c=ke({inputs:{x:r},backend:a,attrs:{shape:[u]}}),d=[{type:"float32",data:[o]},{type:"float32",data:[l]}],h=a.runWebGPUProgram(p,[c],s,d);a.disposeData(c.dataId);let m=[...r.shape,i],f=ke({inputs:{x:h},backend:a,attrs:{shape:m}});return a.disposeData(h.dataId),f}var Ome={kernelName:lo,backendName:"webgpu",kernelFunc:Dme};function uh(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=Fp({inputs:{input:n},backend:a}),s=uh({inputs:{x:r},backend:a}),i=qh({inputs:{input:n},backend:a}),o=uh({inputs:{x:i},backend:a}),l=jo({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Wa({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var zme={kernelName:ku,backendName:"webgpu",kernelFunc:uh};function Rk(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=Fp({inputs:{input:n},backend:a}),s=Rk({inputs:{x:r},backend:a}),i=qh({inputs:{input:n},backend:a}),o=uh({inputs:{x:i},backend:a}),l=jo({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return Wa({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var Lme={kernelName:lu,backendName:"webgpu",kernelFunc:Rk};function Wme(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return E1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let c=E1({inputs:{input:p},backend:a,attrs:{dim:r}});return o.push(c),c}),u=xk({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(p=>a.disposeData(p.dataId)),u}var Bme={kernelName:uu,backendName:"webgpu",kernelFunc:Wme};function Ek(e,t=!1){let a=e.length,n=Pt(a),r=e.map((c,d)=>`uniforms.pad${d}[0]`).join(","),s=e.map((c,d)=>`uniforms.pad${d}[0] + uniforms.xShape${a>1?`[${d}]`:""}`).join(","),i=a>1?`${n}(${r})`:`${r}`,o=a>1?`${n}(${s})`:`${s}`,l=a>1?"any(paddedCoords < start)":"paddedCoords < start",u=a>1?"any(paddedCoords >= end)":"paddedCoords >= end",p=a>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a):"coords";return` let start = ${i}; let end = ${o}; if (${l} || ${u}) { @@ -7881,10 +7881,10 @@ return a / b;`,NJ=` ${ue("index")} { if (index < uniforms.size) { let paddedCoords = getCoordsFromIndex(index); - ${Yk(this.xShape)} + ${Ek(this.xShape)} } } - `}},Ume=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return tn({inputs:{x:r},backend:a});if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return Wa({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new Vme(r.shape,s);return a.runWebGPUProgram(l,[r],r.dtype,o)},Gme={kernelName:wo,backendName:"webgpu",kernelFunc:Ume},Hme=aa({opType:Pe.POW}),jme={kernelName:ko,backendName:"webgpu",kernelFunc:Hme};function qme(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=new Th(Pe.PRELU,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],"float32")}var Xme={kernelName:Io,backendName:"webgpu",kernelFunc:qme};function Kme(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return dl(r,s,i,"prod",a)}var Yme={kernelName:So,backendName:"webgpu",kernelFunc:Kme},Zme=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=Wde(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},Jme={kernelName:Ru,backendName:"webgpu",kernelFunc:Zme},Qme=aa({opType:Pe.DIV}),efe={kernelName:_i,backendName:"webgpu",kernelFunc:Qme},tfe=at({opType:le.RECIPROCAL}),afe={kernelName:Co,backendName:"webgpu",kernelFunc:tfe},nfe=at({opType:le.RELU}),rfe={kernelName:To,backendName:"webgpu",kernelFunc:nfe},sfe=at({opType:le.RELU6}),ife={kernelName:Eo,backendName:"webgpu",kernelFunc:sfe},ofe=class{constructor(e,t,a){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` + `}},Ume=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return tn({inputs:{x:r},backend:a});if(v.sizeFromShape(r.shape)===0){let u=s.map((p,c)=>p[0]+r.shape[c]+p[1]);return Wa({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new Vme(r.shape,s);return a.runWebGPUProgram(l,[r],r.dtype,o)},Gme={kernelName:uo,backendName:"webgpu",kernelFunc:Ume},Hme=aa({opType:Pe.POW}),jme={kernelName:"Pow",backendName:"webgpu",kernelFunc:Hme};function qme(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=new oh(Pe.PRELU,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],"float32")}var Xme={kernelName:po,backendName:"webgpu",kernelFunc:qme};function Kme(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return qo(r,s,i,"prod",a)}var Yme={kernelName:co,backendName:"webgpu",kernelFunc:Kme},Zme=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=Wde(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},Jme={kernelName:du,backendName:"webgpu",kernelFunc:Zme},Qme=aa({opType:Pe.DIV}),efe={kernelName:Ei,backendName:"webgpu",kernelFunc:Qme},tfe=at({opType:le.RECIPROCAL}),afe={kernelName:ho,backendName:"webgpu",kernelFunc:tfe},nfe=at({opType:le.RELU}),rfe={kernelName:mo,backendName:"webgpu",kernelFunc:nfe},sfe=at({opType:le.RELU6}),ife={kernelName:yo,backendName:"webgpu",kernelFunc:sfe},ofe=class{constructor(e,t,a){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7927,7 +7927,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, newValue); } } - `}};function lfe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[o?.5:0]}],h=new ofe(r.shape,l,u);return a.runWebGPUProgram(h,[r],"float32",d)}var ufe={kernelName:Ro,backendName:"webgpu",kernelFunc:lfe},dfe=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32, + `}};function lfe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[o?.5:0]}],h=new ofe(r.shape,l,u);return a.runWebGPUProgram(h,[r],"float32",d)}var ufe={kernelName:go,backendName:"webgpu",kernelFunc:lfe},dfe=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32, invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeBilinearBackprop_${t}`}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -8002,7 +8002,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, accumulator); } } - `}};function pfe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,[,o,l]=r.shape,[,u,p]=s.shape,c=[i&&u>1?o-1:o,i&&p>1?l-1:l],d=[i&&u>1?u-1:u,i&&p>1?p-1:p],h=c[0]/d[0],m=c[1]/d[1],f=1/h,g=1/m,y=Math.ceil(f)*2+2,x=Math.ceil(g)*2+2,A=new dfe(r.shape,i),b=[{type:"int32",data:c},{type:"int32",data:d},{type:"float32",data:[h]},{type:"float32",data:[m]},{type:"float32",data:[f]},{type:"float32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[x]}];return a.runWebGPUProgram(A,[s],s.dtype,b)}var cfe={kernelName:$u,backendName:"webgpu",kernelFunc:pfe},hfe=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":e="vec2(rc) * effectiveInputOverOutputRatioRC",` + `}};function pfe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,[,o,l]=r.shape,[,u,p]=s.shape,c=[i&&u>1?o-1:o,i&&p>1?l-1:l],d=[i&&u>1?u-1:u,i&&p>1?p-1:p],h=c[0]/d[0],m=c[1]/d[1],f=1/h,g=1/m,y=Math.ceil(f)*2+2,x=Math.ceil(g)*2+2,A=new dfe(r.shape,i),b=[{type:"int32",data:c},{type:"int32",data:d},{type:"float32",data:[h]},{type:"float32",data:[m]},{type:"float32",data:[f]},{type:"float32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[x]}];return a.runWebGPUProgram(A,[s],s.dtype,b)}var cfe={kernelName:hu,backendName:"webgpu",kernelFunc:pfe},hfe=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":e="vec2(rc) * effectiveInputOverOutputRatioRC",` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -8033,7 +8033,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, newValue); } } - `}};function mfe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[s?.5:0]}],h=new hfe(r.shape,l,u,i);return a.runWebGPUProgram(h,[r],r.dtype,d)}var ffe={kernelName:No,backendName:"webgpu",kernelFunc:mfe},gfe=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32, + `}};function mfe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,p=s&&l>1?1:0,c=s&&u>1?1:0,d=[{type:"float32",data:[p,c]},{type:"float32",data:[s?.5:0]}],h=new hfe(r.shape,l,u,i);return a.runWebGPUProgram(h,[r],r.dtype,d)}var ffe={kernelName:fo,backendName:"webgpu",kernelFunc:mfe},gfe=class{constructor(e,t){this.variableNames=["dy"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeNearestNeigborBackprop_${t}`}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -8093,7 +8093,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, accumulator); } } - `}};function yfe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,[,o,l]=r.shape,[,u,p]=s.shape,c=[i&&u>1?o-1:o,i&&p>1?l-1:l],d=[i&&u>1?u-1:u,i&&p>1?p-1:p],h=c[0]/d[0],m=c[1]/d[1],f=1/h,g=1/m,y=Math.ceil(f)*2+2,x=Math.ceil(g)*2+2,A=new gfe(r.shape,i),b=[{type:"int32",data:c},{type:"int32",data:d},{type:"float32",data:[f]},{type:"float32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[x]}];return a.runWebGPUProgram(A,[s],s.dtype,b)}var xfe={kernelName:Mu,backendName:"webgpu",kernelFunc:yfe},Afe=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` + `}};function yfe(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,[,o,l]=r.shape,[,u,p]=s.shape,c=[i&&u>1?o-1:o,i&&p>1?l-1:l],d=[i&&u>1?u-1:u,i&&p>1?p-1:p],h=c[0]/d[0],m=c[1]/d[1],f=1/h,g=1/m,y=Math.ceil(f)*2+2,x=Math.ceil(g)*2+2,A=new gfe(r.shape,i),b=[{type:"int32",data:c},{type:"int32",data:d},{type:"float32",data:[f]},{type:"float32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[x]}];return a.runWebGPUProgram(A,[s],s.dtype,b)}var xfe={kernelName:cu,backendName:"webgpu",kernelFunc:yfe},Afe=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` // Using uniform variables as judging conditions, so the function has // coherent execution within all threads. @@ -8123,7 +8123,7 @@ return a / b;`,NJ=` reverseCoords[1], reverseCoords[2], reverseCoords[3])); } } - `}};function bfe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length;if(i===0)return tn({inputs:{x:r},backend:a});let o=r.shape,l=[1,1,1,1];o.forEach((g,y)=>{let x=y+4-i;l[x]=g});let u=v.parseAxisParam(s,r.shape),p=[0,0,0,0];u.forEach(g=>{let y=g+4-i;p[y]=1});let c=[{type:"int32",data:p}],d=ke({inputs:{x:r},backend:a,attrs:{shape:l}}),h=new Afe(l),m=a.runWebGPUProgram(h,[d],d.dtype,c);a.disposeData(d.dataId);let f=ke({inputs:{x:m},backend:a,attrs:{shape:o}});return a.disposeData(m.dataId),f}var vfe={kernelName:Mo,backendName:"webgpu",kernelFunc:bfe},wfe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, + `}};function bfe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length;if(i===0)return tn({inputs:{x:r},backend:a});let o=r.shape,l=[1,1,1,1];o.forEach((g,y)=>{let x=y+4-i;l[x]=g});let u=v.parseAxisParam(s,r.shape),p=[0,0,0,0];u.forEach(g=>{let y=g+4-i;p[y]=1});let c=[{type:"int32",data:p}],d=ke({inputs:{x:r},backend:a,attrs:{shape:l}}),h=new Afe(l),m=a.runWebGPUProgram(h,[d],d.dtype,c);a.disposeData(d.dataId);let f=ke({inputs:{x:m},backend:a,attrs:{shape:o}});return a.disposeData(m.dataId),f}var vfe={kernelName:xo,backendName:"webgpu",kernelFunc:bfe},wfe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -8144,7 +8144,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, outputValue); } } - `}},kfe={kernelName:el,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new wfe(n.shape,s),[u,p]=C.getImageCenter(i,n.shape[1],n.shape[2]),c=[{type:"float32",data:[u]},{type:"float32",data:[p]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof s=="number"?c.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):c.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,c)}},Ife=at({opType:le.ROUND}),Sfe={kernelName:$o,backendName:"webgpu",kernelFunc:Ife},Cfe=at({opType:le.RSQRT,cpuKernelImpl:Bde}),Tfe={kernelName:Po,backendName:"webgpu",kernelFunc:Cfe},zd=class{constructor(e,t,a,n,r,s,i,o=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.sumDupeIndices=o,this.dispatchLayout=me(e),this.dispatch=de(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${a}_${n}_${this.sliceDimGreaterThanOne}_${i}_${o}_${r.length}`;let l=Pt(r.length);this.uniforms=`sliceDim : i32, strides: ${l}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=a}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,a=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",r="";this.dispatchLayout.x.length===1?(n="flattenedIndex",r=` + `}},kfe={kernelName:Oo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new wfe(n.shape,s),[u,p]=C.getImageCenter(i,n.shape[1],n.shape[2]),c=[{type:"float32",data:[u]},{type:"float32",data:[p]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof s=="number"?c.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):c.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,c)}},Ife=at({opType:le.ROUND}),Sfe={kernelName:Ao,backendName:"webgpu",kernelFunc:Ife},Cfe=at({opType:le.RSQRT,cpuKernelImpl:Bde}),Tfe={kernelName:bo,backendName:"webgpu",kernelFunc:Cfe},Ad=class{constructor(e,t,a,n,r,s,i,o=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.sumDupeIndices=o,this.dispatchLayout=me(e),this.dispatch=de(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${a}_${n}_${this.sliceDimGreaterThanOne}_${i}_${o}_${r.length}`;let l=Pt(r.length);this.uniforms=`sliceDim : i32, strides: ${l}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=a}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,a=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",r="";this.dispatchLayout.x.length===1?(n="flattenedIndex",r=` fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 { return index; } @@ -8170,12 +8170,12 @@ return a / b;`,NJ=` flattenedIndex = flattenedIndex + indexInside * ${a}; } let updateValue = - ${Hs(this.type)}(${s}); + ${Gs(this.type)}(${s}); let flatIndex = getOutputIndexFromCoords(${n}); - ${this.sumDupeIndices?xs("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast(updateValue));"} + ${this.sumDupeIndices?ys("&result[flatIndex]","updateValue",this.type):"atomicStore(&result[flatIndex], bitcast(updateValue));"} } - }`}};function Nfe(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=C.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=ke({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),m=ke({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),f=m.dtype,g=Wa({backend:a,attrs:{shape:d,value:0,dtype:f}}),y=v.sizeFromShape(m.shape),x=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[y]}],A=new zd(m.shape,o,h.shape.length,m.shape.length,p,d,f),b=a.runWebGPUProgram(A,[m,h],f,x,g),w=ke({inputs:{x:b},backend:a,attrs:{shape:i}});return a.disposeData(h.dataId),a.disposeData(m.dataId),a.disposeData(b.dataId),w}var Rfe={kernelName:_o,backendName:"webgpu",kernelFunc:Nfe},Efe=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return` + }`}};function Nfe(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=C.calculateShapes(s,r,i),d=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=ke({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),m=ke({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),f=m.dtype,g=Wa({backend:a,attrs:{shape:d,value:0,dtype:f}}),y=v.sizeFromShape(m.shape),x=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[y]}],A=new Ad(m.shape,o,h.shape.length,m.shape.length,p,d,f),b=a.runWebGPUProgram(A,[m,h],f,x,g),w=ke({inputs:{x:b},backend:a,attrs:{shape:i}});return a.disposeData(h.dataId),a.disposeData(m.dataId),a.disposeData(b.dataId),w}var Rfe={kernelName:vo,backendName:"webgpu",kernelFunc:Nfe},Efe=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return` fn findBound(batch: i32, value: f32) -> i32 { var left = i32(0); var right = uniforms.numInputs; @@ -8197,7 +8197,7 @@ return a / b;`,NJ=` setOutputAtIndexI32(index, findBound(coords[0], value)); } } - `}};function Mfe(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n,o=new Efe([s.shape[0],s.shape[1]],i),l=[{type:"int32",data:[r.shape[1]]}];return a.runWebGPUProgram(o,[r,s],"int32",l)}var $fe={kernelName:Do,backendName:"webgpu",kernelFunc:Mfe},Pfe=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=a,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],r=[];for(let s=0;s4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],r=[];for(let s=0;s{this.uniforms+=` pad${l} : vec2,`}),this.shaderKey=`spaceToBatchND_${r}`}getUserCode(){let e=Pt(this.outputShape.length),t=Mk(this.newDim);return` - ${lh(this.paddedXShape,"PaddedX")} + `}};function _fe(e){let{inputs:t,backend:a}=e,{condition:n,t:r,e:s}=t,i=new Pfe(n.shape.length,r.shape,r.shape.length);return a.runWebGPUProgram(i,[n,r,s],ca(r.dtype,s.dtype))}var Ffe={kernelName:mu,backendName:"webgpu",kernelFunc:_fe},Dfe=at({opType:le.SELU}),Ofe={kernelName:Io,backendName:"webgpu",kernelFunc:Dfe},zfe=at({opType:le.SIGMOID}),Lfe={kernelName:To,backendName:"webgpu",kernelFunc:zfe},Wfe=at({opType:le.SIGN}),Bfe={kernelName:Co,backendName:"webgpu",kernelFunc:Wfe},Vfe=at({opType:le.SIN}),Ufe={kernelName:"Sin",backendName:"webgpu",kernelFunc:Vfe},Gfe=at({opType:le.SINH}),Hfe={kernelName:So,backendName:"webgpu",kernelFunc:Gfe},jfe=at({opType:le.SOFTPLUS}),qfe={kernelName:No,backendName:"webgpu",kernelFunc:jfe},Xfe=class{constructor(e,t,a,n,r,s){this.variableNames=["x"],this.outputShape=[],this.uniforms="",this.workgroupSize=[64,1,1],this.size=!0;let i=new Array(n.length);for(let o=0;o{this.uniforms+=` pad${l} : vec2,`}),this.shaderKey=`spaceToBatchND_${r}`}getUserCode(){let e=Pt(this.outputShape.length),t=pk(this.newDim);return` + ${Vc(this.paddedXShape,"PaddedX")} ${ue("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${e}(${t}), uniforms.reshapedPaddedXShape); let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex); - ${Yk(this.xShape,!0)} + ${Ek(this.xShape,!0)} } } - `}},Kfe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=[[0,0]];l.push(...i);for(let x=1+s.length;xx[0]+r.shape[A]+x[1]),p=C.getReshaped(u,s,o,!1),c=C.getPermuted(p.length,s.length,!1),d=C.getReshapedPermuted(u,s,o,!1),h=v.computeStrides(u),m=new Xfe(r.shape,u,l,p,c,h.length),f=[{type:"int32",data:p},{type:"int32",data:h}];l.map(x=>f.push({type:"int32",data:[x[0],x[1]]}));let g=a.runWebGPUProgram(m,[r],r.dtype,f),y=ke({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeData(g.dataId),y},Yfe={kernelName:Fu,backendName:"webgpu",kernelFunc:Kfe},Zfe=class{constructor(e,t,a){this.variableNames=["input","indices","segmentIds"],this.outputShape=[],this.uniforms="segmentSize : i32, sparseSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e,this.type=a,this.dispatchLayout=me([t]),this.dispatch=de(this.dispatchLayout,[t],this.workgroupSize),this.shaderKey="sparseSegmentSum"}getUserCode(){return` + `}},Kfe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((x,A)=>x*A),l=[[0,0]];l.push(...i);for(let x=1+s.length;xx[0]+r.shape[A]+x[1]),p=C.getReshaped(u,s,o,!1),c=C.getPermuted(p.length,s.length,!1),d=C.getReshapedPermuted(u,s,o,!1),h=v.computeStrides(u),m=new Xfe(r.shape,u,l,p,c,h.length),f=[{type:"int32",data:p},{type:"int32",data:h}];l.map(x=>f.push({type:"int32",data:[x[0],x[1]]}));let g=a.runWebGPUProgram(m,[r],r.dtype,f),y=ke({inputs:{x:g},backend:a,attrs:{shape:d}});return a.disposeData(g.dataId),y},Yfe={kernelName:gu,backendName:"webgpu",kernelFunc:Kfe},Zfe=class{constructor(e,t,a){this.variableNames=["input","indices","segmentIds"],this.outputShape=[],this.uniforms="segmentSize : i32, sparseSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e,this.type=a,this.dispatchLayout=me([t]),this.dispatch=de(this.dispatchLayout,[t],this.workgroupSize),this.shaderKey="sparseSegmentSum"}getUserCode(){return` ${ue("index")} { if (index < uniforms.sparseSize) { let indexInSegmentIds = index / uniforms.segmentSize; @@ -8229,14 +8229,14 @@ return a / b;`,NJ=` let value = input[indexInInput * uniforms.segmentSize + indexInSegment]; let outIndex = segmentId * uniforms.segmentSize + indexInSegment; - ${xs("&result[outIndex]","value",this.type)} + ${ys("&result[outIndex]","value",this.type)} } } `}},Jfe=class{constructor(e,t){this.variableNames=["segmentIds"],this.outputShape=[],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=[e],this.dispatchLayout=me(t),this.dispatch=de(this.dispatchLayout,t,this.workgroupSize),this.shaderKey="sparseSegmentIdCountProgram"}getUserCode(){return` ${ue("index")} { if (index < uniforms.segmentIdsShape) { let segmentId = segmentIds[index]; - ${xs("&result[segmentId]","1","int32")} + ${ys("&result[segmentId]","1","int32")} } } `}},Qfe=class{constructor(e,t){this.variableNames=["segmentSum","sameSegmentIdCount"],this.outputShape=[],this.uniforms="segmentSize : i32",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.type=t,this.dispatchLayout=me(e),this.dispatch=de(this.dispatchLayout,e,this.workgroupSize),this.shaderKey="sparseSegmentMean"}getUserCode(){return` @@ -8249,21 +8249,21 @@ return a / b;`,NJ=` } } } - `}};function Zk(e,t,a,n=!1,r){let s=v.sizeFromShape(e.shape)/e.shape[0],i=e.dtype,o=v.sizeFromShape(t.shape),l=r.readSync(a.dataId),u=o>0?l[o-1]+1:0,p,c=e.shape.slice();c[0]=u;let d=o*s,h=Wa({backend:r,attrs:{shape:c,value:0,dtype:i}});p=new Zfe(c,d,i);let m=[{type:"int32",data:[s]},{type:"int32",data:[d]}],f=r.runWebGPUProgram(p,[e,t,a],i,m,h);if(n)return f;let g=Wa({backend:r,attrs:{shape:[u],value:0,dtype:"int32"}});p=new Jfe(u,a.shape);let y=r.runWebGPUProgram(p,[a],"int32",null,g),x=Wa({backend:r,attrs:{shape:c,value:0,dtype:i}});p=new Qfe(c,i),m=[{type:"int32",data:[s]}];let A=r.runWebGPUProgram(p,[f,y],i,m,x);return r.disposeData(f.dataId),r.disposeData(y.dataId),A}function e2e(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;return Zk(n,r,s,!1,a)}var t2e={kernelName:zu,backendName:"webgpu",kernelFunc:e2e};function a2e(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;return Zk(n,r,s,!0,a)}var n2e={kernelName:Lu,backendName:"webgpu",kernelFunc:a2e},r2e=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n0?l[o-1]+1:0,p,c=e.shape.slice();c[0]=u;let d=o*s,h=Wa({backend:r,attrs:{shape:c,value:0,dtype:i}});p=new Zfe(c,d,i);let m=[{type:"int32",data:[s]},{type:"int32",data:[d]}],f=r.runWebGPUProgram(p,[e,t,a],i,m,h);if(n)return f;let g=Wa({backend:r,attrs:{shape:[u],value:0,dtype:"int32"}});p=new Jfe(u,a.shape);let y=r.runWebGPUProgram(p,[a],"int32",null,g),x=Wa({backend:r,attrs:{shape:c,value:0,dtype:i}});p=new Qfe(c,i),m=[{type:"int32",data:[s]}];let A=r.runWebGPUProgram(p,[f,y],i,m,x);return r.disposeData(f.dataId),r.disposeData(y.dataId),A}function e2e(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;return Mk(n,r,s,!1,a)}var t2e={kernelName:Au,backendName:"webgpu",kernelFunc:e2e};function a2e(e){let{inputs:t,backend:a}=e,{data:n,indices:r,segmentIds:s}=t;return Mk(n,r,s,!0,a)}var n2e={kernelName:bu,backendName:"webgpu",kernelFunc:a2e},r2e=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let r=0;r=5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=_e(r.shape,r.dtype,l),p=Xde(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new r2e(r.shape,s);return a.runWebGPUProgram(i,[r],r.dtype)}var i2e={kernelName:ds,backendName:"webgpu",kernelFunc:J3};function o2e(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=C.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let N=a.bufferSync(r),M=a.bufferSync(s),$=v.decodeString(a.readSync(i.dataId)[0]),E=Vde(N,M,o,d,p,u,l,c,$,h);return 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l2e={kernelName:jo,backendName:"webgpu",kernelFunc:o2e};function u2e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=C.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let m=ad({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return p[o]+=d,m})}var d2e={kernelName:Du,backendName:"webgpu",kernelFunc:u2e},p2e=at({opType:le.SQRT}),c2e={kernelName:Uo,backendName:"webgpu",kernelFunc:p2e},h2e={kernelName:Cp,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:a}=e,n=t,r=new td(a.shape,le.SQUARE);return n.runWebGPUProgram(r,[a],a.dtype)}},m2e=aa({opType:Pe.SQUARED_DIFFERENCE}),f2e={kernelName:qo,backendName:"webgpu",kernelFunc:m2e};function g2e({inputs:e,attrs:t,backend:a}){let{x:n}=e,r=new td(n.shape,le.STEP,"stepAlpha : f32,"),s=[{type:"float32",data:[t.alpha]}];return a.runWebGPUProgram(r,[n],n.dtype,s)}var y2e={kernelName:ps,backendName:"webgpu",kernelFunc:g2e},x2e=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=Pt(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let a=0;t=this.outputShape.map((n,r)=>(a++,this.outputShape.length===1?`coords * uniforms.strides[${r}] + uniforms.begin[${r}]`:`coords[${a-1}] * uniforms.strides[${r}] + uniforms.begin[${r}]`)).join(",")}return` + `}};function s2e(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let r=0;r=5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=_e(r.shape,r.dtype,l),p=Xde(u,s);return a.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new r2e(r.shape,s);return a.runWebGPUProgram(i,[r],r.dtype)}var i2e={kernelName:us,backendName:"webgpu",kernelFunc:$3};function o2e(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:p,strides:c,outputSize:d}=C.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let N=a.bufferSync(r),M=a.bufferSync(s),$=v.decodeString(a.readSync(i.dataId)[0]),E=Vde(N,M,o,d,p,u,l,c,$,h);return a.makeTensorInfo(o,E.dtype,E.values)}let m=[d/p,p],f=ke({inputs:{x:r},backend:a,attrs:{shape:[u,l]}}),g=s.shape.length?ke({inputs:{x:s},backend:a,attrs:{shape:[u,p]}}):tn({inputs:{x:s},backend:a}),y=g.dtype,x=a.makeTensorInfo([],y,v.makeZerosTypedArray(1,y)),A=ke({inputs:{x:i},backend:a,attrs:{shape:Array(m.length).fill(1)}}),b=$3({inputs:{x:A},backend:a,attrs:{reps:m}}),w=v.sizeFromShape([u,p]),I=[{type:"int32",data:[l]},{type:"int32",data:c},{type:"int32",data:[w]}];switch(u){case 0:break;case 1:{let N=new Ad([u,p],l,f.shape.length,g.shape.length,c,m,y,h);a.runWebGPUProgram(N,[g,f],y,I,b)}break;default:{let N=new Ad([u,p],l,f.shape.length,x.shape.length,c,m,y,h);a.runWebGPUProgram(N,[x,f],y,I,b)}{let N=new Ad([u,p],l,f.shape.length,g.shape.length,c,m,y);a.runWebGPUProgram(N,[g,f],y,I,b)}}let T=ke({inputs:{x:b},backend:a,attrs:{shape:o}});return a.disposeData(f.dataId),a.disposeData(g.dataId),a.disposeData(A.dataId),a.disposeData(x.dataId),a.disposeData(b.dataId),T}var l2e={kernelName:Mo,backendName:"webgpu",kernelFunc:o2e};function u2e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=C.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),c=r.shape.slice();return l.map(d=>{let h=[...c];h[o]=d;let m=Ou({inputs:{x:r},backend:a,attrs:{begin:p,size:h}});return p[o]+=d,m})}var d2e={kernelName:yu,backendName:"webgpu",kernelFunc:u2e},p2e=at({opType:le.SQRT}),c2e={kernelName:Ro,backendName:"webgpu",kernelFunc:p2e},h2e={kernelName:ip,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:a}=e,n=t,r=new Du(a.shape,le.SQUARE);return n.runWebGPUProgram(r,[a],a.dtype)}},m2e=aa({opType:Pe.SQUARED_DIFFERENCE}),f2e={kernelName:$o,backendName:"webgpu",kernelFunc:m2e};function g2e({inputs:e,attrs:t,backend:a}){let{x:n}=e,r=new Du(n.shape,le.STEP,"stepAlpha : f32,"),s=[{type:"float32",data:[t.alpha]}];return a.runWebGPUProgram(r,[n],n.dtype,s)}var y2e={kernelName:ds,backendName:"webgpu",kernelFunc:g2e},x2e=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=Pt(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let a=0;t=this.outputShape.map((n,r)=>(a++,this.outputShape.length===1?`coords * uniforms.strides[${r}] + uniforms.begin[${r}]`:`coords[${a-1}] * uniforms.strides[${r}] + uniforms.begin[${r}]`)).join(",")}return` ${ue("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, getX(${t})); } } - `}};function A2e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Nt.sliceInfo(r.shape,s,i,o,l,u,p,c,d),w;if(f)w=ke({inputs:{x:r},backend:a,attrs:{shape:m}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let I=Nt.computeOutShape(x,A,b),T=ad({inputs:{x:r},backend:a,attrs:{begin:x,size:I}});w=ke({inputs:{x:T},backend:a,attrs:{shape:m}}),a.disposeData(T.dataId)}else if(a.shouldExecuteOnCPU([r])){let I=a.readSync(r.dataId),T=_e(r.shape,r.dtype,I),N=Hde(h,T,b,x);w=a.makeTensorInfo(m,r.dtype,N.values)}else{let I=new x2e(h),T=[{type:"int32",data:x},{type:"int32",data:b}],N=a.runWebGPUProgram(I,[r],r.dtype,T);w=ke({inputs:{x:N},backend:a,attrs:{shape:m}}),a.disposeData(N.dataId)}return w}var b2e={kernelName:Xo,backendName:"webgpu",kernelFunc:A2e};function v2e(e){let{inputs:t,backend:a,attrs:n}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:p,dataSplits:c}=t,d=a.readSync(p.dataId),h=a.readSync(c.dataId),[m,f]=jde(d,h,r,s,i,o,l,u);return[a.makeTensorInfo([m.length],"string",m),a.makeTensorInfo(c.shape,"int32",f)]}var w2e={kernelName:Wu,backendName:"webgpu",kernelFunc:v2e},k2e=aa({opType:Pe.SUB,cpuKernelImpl:qde,supportsComplex:!0}),I2e={kernelName:Ko,backendName:"webgpu",kernelFunc:k2e},S2e=at({opType:le.TAN}),C2e={kernelName:Yo,backendName:"webgpu",kernelFunc:S2e},T2e=at({opType:le.TANH}),N2e={kernelName:Zo,backendName:"webgpu",kernelFunc:T2e};function R2e(e){let{inputs:t,backend:a,attrs:n}=e,{tensor:r,indices:s,updates:i}=t,{}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:c}=C.calculateShapes(i,s,r.shape),d=[c/u,u];if(c===0)return a.makeTensorInfo(r.shape,s.dtype);let h=[],m=ke({inputs:{x:s},backend:a,attrs:{shape:[l,o]}});h.push(m);let f=ke({inputs:{x:i},backend:a,attrs:{shape:[l,u]}});h.push(f);let g=ke({inputs:{x:r},backend:a,attrs:{shape:d}});h.push(g);let y=J3({inputs:{x:g},backend:a,attrs:{reps:Array(d.length).fill(1)}}),x=new zd([l,u],o,m.shape.length,f.shape.length,p,d,r.dtype,!1),A=v.sizeFromShape([l,u]),b=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[A]}],w=a.runWebGPUProgram(x,[f,m],g.dtype,b,y);h.push(w);let I=ke({inputs:{x:w},backend:a,attrs:{shape:r.shape}});return h.forEach(T=>a.disposeData(T.dataId)),I}var E2e={kernelName:Fo,backendName:"webgpu",kernelFunc:R2e},M2e=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, + `}};function A2e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:c,shrinkAxisMask:d}=n,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Nt.sliceInfo(r.shape,s,i,o,l,u,p,c,d),w;if(f)w=ke({inputs:{x:r},backend:a,attrs:{shape:m}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let I=Nt.computeOutShape(x,A,b),T=Ou({inputs:{x:r},backend:a,attrs:{begin:x,size:I}});w=ke({inputs:{x:T},backend:a,attrs:{shape:m}}),a.disposeData(T.dataId)}else if(a.shouldExecuteOnCPU([r])){let I=a.readSync(r.dataId),T=_e(r.shape,r.dtype,I),N=Hde(h,T,b,x);w=a.makeTensorInfo(m,r.dtype,N.values)}else{let I=new x2e(h),T=[{type:"int32",data:x},{type:"int32",data:b}],N=a.runWebGPUProgram(I,[r],r.dtype,T);w=ke({inputs:{x:N},backend:a,attrs:{shape:m}}),a.disposeData(N.dataId)}return w}var b2e={kernelName:Po,backendName:"webgpu",kernelFunc:A2e};function 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f=ke({inputs:{x:i},backend:a,attrs:{shape:[l,u]}});h.push(f);let g=ke({inputs:{x:r},backend:a,attrs:{shape:d}});h.push(g);let y=$3({inputs:{x:g},backend:a,attrs:{reps:Array(d.length).fill(1)}}),x=new Ad([l,u],o,m.shape.length,f.shape.length,p,d,r.dtype,!1),A=v.sizeFromShape([l,u]),b=[{type:"int32",data:[o]},{type:"int32",data:p},{type:"int32",data:[A]}],w=a.runWebGPUProgram(x,[f,m],g.dtype,b,y);h.push(w);let I=ke({inputs:{x:w},backend:a,attrs:{shape:r.shape}});return h.forEach(T=>a.disposeData(T.dataId)),I}var E2e={kernelName:wo,backendName:"webgpu",kernelFunc:R2e},M2e=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return` ${ue("index")} { if (index < uniforms.size) { @@ -8390,7 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M=c/2,$=M*2;for(let E=M;E>=1;E/=2)f($,E,h.shape)}let g=h;h=Ou({inputs:{x:h},backend:a,attrs:{begin:0,size:[u,s]}}),bl(a,g);let y=Ck({inputs:{x:p,indices:h},backend:a,attrs:{axis:1,batchDims:1}});bl(a,p);let x=o.slice(0,-1);x.push(s),g=h,h=ke({inputs:{x:h},attrs:{shape:x},backend:a}),bl(a,g);let A=y;return y=ke({inputs:{x:y},attrs:{shape:x},backend:a}),bl(a,A),[y,h]}var _2e={kernelName:Fo,backendName:"webgpu",kernelFunc:P2e},F2e=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=me(this.outputShape),this.dispatch=de(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="transform"}getUserCode(){return` fn mapCoord(outCoord : f32, len : f32) -> f32{ var inCoord = outCoord; if(uniforms.fillModeId == 2) { @@ -8506,7 +8506,7 @@ return a / b;`,NJ=` setOutputAtIndex(index, outputValue); } } - `}};function 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L2e={kernelName:wu,backendName:"webgpu",kernelFunc:z2e},W2e=class{constructor(e,t,a){if(this.outputShape=[],this.variableNames=["x","segmentIds"],this.uniforms="numSegments : i32, xSize: i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t,this.dispatchLayout=me(e),this.dispatch=de(this.dispatchLayout,e,this.workgroupSize),a!=="float32"&&a!=="int32")throw new Error(`UnsortedSegmentSum only supports float32 and int32 types, does not support ${a} type.`);this.type=a,this.shaderKey="unsortedSegmentSum"}getUserCode(){return` ${ue("index")} { if (index < uniforms.xSize) { @@ -8519,11 +8519,11 @@ return a / b;`,NJ=` let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments; let value = getX(b, inCol); - ${xs("&result[flatIndex]","value",this.type)} + ${ys("&result[flatIndex]","value",this.type)} } } } - `}};function 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Qk=` + `}};function B2e(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,segmentIds:s}=t,{numSegments:i}=n,o=r.shape.length,l=[],u=0,p=C.getAxesPermutation([u],o),c=r;p!=null&&(c=sr({inputs:{x:r},backend:a,attrs:{perm:p}}),l.push(c),u=C.getInnerMostAxes(1,o)[0]);let d=C.segment_util.computeOutShape(c.shape,u,i),h=v.sizeFromShape([c.shape[u]]),m=ke({inputs:{x:c},backend:a,attrs:{shape:[-1,h]}});l.push(m);let f=r.dtype,g=[m.shape[0],i],y=Wa({backend:a,attrs:{shape:g,value:0,dtype:f}}),x=new W2e(m.shape,g,f),A=[{type:"int32",data:[i]},{type:"int32",data:[v.sizeFromShape(m.shape)]}],b=a.runWebGPUProgram(x,[m,s],f,A,y),w=ke({inputs:{x:b},backend:a,attrs:{shape:d}});l.push(b);let I=w;if(p!=null){l.push(w);let T=C.getUndoAxesPermutation(p);I=sr({inputs:{x:I},backend:a,attrs:{perm:T}})}return l.forEach(T=>a.disposeData(T.dataId)),I}var 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t.debug&&a==="config"&&n.length>0&&K("invalid configuration",n),n}function Et(...e){let t=a=>a&&typeof a=="object";return e.reduce((a,n)=>(Object.keys(n||{}).forEach(r=>{let s=a[r],i=n[r];Array.isArray(s)&&Array.isArray(i)?a[r]=s.concat(...i):t(s)&&t(i)?a[r]=Et(s,i):a[r]=i}),a),{})}var Xo={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,minSize:0,iouThreshold:.1,scale:1.4,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,scale:2.3,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var Pk=` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -8533,7 +8533,7 @@ return a / b;`,NJ=` vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`;var e9=` +`;var _k=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -8545,7 +8545,7 @@ return a / b;`,NJ=` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`,t9=` +`,Fk=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -8557,7 +8557,7 @@ return a / b;`,NJ=` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`,a9=` +`,Dk=` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -8570,7 +8570,7 @@ return a / b;`,NJ=` vec2 coord = pixelate(vUv, size); gl_FragColor += texture2D(texture, coord); } -`,n9=` +`,Ok=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -8593,7 +8593,7 @@ return a / 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Qh=class{constructor(){he(this,"config");he(this,"element");he(this,"stream");he(this,"devices",[]);he(this,"enumerate",async()=>{try{let t=await navigator.mediaDevices.enumerateDevices();this.devices=t.filter(a=>a.kind==="videoinput")}catch(t){this.devices=[]}return this.devices});he(this,"start",async t=>{var r,s;if(t!=null&&t.debug&&(this.config.debug=t==null?void 0:t.debug),t!=null&&t.crop&&(this.config.crop=t==null?void 0:t.crop),t!=null&&t.mode&&(this.config.mode=t==null?void 0:t.mode),t!=null&&t.width&&(this.config.width=t==null?void 0:t.width),t!=null&&t.height&&(this.config.height=t==null?void 0:t.height),t!=null&&t.id&&(this.config.id=t==null?void 0:t.id),t!=null&&t.element)if(typeof t.element=="string"){let i=document.getElementById(t.element);if(i&&i instanceof HTMLVideoElement)this.element=i;else return this.config.debug&&K("webcam","cannot get dom element",t.element),`webcam error: cannot get dom element: ${t.element}`}else if(t.element instanceof 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a=rt.faceLabels.slice();if(a=ut(a,"[id]",e.id.toFixed(0)),e.score&&(a=ut(a,"[score]",100*e.score)),e.gender&&(a=ut(a,"[gender]",e.gender)),e.genderScore&&(a=ut(a,"[genderScore]",100*e.genderScore)),e.age&&(a=ut(a,"[age]",e.age)),e.distance&&(a=ut(a,"[distance]",100*e.distance)),e.real&&(a=ut(a,"[real]",100*e.real)),e.live&&(a=ut(a,"[live]",100*e.live)),e.emotion&&e.emotion.length>0){let d=e.emotion.map(h=>`${Math.trunc(100*h.score)}% ${h.emotion}`);d.length>3&&(d.length=3),a=ut(a,"[emotions]",d.join(" "))}(s=(r=e.rotation)==null?void 0:r.angle)!=null&&s.roll&&(a=ut(a,"[roll]",Ko(e.rotation.angle.roll))),(o=(i=e.rotation)==null?void 0:i.angle)!=null&&o.yaw&&(a=ut(a,"[yaw]",Ko(e.rotation.angle.yaw))),(u=(l=e.rotation)==null?void 0:l.angle)!=null&&u.pitch&&(a=ut(a,"[pitch]",Ko(e.rotation.angle.pitch))),(c=(p=e.rotation)==null?void 0:p.gaze)!=null&&c.bearing&&(a=ut(a,"[gaze]",Ko(e.rotation.gaze.bearing))),wn(t,a,e.box[0],e.box[1],rt)}function Pge(e,t){var a,n,r,s;if((a=e.annotations)!=null&&a.leftEyeIris&&((n=e.annotations)!=null&&n.leftEyeIris[0])){t.strokeStyle=rt.useDepth?"rgba(255, 200, 255, 0.3)":rt.color,t.beginPath();let i=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,o=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],i,o,0,0,2*Math.PI),t.stroke(),rt.fillPolygons&&(t.fillStyle=rt.useDepth?"rgba(255, 255, 200, 0.3)":rt.color,t.fill())}if((r=e.annotations)!=null&&r.rightEyeIris&&((s=e.annotations)!=null&&s.rightEyeIris[0])){t.strokeStyle=rt.useDepth?"rgba(255, 200, 255, 0.3)":rt.color,t.beginPath();let i=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,o=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],i,o,0,0,2*Math.PI),t.stroke(),rt.fillPolygons&&(t.fillStyle=rt.useDepth?"rgba(255, 255, 200, 0.3)":rt.color,t.fill())}}function _ge(e,t){var a;if(rt.drawGaze&&((a=e.rotation)!=null&&a.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*Ko(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Ko(e.rotation.angle.pitch)/90,s=new Path2D(` M ${e.box[0]+e.box[2]/2} ${e.box[1]} C ${n} ${e.box[1]}, @@ -8628,7 +8628,7 @@ return a / b;`,NJ=` ${e.box[0]} ${r}, ${e.box[0]+e.box[2]} ${r}, ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} - `);t.stroke(i),t.stroke(s)}}function Fge(e,t){var a;if(rt.drawGaze&&((a=e.rotation)!=null&&a.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let n=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];oy(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[n[0],n[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];oy(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function Dge(e,t){if(rt.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let a=0;ae.mesh[r]);iy(t,n,rt)}Pge(e,t)}}function Oge(e,t){if(rt.drawPoints)if((e==null?void 0:e.mesh.length)>=468)for(let a=0;a0&&(Oge(r,n),Dge(r,n),_ge(r,n),Fge(r,n))}}function w0(e,t,a){var s,i;let n=Et(Ft,a);if(!t||!e)return;let r=vn(e);if(r){r.lineJoin="round";for(let o=0;o0)){let l=n.bodyLabels.slice();l=ut(l,"[id]",t[o].id.toFixed(0)),l=ut(l,"[score]",100*t[o].score),wn(r,l,t[o].box[0],t[o].box[1],n)}if(n.drawPoints&&t[o].keypoints)for(let l=0;l0&&t[o].keypoints){r.font=n.font;for(let l of t[o].keypoints){if(!l.score||l.score===0)continue;let u=n.bodyPartLabels.slice();u=ut(u,"[label]",l.part),u=ut(u,"[score]",100*l.score),wn(r,u,l.position[0],l.position[1],n)}}if(n.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let l of Object.values(t[o].annotations))for(let u of l)h9(r,u,n)}}}function k0(e,t,a){var s,i;let n=Et(Ft,a);if(!t||!e)return;let r=vn(e);if(r){r.lineJoin="round",r.font=n.font;for(let o of t){if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,dr(r,o.box[0],o.box[1],o.box[2],o.box[3],n),n.drawLabels&&((s=n.handLabels)==null?void 0:s.length)>0){let l=n.handLabels.slice();l=ut(l,"[id]",o.id.toFixed(0)),l=ut(l,"[label]",o.label),l=ut(l,"[score]",100*o.score),wn(r,l,o.box[0],o.box[1],n)}r.stroke()}if(n.drawPoints&&o.keypoints&&o.keypoints.length>0)for(let l of o.keypoints)r.fillStyle=hl(l[2],n),ur(r,l[0],l[1],0,n);if(n.drawLabels&&o.annotations&&((i=n.fingerLabels)==null?void 0:i.length)>0)for(let[l,u]of Object.entries(o.annotations)){let p=n.fingerLabels.slice();p=ut(p,"[label]",l),wn(r,p,u[u.length-1][0],u[u.length-1][1],n)}if(n.drawPolygons&&o.annotations){let l=u=>{if(!(!u||u.length===0||!u[0]))for(let p=0;p0?p-1:0][0],u[p>0?p-1:0][1]),r.lineTo(u[p][0],u[p][1]),r.stroke()}};r.lineWidth=n.lineWidth,l(o.annotations.index),l(o.annotations.middle),l(o.annotations.ring),l(o.annotations.pinky),l(o.annotations.thumb)}}}}function I0(e,t,a){var s;let n=Et(Ft,a);if(!t||!e)return;let r=vn(e);if(r){r.lineJoin="round",r.font=n.font;for(let i of t)if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,dr(r,i.box[0],i.box[1],i.box[2],i.box[3],n),n.drawLabels&&((s=n.objectLabels)==null?void 0:s.length)>0){let o=n.objectLabels.slice();o=ut(o,"[id]",i.id.toFixed(0)),o=ut(o,"[label]",i.label),o=ut(o,"[score]",100*i.score),wn(r,o,i.box[0],i.box[1],n)}r.stroke()}}}function S0(e,t,a){var r;let n=Et(Ft,a);if(!(!t||!e)&&n.drawGestures&&((r=n.gestureLabels)==null?void 0:r.length)>0){let s=vn(e);if(!s)return;s.font=n.font,s.fillStyle=n.color;let i=1;for(let o=0;o1&&u[1].length>0){let p=l[1]>0?`#${l[1]}`:"",c=n.gestureLabels.slice();c=ut(c,"[where]",l[0]),c=ut(c,"[who]",p),c=ut(c,"[what]",u[1]),wn(s,c,8,2+i*n.lineHeight,n),i+=1}}}}var vs={face:`face + `);t.stroke(i),t.stroke(s)}}function Fge(e,t){var a;if(rt.drawGaze&&((a=e.rotation)!=null&&a.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let n=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];B3(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[n[0],n[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];B3(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function Dge(e,t){if(rt.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let a=0;ae.mesh[r]);W3(t,n,rt)}Pge(e,t)}}function Oge(e,t){if(rt.drawPoints)if((e==null?void 0:e.mesh.length)>=468)for(let a=0;a0&&(Oge(r,n),Dge(r,n),_ge(r,n),Fge(r,n))}}function a0(e,t,a){var s,i;let n=Et(Ft,a);if(!t||!e)return;let r=vn(e);if(r){r.lineJoin="round";for(let o=0;o0)){let l=n.bodyLabels.slice();l=ut(l,"[id]",t[o].id.toFixed(0)),l=ut(l,"[score]",100*t[o].score),wn(r,l,t[o].box[0],t[o].box[1],n)}if(n.drawPoints&&t[o].keypoints)for(let l=0;l0&&t[o].keypoints){r.font=n.font;for(let l of t[o].keypoints){if(!l.score||l.score===0)continue;let u=n.bodyPartLabels.slice();u=ut(u,"[label]",l.part),u=ut(u,"[score]",100*l.score),wn(r,u,l.position[0],l.position[1],n)}}if(n.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let l of Object.values(t[o].annotations))for(let u of l)qk(r,u,n)}}}function n0(e,t,a){var s,i;let n=Et(Ft,a);if(!t||!e)return;let r=vn(e);if(r){r.lineJoin="round",r.font=n.font;for(let o of t){if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,dr(r,o.box[0],o.box[1],o.box[2],o.box[3],n),n.drawLabels&&((s=n.handLabels)==null?void 0:s.length)>0){let l=n.handLabels.slice();l=ut(l,"[id]",o.id.toFixed(0)),l=ut(l,"[label]",o.label),l=ut(l,"[score]",100*o.score),wn(r,l,o.box[0],o.box[1],n)}r.stroke()}if(n.drawPoints&&o.keypoints&&o.keypoints.length>0)for(let l of o.keypoints)r.fillStyle=Yo(l[2],n),ur(r,l[0],l[1],0,n);if(n.drawLabels&&o.annotations&&((i=n.fingerLabels)==null?void 0:i.length)>0)for(let[l,u]of Object.entries(o.annotations)){let p=n.fingerLabels.slice();p=ut(p,"[label]",l),wn(r,p,u[u.length-1][0],u[u.length-1][1],n)}if(n.drawPolygons&&o.annotations){let l=u=>{if(!(!u||u.length===0||!u[0]))for(let p=0;p0?p-1:0][0],u[p>0?p-1:0][1]),r.lineTo(u[p][0],u[p][1]),r.stroke()}};r.lineWidth=n.lineWidth,l(o.annotations.index),l(o.annotations.middle),l(o.annotations.ring),l(o.annotations.pinky),l(o.annotations.thumb)}}}}function r0(e,t,a){var s;let n=Et(Ft,a);if(!t||!e)return;let r=vn(e);if(r){r.lineJoin="round",r.font=n.font;for(let i of t)if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,dr(r,i.box[0],i.box[1],i.box[2],i.box[3],n),n.drawLabels&&((s=n.objectLabels)==null?void 0:s.length)>0){let o=n.objectLabels.slice();o=ut(o,"[id]",i.id.toFixed(0)),o=ut(o,"[label]",i.label),o=ut(o,"[score]",100*i.score),wn(r,o,i.box[0],i.box[1],n)}r.stroke()}}}function s0(e,t,a){var r;let n=Et(Ft,a);if(!(!t||!e)&&n.drawGestures&&((r=n.gestureLabels)==null?void 0:r.length)>0){let s=vn(e);if(!s)return;s.font=n.font,s.fillStyle=n.color;let i=1;for(let o=0;o1&&u[1].length>0){let p=l[1]>0?`#${l[1]}`:"",c=n.gestureLabels.slice();c=ut(c,"[where]",l[0]),c=ut(c,"[who]",p),c=ut(c,"[what]",u[1]),wn(s,c,8,2+i*n.lineHeight,n),i+=1}}}}var bs={face:`face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -8637,7 +8637,7 @@ return a / b;`,NJ=` live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var py=0;function Lge(e,t,a){let n=Et(Ft,a);if(!t||!e)return;let r=vn(e);if(r){r.lineJoin="round",r.font=n.font;for(let s=0;smy,kpt:()=>hy});var hy=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],my={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var kn,xl=224,g9,Uge=5,N0=[8,16,32,32,32];function Gge(){let e=[],t=0;for(;ta.x)),y:Bt(e.map(a=>a.y))}}async function y9(e){if(ne.initial&&(kn=null),!kn&&e.body.detector&&e.body.detector.modelPath){kn=await $e(e.body.detector.modelPath);let t=kn!=null&&kn.executor?Object.values(kn.modelSignature.inputs):void 0;xl=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&kn&&K("cached model:",kn.modelUrl);return Gge(),kn}var f9=[5,5];function Hge(e,t){return De(()=>{let a=Sa(e,12,1),n=Oe(a[0]),r=Oe(a[1]),s=Oe(a[2]),i=Oe(a[3]);n=we(ve(n,xl),t.x),r=we(ve(r,xl),t.y),s=te(ve(s,xl),f9[0]),i=te(ve(i,xl),f9[1]);let o=xe(n,ve(s,2)),l=xe(r,ve(i,2)),u=we(o,s),p=we(l,i);return ha([o,l,u,p],1)})}async function jge(e,t,a,n){var u,p;let r=[],s={};s.boxes=Hge(e,g9),s.scores=za(t),s.nms=await fe.nonMaxSuppressionAsync(s.boxes,s.scores,1,((u=a.body.detector)==null?void 0:u.minConfidence)||.1,((p=a.body.detector)==null?void 0:p.iouThreshold)||.1);let i=await s.nms.data(),o=await s.scores.data(),l=await s.boxes.array();for(let c of Array.from(i)){let d=o[c],h=l[c],m=[Math.round(h[0]*n[0]),Math.round(h[1]*n[1]),Math.round(h[2]*n[0]),Math.round(h[3]*n[1])],f={score:d,boxRaw:h,box:m};r.push(f)}return Object.keys(s).forEach(c=>J(s[c])),r}async function x9(e,t,a){let n={};n.res=kn==null?void 0:kn.execute(e,["Identity"]),n.logitsRaw=Fe(n.res,[0,0,0],[1,-1,1]),n.boxesRaw=Fe(n.res,[0,0,1],[1,-1,-1]),n.logits=Oe(n.logitsRaw),n.boxes=Oe(n.boxesRaw);let r=await jge(n.boxes,n.logits,t,a);return Object.keys(n).forEach(s=>J(n[s])),r}function ws(e,t=[1,1]){let a=[e.map(o=>o[0]),e.map(o=>o[1])],n=[Math.min(...a[0]),Math.min(...a[1])],r=[Math.max(...a[0]),Math.max(...a[1])],s=[n[0],n[1],r[0]-n[0],r[1]-n[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function A9(e,t=[1,1]){let a=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...a[0]),Math.min(...a[1])],r=[Math.max(...a[0]),Math.max(...a[1])],s=[(n[0]+r[0])/2,(n[1]+r[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+r[0],-s[1]+r[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function R0(e,t){let a=[e[2]*t,e[3]*t];return[e[0]-(a[0]-e[2])/2,e[1]-(a[1]-e[3])/2,a[0],a[1]]}var Ua,gy=256,fy=Number.MAX_SAFE_INTEGER,qge={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},M0=[],ks=[[0,0],[0,0],[0,0],[0,0]],b9=0,v9=e=>1-1/(1+Math.exp(e)),k9=e=>y9(e);async function I9(e){if(ne.initial&&(Ua=null),Ua)e.debug&&K("cached model:",Ua.modelUrl);else{Ua=await $e(e.body.modelPath);let t=Ua!=null&&Ua.executor?Object.values(Ua.modelSignature.inputs):void 0;gy=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Ua}function w9(e,t,a){var s,i;let n={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((i=e==null?void 0:e.shape)!=null&&i[2]))return e;let r;if(a&&(n.cropped=fe.cropAndResize(e,[a],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let o=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],l=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];ks=[[0,0],o,l,[0,0]],n.pad=Rn(n.cropped||e,ks),n.resize=fe.resizeBilinear(n.pad,[t,t]),r=ve(n.resize,ze.tf255)}else e.shape[1]!==t?(n.resize=fe.resizeBilinear(n.cropped||e,[t,t]),r=ve(n.resize,ze.tf255)):r=ve(n.cropped||e,ze.tf255);return Object.keys(n).forEach(o=>J(n[o])),r}function Xge(e,t,a){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+ks[2][0]+ks[2][1])/t[0]-ks[2][0]),Math.trunc(n.position[1]*(t[1]+ks[1][0]+ks[1][1])/t[1]-ks[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],2*n.position[2]/(t[0]+t[1])];if(a){let n=a[2]-a[0],r=a[3]-a[1];for(let s of e)s.positionRaw=[s.positionRaw[0]/r+a[1],s.positionRaw[1]/n+a[0],s.positionRaw[2]],s.position=[Math.trunc(s.positionRaw[0]*t[0]),Math.trunc(s.positionRaw[1]*t[1]),s.positionRaw[2]]}return e}function Kge(e){let t=e.find(o=>o.part==="leftPalm"),a=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((a.position[2]||0)+(n.position[2]||0))/2;let r=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");r.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function Yge(e,t,a){if(!(Ua!=null&&Ua.executor))return null;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=Ua==null?void 0:Ua.execute(e,qge.landmarks);let r=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(m=>J(n[m]));let o=[],l=5;for(let m=0;mm.position),c=ws(p,[a[0],a[1]]),d={};for(let[m,f]of Object.entries(my)){let g=[];for(let y=0;yb.part===f[y]),A=u.find(b=>b.part===f[y+1]);x&&A&&g.push([x.position,A.position])}d[m]=g}return{id:0,score:Math.trunc(100*r)/100,box:c.box,boxRaw:c.boxRaw,keypoints:u,annotations:d}}async function yy(e,t){var s,i,o;let a=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>ae()-b9,r=fy<(t.body.skipFrames||0);if(t.skipAllowed&&n&&r&&M0!==null)fy++;else{let l=[];if((i=(s=t.body)==null?void 0:s.detector)!=null&&i.enabled){let u=w9(e,224);l=await x9(u,t,a),J(u)}else l=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let u=0;uJ(n[u])),r}async function by(e,t){if(!(Ga!=null&&Ga.executor))return[];let a=(t.object.skipTime||0)>ae()-C9,n=Ay<(t.object.skipFrames||0);return t.skipAllowed&&a&&n&&xy.length>0?(Ay++,xy):(Ay=0,new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],i=fe.resizeBilinear(e,[Al,Al]),o=t.object.enabled?Ga==null?void 0:Ga.execute(i,["tower_0/detections"]):null;C9=ae(),J(i);let l=await Zge(o,s,t);xy=l,r(l)}))}var $0={};Ar($0,{connected:()=>wy,kpt:()=>vy});var vy=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],wy={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Mt,R9=0,Ma={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},ky=Number.MAX_SAFE_INTEGER;async function E9(e){return ne.initial&&(Mt=null),Mt?e.debug&&K("cached model:",Mt.modelUrl):Mt=await $e(e.body.modelPath),Mt}async function Jge(e,t){let[a,n]=e.shape,r=Q(e,[n*a]),s=ga(r,0),i=(await s.data())[0];if(i>t){let o=ir(r,0),l=Gu(o,a),u=(await l.data())[0],p=ve(o,a),c=(await p.data())[0];return J([r,s,o,l,p]),[u,c,i]}return J([r,s]),[0,0,i]}async function Iy(e,t){if(!(Mt!=null&&Mt.executor)||!(Mt!=null&&Mt.inputs[0].shape))return[];let a=(t.body.skipTime||0)>ae()-R9,n=ky<(t.body.skipFrames||0);return t.skipAllowed&&a&&n&&Object.keys(Ma.keypoints).length>0?(ky++,[Ma]):(ky=0,new Promise(async r=>{let s=De(()=>{var m,f;let c=fe.resizeBilinear(e,[((m=Mt==null?void 0:Mt.inputs[0].shape)==null?void 0:m[2])||0,((f=Mt==null?void 0:Mt.inputs[0].shape)==null?void 0:f[1])||0],!1),d=te(c,ze.tf2);return xe(d,ze.tf1)}),i;if(t.body.enabled&&(i=Mt==null?void 0:Mt.execute(s)),R9=ae(),J(s),i){Ma.keypoints.length=0;let c=Oe(i);J(i);let d=Na(c,2);J(c);for(let h=0;h(t.body.minConfidence||0)&&Ma.keypoints.push({score:Math.round(100*g)/100,part:vy[h],positionRaw:[m/Mt.inputs[0].shape[2],f/Mt.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/Mt.inputs[0].shape[2]),Math.round(e.shape[1]*f/Mt.inputs[0].shape[1])]})}d.forEach(h=>J(h))}Ma.score=Ma.keypoints.reduce((c,d)=>d.score>c?d.score:c,0);let o=Ma.keypoints.map(c=>c.position[0]),l=Ma.keypoints.map(c=>c.position[1]);Ma.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=Ma.keypoints.map(c=>c.positionRaw[0]),p=Ma.keypoints.map(c=>c.positionRaw[1]);Ma.boxRaw=[Math.min(...u),Math.min(...p),Math.max(...u)-Math.min(...u),Math.max(...p)-Math.min(...p)];for(let[c,d]of Object.entries(wy)){let h=[];for(let m=0;my.part===d[m]),g=Ma.keypoints.find(y=>y.part===d[m+1]);f&&g&&f.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([f.position,g.position])}Ma.annotations[c]=h}r([Ma])}))}var sd=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],P0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],_0=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],F0=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],_9=(e,t,a)=>{let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],s=e.landmarks.map(i=>[(i[0]+a[0])*t[0],(i[1]+a[1])*t[1]]);return{startPoint:n,endPoint:r,landmarks:s,confidence:e.confidence}},Sy=(e,t,a)=>{let n=t.shape[1],r=t.shape[2],s=[e.startPoint[1]/n,e.startPoint[0]/r,e.endPoint[1]/n,e.endPoint[0]/r],i=fe.cropAndResize(t,[s],[0],a),o=ve(i,ze.tf255);return J(i),o},D0=(e,t)=>{let a=P0(e),n=sd(e),r=[t*n[0]/2,t*n[1]/2];return{startPoint:[a[0]-r[0],a[1]-r[1]],endPoint:[a[0]+r[0],a[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence,size:n}},O0=e=>{let t=P0(e),a=sd(e),n=Math.max(...a)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence,size:[Math.round(a[0]),Math.round(a[1])]}},F9=e=>{let t=e.map(n=>n[0]),a=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...a)],endPoint:[Math.max(...t),Math.max(...a)],landmarks:e}},Cy=[[1,0,0],[0,1,0],[0,0,1]],Qge=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),e3e=(e,t)=>Qge(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var $9=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],bl=(e,t)=>{let a=0;for(let n=0;n{let a=[];for(let n=0;n{let a=[],n=e.length;for(let r=0;r{let a=Math.cos(e),n=Math.sin(e),r=[[a,-n,0],[n,a,0],[0,0,1]],s=$9(t[0],t[1]),i=P9(s,r),o=$9(-t[0],-t[1]);return P9(i,o)},a3e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],a=[e[0][2],e[1][2]],n=[-bl(t[0],a),-bl(t[1],a)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},n3e=(e,t)=>[bl(e,t[0]),bl(e,t[1])];function O9(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},a=[];for(let n=0;n[s[0]/r*(h[0]-r/2),s[1]/r*(h[1]-r/2),h[2]||0]),o=a&&a!==0&&Math.abs(a)>.2,l=o?D9(a,[0,0]):Cy,u=o?i.map(h=>[...n3e(h,l),h[2]]):i,p=o?a3e(n):Cy,c=P0(t),d=[bl(c,p[0]),bl(c,p[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2]||0)])}function L9(e,t,a,n){let r=t.landmarks.length>=ly.count?ly.symmetryLine:ml.symmetryLine,s=0,i=Cy,o;if(e&&ne.kernels.includes("rotatewithoffset"))if(s=e3e(t.landmarks[r[0]],t.landmarks[r[1]]),s&&s!==0&&Math.abs(s)>.2){let u=P0(t),p=[u[0]/a.shape[2],u[1]/a.shape[1]],c=fe.rotateWithOffset(a,s,0,[p[0],p[1]]);i=D9(-s,u),o=Sy(t,c,[n,n]),J(c)}else o=Sy(t,a,[n,n]);else o=Sy(t,a,[n,n]);return[s,i,o]}var r3e=e=>{let t=e.map(n=>n[0]),a=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...a)+(Math.max(...a)-Math.min(...a))/2]},W9=(e,t)=>{let a=r3e(e),n=sd(t);return{startPoint:[a[0]-n[0]/2,a[1]-n[1]/2],endPoint:[a[0]+n[0]/2,a[1]+n[1]/2]}};var B9=6,Gn,z0=null,pr=0,id=null,V9=()=>pr;async function U9(e){var t;return ne.initial&&(Gn=null),Gn?e.debug&&K("cached model:",Gn.modelUrl):Gn=await $e((t=e.face.detector)==null?void 0:t.modelPath),pr=Gn.executor&&Gn.inputs[0].shape?Gn.inputs[0].shape[2]:256,id=Ge(pr,"int32"),z0=Qn(O9(pr)),Gn}function s3e(e){if(!z0||!id)return yn([0,0]);let t={};t.boxStarts=Fe(e,[0,1],[-1,2]),t.centers=we(t.boxStarts,z0),t.boxSizes=Fe(e,[0,3],[-1,2]),t.boxSizesNormalized=ve(t.boxSizes,id),t.centersNormalized=ve(t.centers,id),t.halfBoxSize=ve(t.boxSizesNormalized,ze.tf2),t.starts=xe(t.centersNormalized,t.halfBoxSize),t.ends=we(t.centersNormalized,t.halfBoxSize),t.startNormalized=te(t.starts,id),t.endNormalized=te(t.ends,id);let a=Uu([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>J(t[n])),a}async function G9(e,t){var u,p,c,d,h,m,f,g,y;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let a={},n=[0,0],r=[1,1];if((p=(u=t==null?void 0:t.face)==null?void 0:u.detector)!=null&&p.square){let x=Math.max(e.shape[2],e.shape[1]);n=[Math.floor((x-e.shape[2])/2),Math.floor((x-e.shape[1])/2)],a.padded=Rn(e,[[0,0],[n[1],n[1]],[n[0],n[0]],[0,0]]),r=[e.shape[2]/x,e.shape[1]/x],n=[n[0]/pr,n[1]/pr]}else a.padded=e.clone();a.resized=fe.resizeBilinear(a.padded,[pr,pr]),a.div=ve(a.resized,ze.tf127),a.normalized=xe(a.div,ze.tf1);let s=Gn==null?void 0:Gn.execute(a.normalized);if(Array.isArray(s)&&s.length>2){let x=s.sort((A,b)=>A.size-b.size);a.concat384=lt([x[0],x[2]],2),a.concat512=lt([x[1],x[3]],2),a.concat=lt([a.concat512,a.concat384],1),a.batch=Oe(a.concat,[0])}else Array.isArray(s)?a.batch=Oe(s[0]):a.batch=Oe(s);J(s),a.boxes=s3e(a.batch),a.logits=Fe(a.batch,[0,0],[-1,1]),a.sigmoid=za(a.logits),a.scores=Oe(a.sigmoid),a.nms=await fe.nonMaxSuppressionAsync(a.boxes,a.scores,((c=t.face.detector)==null?void 0:c.maxDetected)||0,((d=t.face.detector)==null?void 0:d.iouThreshold)||0,((h=t.face.detector)==null?void 0:h.minConfidence)||0);let i=await a.nms.array(),o=[],l=await a.scores.data();for(let x=0;x(((m=t.face.detector)==null?void 0:m.minConfidence)||0)){let b={};b.bbox=Fe(a.boxes,[i[x],0],[1,-1]),b.slice=Fe(a.batch,[i[x],B9-1],[1,-1]),b.squeeze=Oe(b.slice),b.landmarks=Q(b.squeeze,[B9,-1]);let w=await b.bbox.data(),I=[w[0]*r[0]-n[0],w[1]*r[1]-n[1],w[2]*r[0]-n[0],w[3]*r[1]-n[1]],T={startPoint:[I[0],I[1]],endPoint:[I[2],I[3]],landmarks:await b.landmarks.array(),confidence:A};b.anchor=Fe(z0,[i[x],0],[1,2]);let N=await b.anchor.data(),M=_9(T,[(e.shape[2]||0)/pr,(e.shape[1]||0)/pr],N),$=D0(M,((f=t.face.detector)==null?void 0:f.scale)||1.4),E=O0($);E.size[0]>(((g=t.face.detector)==null?void 0:g.minSize)||0)&&E.size[1]>(((y=t.face.detector)==null?void 0:y.minSize)||0)&&o.push(E),Object.keys(b).forEach(S=>J(b[S]))}}return Object.keys(a).forEach(x=>J(a[x])),o}var nn,Is=0,Ny=Pn.leftEyeLower0,Ry=Pn.rightEyeLower0,od={leftBounds:[Ny[0],Ny[Ny.length-1]],rightBounds:[Ry[0],Ry[Ry.length-1]]},ld={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function K9(e){var t,a;return ne.initial&&(nn=null),nn?e.debug&&K("cached model:",nn.modelUrl):nn=await $e((t=e.face.iris)==null?void 0:t.modelPath),Is=nn!=null&&nn.executor&&((a=nn.inputs)!=null&&a[0].shape)?nn.inputs[0].shape[2]:0,Is===-1&&(Is=64),nn}function L0(e,t,a,n){for(let r=0;r{let t=e[od.leftBounds[0]][2],a=e[od.rightBounds[0]][2];return t-a},j9=(e,t,a,n,r,s=!1,i=2.3)=>{let o=O0(D0(F9([e[a],e[n]]),i)),l=sd(o),u=fe.cropAndResize(t,[[o.startPoint[1]/r,o.startPoint[0]/r,o.endPoint[1]/r,o.endPoint[0]/r]],[0],[Is,Is]);if(s&&ne.kernels.includes("flipleftright")){let p=fe.flipLeftRight(u);J(u),u=p}return{box:o,boxSize:l,crop:u}},q9=(e,t,a,n=!1)=>{let r=[];for(let s=0;s{let n=e[Pn[`${a}EyeUpper0`][ld.upperCenter]][2],r=e[Pn[`${a}EyeLower0`][ld.lowerCenter]][2],s=(n+r)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=r),[i[0],i[1],l]})};async function Y9(e,t,a,n){var T,N;if(!(nn!=null&&nn.executor))return e;let{box:r,boxSize:s,crop:i}=j9(e,t,od.leftBounds[0],od.leftBounds[1],a,!0,((T=n.face.iris)==null?void 0:T.scale)||2.3),{box:o,boxSize:l,crop:u}=j9(e,t,od.rightBounds[0],od.rightBounds[1],a,!0,((N=n.face.iris)==null?void 0:N.scale)||2.3),p=lt([i,u]);J(i),J(u);let c=nn.execute(p);J(p);let d=await c.data();J(c);let h=d.slice(0,ld.numCoordinates*3),{rawCoords:m,iris:f}=q9(h,r,s,!0),g=d.slice(ld.numCoordinates*3),{rawCoords:y,iris:x}=q9(g,o,l,!1),A=i3e(e);Math.abs(A)<30?(L0(e,m,"left",null),L0(e,y,"right",null)):A<1?L0(e,m,"left",["EyeUpper0","EyeLower0"]):L0(e,y,"right",["EyeUpper0","EyeLower0"]);let b=X9(e,f,"left"),w=X9(e,x,"right");return e.concat(b).concat(w)}async function J9(e,t){var s,i,o,l,u,p,c,d,h,m;let a={lips:await((i=(s=t.filter(f=>f.size===160))==null?void 0:s[0])==null?void 0:i.data()),irisL:await((l=(o=t.filter(f=>f.size===10))==null?void 0:o[0])==null?void 0:l.data()),eyeL:await((p=(u=t.filter(f=>f.size===142))==null?void 0:u[0])==null?void 0:p.data()),irisR:await((d=(c=t.filter(f=>f.size===10))==null?void 0:c[1])==null?void 0:d.data()),eyeR:await((m=(h=t.filter(f=>f.size===142))==null?void 0:h[1])==null?void 0:m.data())};for(let f of Object.values(a))if(!f)return e;let n=gl.reduce((f,g)=>f+=e[g][2],0)/gl.length;for(let f=0;ff+=e[g][2],0)/yl.length;for(let f=0;fae()-cr.timestamp,n=cr.skipped<(((u=t.face.detector)==null?void 0:u.skipFrames)||0);!t.skipAllowed||!a||!n||cr.boxes.length===0?(cr.boxes=await G9(e,t),cr.timestamp=ae(),cr.skipped=0):cr.skipped++;let r=[],s=[],i=0,o=dc;for(let x=0;x[T[0]/(e.shape[2]||0),T[1]/(e.shape[1]||0),(T[2]||0)/o]);for(let T of Object.keys(ml))I.annotations[T]=[I.mesh[ml[T]]]}else if(!Ct)t.debug&&K("face mesh detection requested, but model is not loaded");else{if((h=t.face.attention)!=null&&h.enabled&&!ne.kernels.includes("atan2"))return t.face.attention.enabled=!1,J(I.tensor),r;let T=Ct.execute(I.tensor),M=await T.find($=>$.shape[$.shape.length-1]===1).data();if(I.faceScore=Math.round(100*M[0])/100,I.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(A.confidence=I.faceScore,t.face.mesh.keepInvalid){I.box=_0(A,e),I.boxRaw=F0(A,e),I.size=A.size,I.score=I.boxScore,I.mesh=A.landmarks,I.meshRaw=I.mesh.map($=>[$[0]/(e.shape[2]||1),$[1]/(e.shape[1]||1),($[2]||0)/o]);for(let $ of Object.keys(ml))I.annotations[$]=[I.mesh[ml[$]]]}}else{let $=T.find(O=>O.shape[O.shape.length-1]===1404),E=Q($,[-1,3]),S=await E.array();J(E),(f=t.face.attention)!=null&&f.enabled?S=await J9(S,T):(g=t.face.iris)!=null&&g.enabled&&(S=await Y9(S,I.tensor,dc,t)),I.mesh=z9(S,A,b,w,dc),I.meshRaw=I.mesh.map(O=>[O[0]/(e.shape[2]||0),O[1]/(e.shape[1]||0),(O[2]||0)/o]);for(let O of Object.keys(Pn))I.annotations[O]=Pn[O].map(W=>I.mesh[W]);I.score=I.faceScore;let _={...W9(I.mesh,A),confidence:A.confidence,landmarks:A.landmarks,size:A.size};I.box=_0(_,e),I.boxRaw=F0(_,e),I.size=_.size,s.push(_)}J(T)}I.score>(((y=t.face.detector)==null?void 0:y.minConfidence)||1)?r.push(I):J(I.tensor)}return cr.boxes=s,r}async function eI(e){var t,a,n,r,s,i;return ne.initial&&(Ct=null),(t=e.face.attention)!=null&&t.enabled&&(Ct!=null&&Ct.signature)&&Object.keys(((a=Ct==null?void 0:Ct.signature)==null?void 0:a.outputs)||{}).length<6&&(Ct=null),Ct?e.debug&&K("cached model:",Ct.modelUrl):(n=e.face.attention)!=null&&n.enabled?Ct=await $e(e.face.attention.modelPath):Ct=await $e((r=e.face.mesh)==null?void 0:r.modelPath),dc=Ct.executor&&((s=Ct==null?void 0:Ct.inputs)!=null&&s[0].shape)?(i=Ct==null?void 0:Ct.inputs)==null?void 0:i[0].shape[2]:256,Ct}var tI=fl,aI=lc;var $y=[],sa,W0=[],nI=0,rI=0,My=Number.MAX_SAFE_INTEGER,Py=!1;async function sI(e){var t,a,n;return ne.initial&&(sa=null),sa?e.debug&&K("cached model:",sa.modelUrl):(sa=await $e((t=e.face.emotion)==null?void 0:t.modelPath),Py=((n=(a=sa==null?void 0:sa.inputs)==null?void 0:a[0].shape)==null?void 0:n[3])===3,Py?$y=["angry","disgust","fear","happy","neutral","sad","surprise"]:$y=["angry","disgust","fear","happy","sad","surprise","neutral"]),sa}async function _y(e,t,a,n){var i,o;if(!sa)return[];let r=My<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>ae()-rI;return t.skipAllowed&&s&&r&&nI===n&&W0[a]&&W0[a].length>0?(My++,W0[a]):(My=0,new Promise(async l=>{var p,c,d;let u=[];if((p=t.face.emotion)!=null&&p.enabled){let h={},m=sa!=null&&sa.inputs[0].shape?sa.inputs[0].shape[2]:0;if(((c=t.face.emotion)==null?void 0:c.crop)>0){let g=(d=t.face.emotion)==null?void 0:d.crop,y=[[g,g,1-g,1-g]];h.resize=fe.cropAndResize(e,y,[0],[m,m])}else h.resize=fe.resizeBilinear(e,[m,m],!1);Py?(h.mul=te(h.resize,255),h.normalize=xe(h.mul,[103.939,116.779,123.68]),h.emotion=sa==null?void 0:sa.execute(h.normalize)):(h.channels=te(h.resize,ze.rgb),h.grayscale=ot(h.channels,3,!0),h.grayscaleSub=xe(h.grayscale,ze.tf05),h.grayscaleMul=te(h.grayscaleSub,ze.tf2),h.emotion=sa==null?void 0:sa.execute(h.grayscaleMul)),rI=ae();let f=await h.emotion.data();for(let g=0;g(t.face.emotion.minConfidence||0)&&u.push({score:Math.min(.99,Math.trunc(100*f[g])/100),emotion:$y[g]});u.sort((g,y)=>y.score-g.score),Object.keys(h).forEach(g=>J(h[g]))}W0[a]=u,nI=n,l(u)}))}var ia,Ss=[],oI=0,lI=0,Fy=Number.MAX_SAFE_INTEGER;async function uI(e){var t;return ne.initial&&(ia=null),ia?e.debug&&K("cached model:",ia.modelUrl):ia=await $e((t=e.face.description)==null?void 0:t.modelPath),ia}function l3e(e,t){var s,i;let a=e.image||e.tensor||e;if(!(ia!=null&&ia.inputs[0].shape))return a;let n;if(((s=t.face.description)==null?void 0:s.crop)>0){let o=(i=t.face.description)==null?void 0:i.crop,l=[[o,o,1-o,1-o]];n=fe.cropAndResize(a,l,[0],[ia.inputs[0].shape[2],ia.inputs[0].shape[1]])}else n=fe.resizeBilinear(a,[ia.inputs[0].shape[2],ia.inputs[0].shape[1]],!1);let r=te(n,ze.tf255);return J(n),r}async function Dy(e,t,a,n){var o,l,u,p;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(ia!=null&&ia.executor))return r;let s=Fy<(((o=t.face.description)==null?void 0:o.skipFrames)||0),i=(((l=t.face.description)==null?void 0:l.skipTime)||0)>ae()-oI;return t.skipAllowed&&s&&i&&lI===n&&((u=Ss==null?void 0:Ss[a])==null?void 0:u.age)>0&&((p=Ss==null?void 0:Ss[a])==null?void 0:p.genderScore)>0?(Fy++,Ss[a]):(Fy=0,new Promise(async c=>{var d;if((d=t.face.description)!=null&&d.enabled){let h=l3e(e,t),m=ia==null?void 0:ia.execute(h);oI=ae(),J(h);let g=await m.find(N=>N.shape[1]===1).data(),y=Math.trunc(200*Math.abs(g[0]-.5))/100;y>(t.face.description.minConfidence||0)&&(r.gender=g[0]<=.5?"female":"male",r.genderScore=Math.min(.99,y));let x=ir(m.find(N=>N.shape[1]===100),1),A=(await x.data())[0];J(x);let w=await m.find(N=>N.shape[1]===100).data();r.age=Math.round(w[A-1]>w[A+1]?10*A-100*w[A-1]:10*A+100*w[A+1])/10,(Number.isNaN(g[0])||Number.isNaN(w[0]))&&K("faceres error:",{model:ia,result:m});let I=m.find(N=>N.shape[1]===1024),T=I?await I.data():[];r.descriptor=Array.from(T),m.forEach(N=>J(N))}Ss[a]=r,lI=n,c(r)}))}var ud=.1,Oy=.5;function u3e(e,t,a){let n=!1,r=a.length-1;for(let s=0;st!=a[r].y>t&&e<(a[r].x-a[s].x)*(t-a[s].y)/(a[r].y-a[s].y)+a[s].x&&(n=!n);return n}async function pI(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,a=e.tensor.shape[1]||0,n=await e.tensor.buffer(),r=[];for(let i of Pn.silhouette)r.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});ud&&ud>0&&(r=r.map(i=>({x:i.x>.5?i.x+ud:i.x-ud,y:i.y>.5?i.y+ud:i.y-ud})));for(let i=0;iae()-hI,s=zy<(((o=t.face.antispoof)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&r&&s&&cI===n&&B0[a]?(zy++,B0[a]):(zy=0,new Promise(async l=>{let u=fe.resizeBilinear(e,[oa!=null&&oa.inputs[0].shape?oa.inputs[0].shape[2]:0,oa!=null&&oa.inputs[0].shape?oa.inputs[0].shape[1]:0],!1),p=oa==null?void 0:oa.execute(u),c=(await p.data())[0];B0[a]=Math.round(100*c)/100,cI=n,hI=ae(),J([u,p]),l(B0[a])}))}var la,V0=[],Wy=Number.MAX_SAFE_INTEGER,gI=0,yI=0;async function xI(e){var t;return ne.initial&&(la=null),la?e.debug&&K("cached model:",la.modelUrl):la=await $e((t=e.face.liveness)==null?void 0:t.modelPath),la}async function By(e,t,a,n){var i,o;if(!(la!=null&&la.executor))return 0;let r=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>ae()-yI,s=Wy<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&r&&s&&gI===n&&V0[a]?(Wy++,V0[a]):(Wy=0,new Promise(async l=>{let u=fe.resizeBilinear(e,[la!=null&&la.inputs[0].shape?la.inputs[0].shape[2]:0,la!=null&&la.inputs[0].shape?la.inputs[0].shape[1]:0],!1),p=la==null?void 0:la.execute(u),c=(await p.data())[0];V0[a]=Math.round(100*c)/100,gI=n,yI=ae(),J([u,p]),l(V0[a])}))}var _n,Vy=[],p3e=["white","black","asian","indian","other"],c3e=[15,23,28,35.5,45.5,55.5,65],bI=0,vI=0,Uy=Number.MAX_SAFE_INTEGER;async function wI(e){var t;return ne.initial&&(_n=null),_n?e.debug&&K("cached model:",_n.modelUrl):_n=await $e((t=e.face.gear)==null?void 0:t.modelPath),_n}async function Gy(e,t,a,n){var i,o;if(!_n)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=Uy<(((i=t.face.gear)==null?void 0:i.skipFrames)||0),s=(((o=t.face.gear)==null?void 0:o.skipTime)||0)>ae()-vI;return t.skipAllowed&&s&&r&&bI===n&&Vy[a]?(Uy++,Vy[a]):(Uy=0,new Promise(async l=>{var y,x,A,b;if(!(_n!=null&&_n.inputs[0].shape))return;let u={},p=[[0,.1,.9,.9]];if(((y=t.face.gear)==null?void 0:y.crop)>0){let w=(x=t.face.gear)==null?void 0:x.crop;p=[[w,w,1-w,1-w]]}u.resize=fe.cropAndResize(e,p,[0],[_n.inputs[0].shape[2],_n.inputs[0].shape[1]]);let c={age:0,gender:"unknown",genderScore:0,race:[]};(A=t.face.gear)!=null&&A.enabled&&([u.age,u.gender,u.race]=_n.execute(u.resize,["age_output","gender_output","race_output"]));let d=await u.gender.data();c.gender=d[0]>d[1]?"male":"female",c.genderScore=Math.round(100*(d[0]>d[1]?d[0]:d[1]))/100;let h=await u.race.data();for(let w=0;w(((b=t.face.gear)==null?void 0:b.minConfidence)||.2)&&c.race.push({score:Math.round(100*h[w])/100,race:p3e[w]});c.race.sort((w,I)=>I.score-w.score);let f=Array.from(await u.age.data()).map((w,I)=>[c3e[I],w]).sort((w,I)=>I[1]-w[1]),g=f[0][0];for(let w=1;wJ(u[w])),Vy[a]=c,bI=n,vI=ae(),l(c)}))}var $a,U0=[],II=0,SI=0,Hy=Number.MAX_SAFE_INTEGER;async function CI(e){return ne.initial&&($a=null),$a?e.debug&&K("cached model:",$a.modelUrl):$a=await $e(e.face.ssrnet.modelPathAge),$a}async function jy(e,t,a,n){var i,o,l,u;if(!$a)return{age:0};let r=Hy<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>ae()-SI;return t.skipAllowed&&r&&s&&II===n&&((l=U0[a])!=null&&l.age)&&((u=U0[a])==null?void 0:u.age)>0?(Hy++,U0[a]):(Hy=0,new Promise(async p=>{var h,m,f;if(!($a!=null&&$a.inputs)||!$a.inputs[0]||!$a.inputs[0].shape)return;let c={};if(((h=t.face.ssrnet)==null?void 0:h.crop)>0){let g=(m=t.face.ssrnet)==null?void 0:m.crop,y=[[g,g,1-g,1-g]];c.resize=fe.cropAndResize(e,y,[0],[$a.inputs[0].shape[2],$a.inputs[0].shape[1]])}else c.resize=fe.resizeBilinear(e,[$a.inputs[0].shape[2],$a.inputs[0].shape[1]],!1);c.enhance=te(c.resize,ze.tf255);let d={age:0};if((f=t.face.ssrnet)!=null&&f.enabled&&(c.age=$a.execute(c.enhance)),c.age){let g=await c.age.data();d.age=Math.trunc(10*g[0])/10}Object.keys(c).forEach(g=>J(c[g])),U0[a]=d,II=n,SI=ae(),p(d)}))}var xa,G0=[],NI=0,RI=0,qy=Number.MAX_SAFE_INTEGER,Xy=[.2989,.587,.114];async function EI(e){var t;return ne.initial&&(xa=null),xa?e.debug&&K("cached model:",xa.modelUrl):xa=await $e((t=e.face.ssrnet)==null?void 0:t.modelPathGender),xa}async function Ky(e,t,a,n){var i,o,l,u;if(!xa)return{gender:"unknown",genderScore:0};let r=qy<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>ae()-RI;return t.skipAllowed&&r&&s&&NI===n&&((l=G0[a])!=null&&l.gender)&&((u=G0[a])==null?void 0:u.genderScore)>0?(qy++,G0[a]):(qy=0,new Promise(async p=>{var m,f,g;if(!(xa!=null&&xa.inputs[0].shape))return;let c={};if(((m=t.face.ssrnet)==null?void 0:m.crop)>0){let y=(f=t.face.ssrnet)==null?void 0:f.crop,x=[[y,y,1-y,1-y]];c.resize=fe.cropAndResize(e,x,[0],[xa.inputs[0].shape[2],xa.inputs[0].shape[1]])}else c.resize=fe.resizeBilinear(e,[xa.inputs[0].shape[2],xa.inputs[0].shape[1]],!1);c.enhance=De(()=>{var x,A;let y;if(((A=(x=xa==null?void 0:xa.inputs)==null?void 0:x[0].shape)==null?void 0:A[3])===1){let[b,w,I]=Sa(c.resize,3,3),T=te(b,Xy[0]),N=te(w,Xy[1]),M=te(I,Xy[2]),$=Dh([T,N,M]);y=te(xe($,ze.tf05),2)}else y=te(xe(c.resize,ze.tf05),2);return y});let d={gender:"unknown",genderScore:0};(g=t.face.ssrnet)!=null&&g.enabled&&(c.gender=xa.execute(c.enhance));let h=await c.gender.data();d.gender=h[0]>h[1]?"female":"male",d.genderScore=h[0]>h[1]?Math.trunc(100*h[0])/100:Math.trunc(100*h[1])/100,Object.keys(c).forEach(y=>J(c[y])),G0[a]=d,NI=n,RI=ae(),p(d)}))}var rn,Yy=[],$I=0,PI=0,_I=Number.MAX_SAFE_INTEGER;async function FI(e){var t;return ne.initial&&(rn=null),rn?e.debug&&K("cached model:",rn.modelUrl):rn=await $e((t=e.face.mobilefacenet)==null?void 0:t.modelPath),rn}async function Zy(e,t,a,n){var i,o;if(!(rn!=null&&rn.executor))return[];let r=_I<(((i=t.face.mobilefacenet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.mobilefacenet)==null?void 0:o.skipTime)||0)>ae()-PI;return t.skipAllowed&&s&&r&&$I===n&&Yy[a]?(_I++,Yy[a]):new Promise(async l=>{var p;let u=[];if((p=t.face.mobilefacenet)!=null&&p.enabled&&(rn!=null&&rn.inputs[0].shape)){let c={};c.crop=fe.resizeBilinear(e,[rn.inputs[0].shape[2],rn.inputs[0].shape[1]],!1),c.data=rn.execute(c.crop);let d=await c.data.data();u=Array.from(d),Object.keys(c).forEach(h=>J(c[h]))}Yy[a]=u,$I=n,PI=ae(),l(u)})}var sn,Jy=[],OI=0,zI=0,LI=Number.MAX_SAFE_INTEGER;async function WI(e){return ne.initial&&(sn=null),sn?e.debug&&K("cached model:",sn.modelUrl):sn=await $e(e.face.insightface.modelPath),sn}async function Qy(e,t,a,n){var i,o;if(!(sn!=null&&sn.executor))return[];let r=LI<(((i=t.face.insightface)==null?void 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${n<0?"left":"right"}`}),Math.abs(e[a].mesh[374][1]-e[a].mesh[386][1])/Math.abs(e[a].mesh[443][1]-e[a].mesh[450][1])<.2&&t.push({face:a,gesture:"blink left eye"}),Math.abs(e[a].mesh[145][1]-e[a].mesh[159][1])/Math.abs(e[a].mesh[223][1]-e[a].mesh[230][1])<.2&&t.push({face:a,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[a].mesh[13][1]-e[a].mesh[14][1])/Math.abs(e[a].mesh[10][1]-e[a].mesh[152][1]));o>10&&t.push({face:a,gesture:`mouth ${Math.trunc(o)}% open`});let l=e[a].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:a,gesture:`head ${l<0?"up":"down"}`})}return t},eS=e=>{var a,n,r,s;if(!e)return[];let t=[];for(let i=0;i.06||g>.06)&&(h=!1),f>g?g>.04&&t.push({iris:i,gesture:"looking right"}):f>.04&&t.push({iris:i,gesture:"looking left"});let y=Math.abs(e[i].mesh[145][1]-e[i].annotations.rightEyeIris[0][1])/e[i].box[3],x=Math.abs(e[i].mesh[374][1]-e[i].annotations.leftEyeIris[0][1])/e[i].box[3];(x<.01||y<.01||x>.022||y>.022)&&(h=!1),(x<.01||y<.01)&&t.push({iris:i,gesture:"looking down"}),(x>.022||y>.022)&&t.push({iris:i,gesture:"looking up"}),h&&t.push({iris:i,gesture:"looking center"})}return t},tS=e=>{if(!e)return[];let t=[];for(let a=0;a0){let r=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:a,gesture:`${r.name} forward`});let s=n.reduce((i,o)=>i.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:a,endPoint:n,palmLandmarks:r,confidence:e.confidence}}function q0(e,t=1.5){let a=pc(e),n=j0(e),r=[t*n[0]/2,t*n[1]/2],s=[a[0]-r[0],a[1]-r[1]],i=[a[0]+r[0],a[1]+r[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function X0(e){let t=pc(e),a=j0(e),r=Math.max(...a)/2,s=[t[0]-r,t[1]-r],i=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function k3e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function iS(e,t){let a=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return k3e(a)}var aS=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ms(e,t){let a=0;for(let n=0;n[i.x,i.y]),this.anchorsTensor=Qn(this.anchors),this.inputSize=((s=(r=(n=(a=this==null?void 0:this.model)==null?void 0:a.inputs)==null?void 0:n[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=Bt([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=Bt([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let a={};a.boxOffsets=Fe(t,[0,0],[-1,2]),a.boxSizes=Fe(t,[0,2],[-1,2]),a.div=ve(a.boxOffsets,this.inputSizeTensor),a.boxCenterPoints=we(a.div,this.anchorsTensor),a.halfBoxSizes=ve(a.boxSizes,this.doubleInputSizeTensor),a.sub=xe(a.boxCenterPoints,a.halfBoxSizes),a.startPoints=te(a.sub,this.inputSizeTensor),a.add=we(a.boxCenterPoints,a.halfBoxSizes),a.endPoints=te(a.add,this.inputSizeTensor);let n=Uu([a.startPoints,a.endPoints],1);return Object.keys(a).forEach(r=>J(a[r])),n}normalizeLandmarks(t,a){let n={};n.reshape=Q(t,[-1,7,2]),n.div=ve(n.reshape,this.inputSizeTensor),n.landmarks=we(n.div,this.anchors[a]?this.anchors[a]:0);let r=te(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>J(n[s])),r}async predict(t,a){var o;let n={};n.resize=fe.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=ve(n.resize,ze.tf127),n.image=xe(n.div,ze.tf1),n.batched=this.model.execute(n.image),n.predictions=Oe(n.batched),n.slice=Fe(n.predictions,[0,0],[-1,1]),n.sigmoid=za(n.slice),n.scores=Oe(n.sigmoid);let r=await n.scores.data();n.boxes=Fe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await fe.nonMaxSuppressionAsync(n.norm,n.scores,3*(((o=a.hand)==null?void 0:o.maxDetected)||1),a.hand.iouThreshold,a.hand.minConfidence);let s=await n.nms.array(),i=[];for(let l of s){let u={};u.box=Fe(n.norm,[l,0],[1,-1]),u.slice=Fe(n.predictions,[l,5],[1,14]),u.norm=this.normalizeLandmarks(u.slice,l),u.palmLandmarks=Q(u.norm,[-1,2]);let p=await u.box.data(),c=p.slice(0,2),d=p.slice(2,4),h=await u.palmLandmarks.array(),m={startPoint:c,endPoint:d,palmLandmarks:h,confidence:r[l]},f=sS(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);i.push(f),Object.keys(u).forEach(g=>J(u[g]))}return Object.keys(n).forEach(l=>J(n[l])),i}};var T3e=5,dS=1.65,pS=[0,5,9,13,17,1,2],N3e=0,R3e=2,cS=0,Y0=class{constructor(t,a){he(this,"handDetector");he(this,"handPoseModel");he(this,"inputSize");he(this,"storedBoxes");he(this,"skipped");he(this,"detectedHands");var n,r,s;this.handDetector=t,this.handPoseModel=a,this.inputSize=((s=(r=(n=this.handPoseModel)==null?void 0:n.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let a=t.map(i=>i[0]),n=t.map(i=>i[1]),r=[Math.min(...a),Math.min(...n)],s=[Math.max(...a),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,a){let n=t.map(s=>rx([...s,1],a)),r=this.calculateLandmarksBoundingBox(n);return q0(X0(r),T3e)}getBoxForHandLandmarks(t){let a=this.calculateLandmarksBoundingBox(t),n=q0(X0(a),dS);n.palmLandmarks=[];for(let r=0;r[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=nx(n,[0,0]),u=o.map(h=>[...rx(h,l),h[2]]),p=oS(r),c=[...pc(a),1],d=[Ms(c,p[0]),Ms(c,p[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2])])}async estimateHands(t,a){let n=!1,r,s=(a.hand.skipTime||0)>ae()-cS,i=this.skipped<(a.hand.skipFrames||0);a.skipAllowed&&s&&i?this.skipped++:(r=await this.handDetector.predict(t,a),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==a.hand.maxDetected||!a.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l=a.hand.minConfidence/4){let w=Q(A,[-1,3]),I=await w.array();J(A),J(w);let T=this.transformRawCoords(I,f,p,m),N=this.getBoxForHandLandmarks(T);this.storedBoxes[l]={...N,confidence:b};let M={landmarks:T,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:N.startPoint,bottomRight:N.endPoint}};o.push(M)}else this.storedBoxes[l]=null;J(A)}else{let p=q0(X0(u),dS),c={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:p.startPoint,bottomRight:p.endPoint},landmarks:[]};o.push(c)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>a.hand.maxDetected&&(o.length=a.hand.maxDetected),o}};var hS={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},Il,Sl,sx;function M3e(){let e=Il?new K0(Il):void 0;e&&Sl&&(sx=new Y0(e,Sl))}async function ix(e,t){sx||M3e();let a=await sx.estimateHands(e,t);if(!a)return[];let n=[];for(let r=0;ra[r].landmarks[c]);let i=a[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let p of i)p[0]o[2]&&(o[2]=p[0]),p[1]>o[3]&&(o[3]=p[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=a[r].box?[Math.trunc(Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.max(0,a[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,a[r].box.bottomRight[0])-Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,a[r].box.bottomRight[1])-Math.max(0,a[r].box.topLeft[1]))]:[0,0,0,0],l=[a[r].box.topLeft[0]/(e.shape[2]||0),a[r].box.topLeft[1]/(e.shape[1]||0),(a[r].box.bottomRight[0]-a[r].box.topLeft[0])/(e.shape[2]||0),(a[r].box.bottomRight[1]-a[r].box.topLeft[1])/(e.shape[1]||0)];let u=H0(i);n.push({id:r,score:Math.round(100*a[r].confidence)/100,boxScore:Math.round(100*a[r].boxConfidence)/100,fingerScore:Math.round(100*a[r].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function mS(e){var t;return ne.initial&&(Il=null),Il?e.debug&&K("cached model:",Il.modelUrl):Il=await $e((t=e.hand.detector)==null?void 0:t.modelPath),Il}async function fS(e){var t;return ne.initial&&(Sl=null),Sl?e.debug&&K("cached model:",Sl.modelUrl):Sl=await $e((t=e.hand.skeleton)==null?void 0:t.modelPath),Sl}var Ot=[null,null],$3e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],$s=[[0,0],[0,0]],P3e=["hand","fist","pinch","point","face","tip","pinchtip"],yS=4,xS=1.6,_3e=512,F3e=1.4,Z0=Number.MAX_SAFE_INTEGER,ox=0,Dr=[0,0],Dt={boxes:[],hands:[]},AS={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function bS(e){var t;if(ne.initial&&(Ot[0]=null),Ot[0])e.debug&&K("cached model:",Ot[0].modelUrl);else{b0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Ot[0]=await $e((t=e.hand.detector)==null?void 0:t.modelPath);let a=Ot[0].executor?Object.values(Ot[0].modelSignature.inputs):void 0;$s[0][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,$s[0][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Ot[0]}async function vS(e){var t;if(ne.initial&&(Ot[1]=null),Ot[1])e.debug&&K("cached model:",Ot[1].modelUrl);else{Ot[1]=await $e((t=e.hand.skeleton)==null?void 0:t.modelPath);let a=Ot[1].executor?Object.values(Ot[1].modelSignature.inputs):void 0;$s[1][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,$s[1][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Ot[1]}async function D3e(e,t){let a=[];if(!e||!Ot[0])return a;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,_3e),i=Math.round(s*r/8)*8;n.resize=fe.resizeBilinear(e,[s,i]),n.cast=Ue(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await Ot[0].executeAsync(n.cast,$3e),n.boxes=Oe(n.rawBoxes,[0,2]),n.scores=Oe(n.rawScores,[0]);let o=Na(n.scores,1);J(o[yS]),o.splice(yS,1),n.filtered=ha(o,1),J(o),n.max=ga(n.filtered,1),n.argmax=ir(n.filtered,1);let l=0;n.nms=await fe.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),p=await n.max.data(),c=await n.argmax.data();for(let d of Array.from(u)){let h=Fe(n.boxes,d,1),m=await h.data();J(h);let f=[m[1],m[0],m[3]-m[1],m[2]-m[0]],g=R0(f,F3e),y=[Math.trunc(f[0]*Dr[0]),Math.trunc(f[1]*Dr[1]),Math.trunc(f[2]*Dr[0]),Math.trunc(f[3]*Dr[1])],x=p[d],A=P3e[c[d]],b={id:l++,score:x,box:y,boxRaw:g,label:A};a.push(b)}return Object.keys(n).forEach(d=>J(n[d])),a.sort((d,h)=>h.score-d.score),a.length>(t.hand.maxDetected||1)&&(a.length=t.hand.maxDetected||1),a}async function lx(e,t,a){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Ot[1]&&a.hand.landmarks&&t.score>(a.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=fe.cropAndResize(e,[s],[0],[$s[1][0],$s[1][1]],"bilinear"),r.div=ve(r.crop,ze.tf255),[r.score,r.keypoints]=Ot[1].execute(r.div,["Identity_1","Identity"]);let i=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(a.hand.minConfidence||0)){n.fingerScore=o,r.reshaped=Q(r.keypoints,[-1,3]);let p=(await r.reshaped.array()).map(c=>[c[0]/$s[1][1],c[1]/$s[1][0],c[2]||0]).map(c=>[c[0]*t.boxRaw[2],c[1]*t.boxRaw[3],c[2]||0]);n.keypoints=p.map(c=>[Dr[0]*(c[0]+t.boxRaw[0]),Dr[1]*(c[1]+t.boxRaw[1]),c[2]||0]),n.landmarks=H0(n.keypoints);for(let c of Object.keys(AS))n.annotations[c]=AS[c].map(d=>n.landmarks&&n.keypoints[d]?n.keypoints[d]:null)}Object.keys(r).forEach(l=>J(r[l]))}return n}async function ux(e,t){var r,s;if(!((r=Ot[0])!=null&&r.executor)||!((s=Ot[1])!=null&&s.executor)||!Ot[0].inputs[0].shape||!Ot[1].inputs[0].shape)return[];Dr=[e.shape[2]||0,e.shape[1]||0],Z0++;let a=(t.hand.skipTime||0)>ae()-ox,n=Z0<(t.hand.skipFrames||0);return t.skipAllowed&&a&&n?Dt.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>ae()-ox,l=Z0<3*(t.hand.skipFrames||0);t.skipAllowed&&Dt.hands.length===t.hand.maxDetected?Dt.hands=await Promise.all(Dt.boxes.map(p=>lx(e,p,t))):t.skipAllowed&&o&&l&&Dt.hands.length>0?Dt.hands=await Promise.all(Dt.boxes.map(p=>lx(e,p,t))):(Dt.boxes=await D3e(e,t),ox=ae(),Dt.hands=await Promise.all(Dt.boxes.map(p=>lx(e,p,t))),Z0=0);let u=[...Dt.boxes];if(Dt.boxes.length=0,t.cacheSensitivity>0)for(let p=0;p.05&&c.box[3]/(e.shape[1]||1)>.05&&Dt.hands[p].fingerScore&&Dt.hands[p].fingerScore>(t.hand.minConfidence||0)){let d=R0(c.box,xS),h=R0(c.boxRaw,xS);Dt.boxes.push({...u[p],box:d,boxRaw:h})}}for(let p=0;p({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var cc={};Ar(cc,{connected:()=>Q0,horizontal:()=>dx,kpt:()=>J0,relative:()=>cx,vertical:()=>px});var J0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],dx=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],px=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],cx=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Q0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Ae=hr(),hx=0;function kS(e,t){var i,o,l,u,p,c,d,h,m,f,g,y,x,A,b,w,I,T,N,M,$,E,S,_,O,W;let a=ae();if(!e)return hr();let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(Ae.canvas=e.canvas),e.error&&(Ae.error=e.error),!Ae.body||e.body.length!==Ae.body.length)Ae.body=JSON.parse(JSON.stringify(e.body));else for(let P=0;P((r-1)*Ae.body[P].box[X]+Z)/r),G=e.body[P].boxRaw.map((Z,X)=>((r-1)*Ae.body[P].boxRaw[X]+Z)/r),q=e.body[P].keypoints.map((Z,X)=>{var re,ee,ge,ie,be,Ce,Re,Le,qe;return{score:Z.score,part:Z.part,position:[Ae.body[P].keypoints[X]?((r-1)*(Ae.body[P].keypoints[X].position[0]||0)+(Z.position[0]||0))/r:Z.position[0],Ae.body[P].keypoints[X]?((r-1)*(Ae.body[P].keypoints[X].position[1]||0)+(Z.position[1]||0))/r:Z.position[1],Ae.body[P].keypoints[X]?((r-1)*(Ae.body[P].keypoints[X].position[2]||0)+(Z.position[2]||0))/r:Z.position[2]],positionRaw:[Ae.body[P].keypoints[X]?((r-1)*(Ae.body[P].keypoints[X].positionRaw[0]||0)+(Z.positionRaw[0]||0))/r:Z.positionRaw[0],Ae.body[P].keypoints[X]?((r-1)*(Ae.body[P].keypoints[X].positionRaw[1]||0)+(Z.positionRaw[1]||0))/r:Z.positionRaw[1],Ae.body[P].keypoints[X]?((r-1)*(Ae.body[P].keypoints[X].positionRaw[2]||0)+(Z.positionRaw[2]||0))/r:Z.positionRaw[2]],distance:[Ae.body[P].keypoints[X]?((r-1)*(((re=Ae.body[P].keypoints[X].distance)==null?void 0:re[0])||0)+(((ee=Z.distance)==null?void 0:ee[0])||0))/r:(ge=Z.distance)==null?void 0:ge[0],Ae.body[P].keypoints[X]?((r-1)*(((ie=Ae.body[P].keypoints[X].distance)==null?void 0:ie[1])||0)+(((be=Z.distance)==null?void 0:be[1])||0))/r:(Ce=Z.distance)==null?void 0:Ce[1],Ae.body[P].keypoints[X]?((r-1)*(((Re=Ae.body[P].keypoints[X].distance)==null?void 0:Re[2])||0)+(((Le=Z.distance)==null?void 0:Le[2])||0))/r:(qe=Z.distance)==null?void 0:qe[2]]}}),H={},V={connected:{}};(i=t.body.modelPath)!=null&&i.includes("efficientpose")?V=$0:(o=t.body.modelPath)!=null&&o.includes("blazepose")?V=T0:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(V=cc);for(let[Z,X]of Object.entries(V.connected)){let re=[];for(let ee=0;eebe.part===X[ee]),ie=q.find(be=>be.part===X[ee+1]);ge&&ie&&re.push([ge.position,ie.position])}H[Z]=re}Ae.body[P]={...e.body[P],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!Ae.hand||e.hand.length!==Ae.hand.length)Ae.hand=JSON.parse(JSON.stringify(e.hand));else for(let P=0;P((r-1)*Ae.hand[P].box[Z]+V)/r),G=e.hand[P].boxRaw.map((V,Z)=>((r-1)*Ae.hand[P].boxRaw[Z]+V)/r);Ae.hand[P].keypoints.length!==e.hand[P].keypoints.length&&(Ae.hand[P].keypoints=e.hand[P].keypoints);let q=e.hand[P].keypoints&&e.hand[P].keypoints.length>0?e.hand[P].keypoints.map((V,Z)=>V.map((X,re)=>((r-1)*(Ae.hand[P].keypoints[Z][re]||1)+(X||0))/r)):[],H={};if(Object.keys(Ae.hand[P].annotations).length!==Object.keys(e.hand[P].annotations).length)Ae.hand[P].annotations=e.hand[P].annotations,H=Ae.hand[P].annotations;else if(e.hand[P].annotations)for(let V of Object.keys(e.hand[P].annotations))H[V]=(c=(p=(u=e.hand[P])==null?void 0:u.annotations)==null?void 0:p[V])!=null&&c[0]?e.hand[P].annotations[V].map((Z,X)=>Z.map((re,ee)=>((r-1)*Ae.hand[P].annotations[V][X][ee]+re)/r)):null;Ae.hand[P]={...e.hand[P],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!Ae.face||e.face.length!==Ae.face.length)Ae.face=JSON.parse(JSON.stringify(e.face));else for(let 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a={};a.resize=fe.resizeBilinear(e,[Aa.inputs[0].shape?Aa.inputs[0].shape[1]:0,Aa.inputs[0].shape?Aa.inputs[0].shape[2]:0],!1),a.norm=ve(a.resize,ze.tf255),a.res=Aa.execute(a.norm),a.squeeze=Oe(a.res,[0]),[a.bgRaw,a.fgRaw]=Na(a.squeeze,2),a.fg=Uh(a.fgRaw),a.mul=te(a.fg,ze.tf255),a.expand=Wt(a.mul,2),a.output=fe.resizeBilinear(a.expand,[e.shape[1]||0,e.shape[2]||0]);let n;switch(t.segmentation.mode||"default"){case"default":a.input=Oe(e),a.concat=lt([a.input,a.output],-1),n=Ue(a.concat,"int32");break;case"alpha":n=Ue(a.output,"int32");break;default:n=Ve(0)}return Object.keys(a).forEach(s=>J(a[s])),n}var em={};Ar(em,{distance:()=>fx,find:()=>L3e,similarity:()=>z3e});function fx(e,t,a={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let n=0;for(let s=0;s{if(e===0)return 1;let s=(1-(t===2?Math.sqrt(e):e**(1/t))/100-a)/(n-a);return Math.round(100*Math.max(Math.min(s,1),0))/100};function z3e(e,t,a={order:2,multiplier:25,min:.2,max:.8}){let n=fx(e,t,a);return 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o=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[i].position[0]-e.keypoints[n].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[i].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0])]:[0,0];if(o[0]>o[1]||l[0]>l[1]){let u=e.keypoints[n];e.keypoints[n]=e.keypoints[r],e.keypoints[r]=u}}}function NS(e){for(let t=0;te.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],a.pad=Rn(e,on.padding),a.resize=fe.resizeBilinear(a.pad,[t,t]);let n=Ue(a.resize,"int32");return Object.keys(a).forEach(i=>J(a[i])),n}function ES(e,t){e.keypoints=e.keypoints.filter(n=>n==null?void 0:n.position);for(let n of e.keypoints)n.position=[n.position[0]*(t[0]+on.padding[2][0]+on.padding[2][1])/t[0]-on.padding[2][0],n.position[1]*(t[1]+on.padding[1][0]+on.padding[1][1])/t[1]-on.padding[1][0]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1]];let a=ws(e.keypoints.map(n=>n.position),t);return e.box=a.box,e.boxRaw=a.boxRaw,e}var jt,tm=0,yx=Number.MAX_SAFE_INTEGER,Cl={boxes:[],bodies:[],last:0};async function MS(e){var t;return ne.initial&&(jt=null),jt?e.debug&&K("cached model:",jt.modelUrl):(b0(["size"],e),jt=await $e(e.body.modelPath)),tm=jt!=null&&jt.executor&&((t=jt==null?void 0:jt.inputs)!=null&&t[0].shape)?jt.inputs[0].shape[2]:0,tm<64&&(tm=256),B().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS&&B().set("WEBGL_USE_SHAPES_UNIFORMS",!1),jt}function B3e(e,t,a){let n=e[0][0],r=[],s=0;for(let p=0;pt.body.minConfidence){let c=[n[p][1],n[p][0]];r.push({score:Math.round(100*s)/100,part:J0[p],positionRaw:c,position:[Math.round((a.shape[2]||0)*c[0]),Math.round((a.shape[1]||0)*c[1])]})}s=r.reduce((p,c)=>c.score>p?c.score:p,0);let i=[],o=ws(r.map(p=>p.position),[a.shape[2],a.shape[1]]),l={};for(let[p,c]of Object.entries(Q0)){let d=[];for(let h=0;hg.part===c[h]),f=r.find(g=>g.part===c[h+1]);m&&f&&m.score>(t.body.minConfidence||0)&&f.score>(t.body.minConfidence||0)&&d.push([m.position,f.position])}l[p]=d}let u={id:0,score:s,box:o.box,boxRaw:o.boxRaw,keypoints:r,annotations:l};return gx(u),i.push(u),i}function V3e(e,t,a){let n=[];for(let r=0;rt.body.minConfidence){let o=[];for(let d=0;d<17;d++){let h=s[3*d+2];if(h>t.body.minConfidence){let m=[s[3*d+1],s[3*d+0]];o.push({part:J0[d],score:Math.round(100*h)/100,positionRaw:m,position:[Math.round((a.shape[2]||0)*m[0]),Math.round((a.shape[1]||0)*m[1])]})}}let l=[s[52],s[51],s[54]-s[52],s[53]-s[51]],u=[Math.trunc(l[0]*(a.shape[2]||0)),Math.trunc(l[1]*(a.shape[1]||0)),Math.trunc(l[2]*(a.shape[2]||0)),Math.trunc(l[3]*(a.shape[1]||0))],p={};for(let[d,h]of Object.entries(Q0)){let m=[];for(let f=0;fx.part===h[f]),y=o.find(x=>x.part===h[f+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&m.push([g.position,y.position])}p[d]=m}let c={id:r,score:i,box:u,boxRaw:l,keypoints:[...o],annotations:p};gx(c),n.push(c)}}return n.sort((r,s)=>s.score-r.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function xx(e,t){var r;if(!(jt!=null&&jt.executor)||!((r=jt==null?void 0:jt.inputs)!=null&&r[0].shape))return[];t.skipAllowed||(Cl.boxes.length=0),yx++;let a=(t.body.skipTime||0)>ae()-Cl.last,n=yx<(t.body.skipFrames||0);return t.skipAllowed&&a&&n?Cl.bodies:new Promise(async s=>{let i={};yx=0,i.input=RS(e,tm),i.res=jt==null?void 0:jt.execute(i.input),Cl.last=ae();let o=await 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n=["const","placeholder","noop","pad","squeeze","add","sub","mul","div"],r=["biasadd","fusedbatchnormv3","matmul","switch","shape","merge","split","broadcastto"],s=[],i=[],o=t.modelUrl,l=t.executor;if((p=l==null?void 0:l.graph)!=null&&p.nodes)for(let c of Object.values(l.graph.nodes)){let d=c.op.toLowerCase();s.includes(d)||s.push(d)}else!l&&e.config.debug&&K("model not loaded",a);for(let c of s)!n.includes(c)&&!r.includes(c)&&!e.env.kernels.includes(c)&&!e.env.kernels.includes(c.replace("_",""))&&!e.env.kernels.includes(c.replace("native",""))&&!e.env.kernels.includes(c.replace("v2",""))&&i.push(c);return e.config.debug&&i.length>0&&K("model validation failed:",a,i),i.length>0?{name:a,missing:i,ops:s,url:o}:null}var fc=class{constructor(t){he(this,"instance");he(this,"models",{});this.models={},this.instance=t}stats(){let t=0,a=0,n=0;for(let s of 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t)h.box[0]>b.box[0]&&h.box[0]b.box[1]&&h.box[1]+h.box[3]m.body.box[0]&&b.box[0]+b.box[2]m.body.box[1]&&b.box[1]+b.box[3]m.body.box[0]&&b.box[1]+b.box[3]>m.body.box[1]&&b.box[1]+b.box[3]{b&&b.length===4&&(f.push(b[0],b[0]+b[2]),g.push(b[1],b[1]+b[3]))};y(m.face.box),y((p=m.body)==null?void 0:p.box),y((c=m.hands.left)==null?void 0:c.box),y((d=m.hands.right)==null?void 0:d.box);let x=Math.min(...f),A=Math.min(...g);m.box=[x,A,Math.max(...f)-x,Math.max(...g)-A],r!=null&&r[1]&&(r!=null&&r[2])&&(m.boxRaw=[m.box[0]/r[2],m.box[1]/r[1],m.box[2]/r[2],m.box[3]/r[1]]),i.push(m)}return i}var lm=` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var H3=0;function Lge(e,t,a){let n=Et(Ft,a);if(!t||!e)return;let r=vn(e);if(r){r.lineJoin="round",r.font=n.font;for(let s=0;sX3,kpt:()=>q3});var 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kn,tl=224,Yk,Uge=5,l0=[8,16,32,32,32];function Gge(){let e=[],t=0;for(;ta.x)),y:Bt(e.map(a=>a.y))}}async function Zk(e){if(ne.initial&&(kn=null),!kn&&e.body.detector&&e.body.detector.modelPath){kn=await $e(e.body.detector.modelPath);let t=kn!=null&&kn.executor?Object.values(kn.modelSignature.inputs):void 0;tl=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&kn&&K("cached model:",kn.modelUrl);return Gge(),kn}var Kk=[5,5];function Hge(e,t){return De(()=>{let a=Sa(e,12,1),n=Oe(a[0]),r=Oe(a[1]),s=Oe(a[2]),i=Oe(a[3]);n=we(ve(n,tl),t.x),r=we(ve(r,tl),t.y),s=te(ve(s,tl),Kk[0]),i=te(ve(i,tl),Kk[1]);let o=xe(n,ve(s,2)),l=xe(r,ve(i,2)),u=we(o,s),p=we(l,i);return ha([o,l,u,p],1)})}async function jge(e,t,a,n){var u,p;let r=[],s={};s.boxes=Hge(e,Yk),s.scores=za(t),s.nms=await fe.nonMaxSuppressionAsync(s.boxes,s.scores,1,((u=a.body.detector)==null?void 0:u.minConfidence)||.1,((p=a.body.detector)==null?void 0:p.iouThreshold)||.1);let i=await s.nms.data(),o=await 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a=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...a[0]),Math.min(...a[1])],r=[Math.max(...a[0]),Math.max(...a[1])],s=[(n[0]+r[0])/2,(n[1]+r[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+r[0],-s[1]+r[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function u0(e,t){let a=[e[2]*t,e[3]*t];return[e[0]-(a[0]-e[2])/2,e[1]-(a[1]-e[3])/2,a[0],a[1]]}var Ua,Y3=256,K3=Number.MAX_SAFE_INTEGER,qge={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},p0=[],ws=[[0,0],[0,0],[0,0],[0,0]],e9=0,t9=e=>1-1/(1+Math.exp(e)),n9=e=>Zk(e);async function r9(e){if(ne.initial&&(Ua=null),Ua)e.debug&&K("cached model:",Ua.modelUrl);else{Ua=await $e(e.body.modelPath);let t=Ua!=null&&Ua.executor?Object.values(Ua.modelSignature.inputs):void 0;Y3=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Ua}function a9(e,t,a){var s,i;let n={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((i=e==null?void 0:e.shape)!=null&&i[2]))return e;let r;if(a&&(n.cropped=fe.cropAndResize(e,[a],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let o=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],l=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];ws=[[0,0],o,l,[0,0]],n.pad=Rn(n.cropped||e,ws),n.resize=fe.resizeBilinear(n.pad,[t,t]),r=ve(n.resize,ze.tf255)}else e.shape[1]!==t?(n.resize=fe.resizeBilinear(n.cropped||e,[t,t]),r=ve(n.resize,ze.tf255)):r=ve(n.cropped||e,ze.tf255);return Object.keys(n).forEach(o=>J(n[o])),r}function Xge(e,t,a){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+ws[2][0]+ws[2][1])/t[0]-ws[2][0]),Math.trunc(n.position[1]*(t[1]+ws[1][0]+ws[1][1])/t[1]-ws[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],2*n.position[2]/(t[0]+t[1])];if(a){let n=a[2]-a[0],r=a[3]-a[1];for(let s of e)s.positionRaw=[s.positionRaw[0]/r+a[1],s.positionRaw[1]/n+a[0],s.positionRaw[2]],s.position=[Math.trunc(s.positionRaw[0]*t[0]),Math.trunc(s.positionRaw[1]*t[1]),s.positionRaw[2]]}return e}function Kge(e){let t=e.find(o=>o.part==="leftPalm"),a=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((a.position[2]||0)+(n.position[2]||0))/2;let r=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");r.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function Yge(e,t,a){if(!(Ua!=null&&Ua.executor))return null;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=Ua==null?void 0:Ua.execute(e,qge.landmarks);let r=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(m=>J(n[m]));let o=[],l=5;for(let m=0;mm.position),c=vs(p,[a[0],a[1]]),d={};for(let[m,f]of Object.entries(X3)){let g=[];for(let y=0;yb.part===f[y]),A=u.find(b=>b.part===f[y+1]);x&&A&&g.push([x.position,A.position])}d[m]=g}return{id:0,score:Math.trunc(100*r)/100,box:c.box,boxRaw:c.boxRaw,keypoints:u,annotations:d}}async function Z3(e,t){var s,i,o;let a=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>ae()-e9,r=K3<(t.body.skipFrames||0);if(t.skipAllowed&&n&&r&&p0!==null)K3++;else{let l=[];if((i=(s=t.body)==null?void 0:s.detector)!=null&&i.enabled){let u=a9(e,224);l=await Jk(u,t,a),J(u)}else l=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let u=0;uJ(n[u])),r}async function ey(e,t){if(!(Ga!=null&&Ga.executor))return[];let a=(t.object.skipTime||0)>ae()-i9,n=Q3<(t.object.skipFrames||0);return t.skipAllowed&&a&&n&&J3.length>0?(Q3++,J3):(Q3=0,new Promise(async r=>{let s=[e.shape[2]||0,e.shape[1]||0],i=fe.resizeBilinear(e,[al,al]),o=t.object.enabled?Ga==null?void 0:Ga.execute(i,["tower_0/detections"]):null;i9=ae(),J(i);let l=await Zge(o,s,t);J3=l,r(l)}))}var c0={};Ar(c0,{connected:()=>ay,kpt:()=>ty});var ty=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],ay={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Mt,u9=0,Ma={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},ny=Number.MAX_SAFE_INTEGER;async function d9(e){return ne.initial&&(Mt=null),Mt?e.debug&&K("cached model:",Mt.modelUrl):Mt=await $e(e.body.modelPath),Mt}async function Jge(e,t){let[a,n]=e.shape,r=Q(e,[n*a]),s=ga(r,0),i=(await s.data())[0];if(i>t){let o=ir(r,0),l=Su(o,a),u=(await l.data())[0],p=ve(o,a),c=(await p.data())[0];return J([r,s,o,l,p]),[u,c,i]}return J([r,s]),[0,0,i]}async function ry(e,t){if(!(Mt!=null&&Mt.executor)||!(Mt!=null&&Mt.inputs[0].shape))return[];let a=(t.body.skipTime||0)>ae()-u9,n=ny<(t.body.skipFrames||0);return t.skipAllowed&&a&&n&&Object.keys(Ma.keypoints).length>0?(ny++,[Ma]):(ny=0,new Promise(async r=>{let s=De(()=>{var m,f;let c=fe.resizeBilinear(e,[((m=Mt==null?void 0:Mt.inputs[0].shape)==null?void 0:m[2])||0,((f=Mt==null?void 0:Mt.inputs[0].shape)==null?void 0:f[1])||0],!1),d=te(c,ze.tf2);return xe(d,ze.tf1)}),i;if(t.body.enabled&&(i=Mt==null?void 0:Mt.execute(s)),u9=ae(),J(s),i){Ma.keypoints.length=0;let c=Oe(i);J(i);let d=Na(c,2);J(c);for(let h=0;h(t.body.minConfidence||0)&&Ma.keypoints.push({score:Math.round(100*g)/100,part:ty[h],positionRaw:[m/Mt.inputs[0].shape[2],f/Mt.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/Mt.inputs[0].shape[2]),Math.round(e.shape[1]*f/Mt.inputs[0].shape[1])]})}d.forEach(h=>J(h))}Ma.score=Ma.keypoints.reduce((c,d)=>d.score>c?d.score:c,0);let o=Ma.keypoints.map(c=>c.position[0]),l=Ma.keypoints.map(c=>c.position[1]);Ma.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=Ma.keypoints.map(c=>c.positionRaw[0]),p=Ma.keypoints.map(c=>c.positionRaw[1]);Ma.boxRaw=[Math.min(...u),Math.min(...p),Math.max(...u)-Math.min(...u),Math.max(...p)-Math.min(...p)];for(let[c,d]of Object.entries(ay)){let h=[];for(let m=0;my.part===d[m]),g=Ma.keypoints.find(y=>y.part===d[m+1]);f&&g&&f.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([f.position,g.position])}Ma.annotations[c]=h}r([Ma])}))}var Wu=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],h0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],m0=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],f0=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],m9=(e,t,a)=>{let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],s=e.landmarks.map(i=>[(i[0]+a[0])*t[0],(i[1]+a[1])*t[1]]);return{startPoint:n,endPoint:r,landmarks:s,confidence:e.confidence}},sy=(e,t,a)=>{let n=t.shape[1],r=t.shape[2],s=[e.startPoint[1]/n,e.startPoint[0]/r,e.endPoint[1]/n,e.endPoint[0]/r],i=fe.cropAndResize(t,[s],[0],a),o=ve(i,ze.tf255);return J(i),o},g0=(e,t)=>{let a=h0(e),n=Wu(e),r=[t*n[0]/2,t*n[1]/2];return{startPoint:[a[0]-r[0],a[1]-r[1]],endPoint:[a[0]+r[0],a[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence,size:n}},y0=e=>{let t=h0(e),a=Wu(e),n=Math.max(...a)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence,size:[Math.round(a[0]),Math.round(a[1])]}},f9=e=>{let t=e.map(n=>n[0]),a=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...a)],endPoint:[Math.max(...t),Math.max(...a)],landmarks:e}},iy=[[1,0,0],[0,1,0],[0,0,1]],Qge=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),e3e=(e,t)=>Qge(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var c9=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],nl=(e,t)=>{let a=0;for(let n=0;n{let a=[];for(let n=0;n{let a=[],n=e.length;for(let r=0;r{let a=Math.cos(e),n=Math.sin(e),r=[[a,-n,0],[n,a,0],[0,0,1]],s=c9(t[0],t[1]),i=h9(s,r),o=c9(-t[0],-t[1]);return h9(i,o)},a3e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],a=[e[0][2],e[1][2]],n=[-nl(t[0],a),-nl(t[1],a)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},n3e=(e,t)=>[nl(e,t[0]),nl(e,t[1])];function y9(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},a=[];for(let n=0;n[s[0]/r*(h[0]-r/2),s[1]/r*(h[1]-r/2),h[2]||0]),o=a&&a!==0&&Math.abs(a)>.2,l=o?g9(a,[0,0]):iy,u=o?i.map(h=>[...n3e(h,l),h[2]]):i,p=o?a3e(n):iy,c=h0(t),d=[nl(c,p[0]),nl(c,p[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2]||0)])}function A9(e,t,a,n){let r=t.landmarks.length>=V3.count?V3.symmetryLine:Zo.symmetryLine,s=0,i=iy,o;if(e&&ne.kernels.includes("rotatewithoffset"))if(s=e3e(t.landmarks[r[0]],t.landmarks[r[1]]),s&&s!==0&&Math.abs(s)>.2){let u=h0(t),p=[u[0]/a.shape[2],u[1]/a.shape[1]],c=fe.rotateWithOffset(a,s,0,[p[0],p[1]]);i=g9(-s,u),o=sy(t,c,[n,n]),J(c)}else o=sy(t,a,[n,n]);else o=sy(t,a,[n,n]);return[s,i,o]}var r3e=e=>{let t=e.map(n=>n[0]),a=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...a)+(Math.max(...a)-Math.min(...a))/2]},b9=(e,t)=>{let a=r3e(e),n=Wu(t);return{startPoint:[a[0]-n[0]/2,a[1]-n[1]/2],endPoint:[a[0]+n[0]/2,a[1]+n[1]/2]}};var v9=6,Gn,x0=null,pr=0,Bu=null,w9=()=>pr;async function k9(e){var t;return ne.initial&&(Gn=null),Gn?e.debug&&K("cached model:",Gn.modelUrl):Gn=await $e((t=e.face.detector)==null?void 0:t.modelPath),pr=Gn.executor&&Gn.inputs[0].shape?Gn.inputs[0].shape[2]:256,Bu=Ge(pr,"int32"),x0=Qn(y9(pr)),Gn}function s3e(e){if(!x0||!Bu)return yn([0,0]);let t={};t.boxStarts=Fe(e,[0,1],[-1,2]),t.centers=we(t.boxStarts,x0),t.boxSizes=Fe(e,[0,3],[-1,2]),t.boxSizesNormalized=ve(t.boxSizes,Bu),t.centersNormalized=ve(t.centers,Bu),t.halfBoxSize=ve(t.boxSizesNormalized,ze.tf2),t.starts=xe(t.centersNormalized,t.halfBoxSize),t.ends=we(t.centersNormalized,t.halfBoxSize),t.startNormalized=te(t.starts,Bu),t.endNormalized=te(t.ends,Bu);let a=Iu([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>J(t[n])),a}async function I9(e,t){var u,p,c,d,h,m,f,g,y;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let a={},n=[0,0],r=[1,1];if((p=(u=t==null?void 0:t.face)==null?void 0:u.detector)!=null&&p.square){let x=Math.max(e.shape[2],e.shape[1]);n=[Math.floor((x-e.shape[2])/2),Math.floor((x-e.shape[1])/2)],a.padded=Rn(e,[[0,0],[n[1],n[1]],[n[0],n[0]],[0,0]]),r=[e.shape[2]/x,e.shape[1]/x],n=[n[0]/pr,n[1]/pr]}else a.padded=e.clone();a.resized=fe.resizeBilinear(a.padded,[pr,pr]),a.div=ve(a.resized,ze.tf127),a.normalized=xe(a.div,ze.tf1);let s=Gn==null?void 0:Gn.execute(a.normalized);if(Array.isArray(s)&&s.length>2){let x=s.sort((A,b)=>A.size-b.size);a.concat384=lt([x[0],x[2]],2),a.concat512=lt([x[1],x[3]],2),a.concat=lt([a.concat512,a.concat384],1),a.batch=Oe(a.concat,[0])}else Array.isArray(s)?a.batch=Oe(s[0]):a.batch=Oe(s);J(s),a.boxes=s3e(a.batch),a.logits=Fe(a.batch,[0,0],[-1,1]),a.sigmoid=za(a.logits),a.scores=Oe(a.sigmoid),a.nms=await fe.nonMaxSuppressionAsync(a.boxes,a.scores,((c=t.face.detector)==null?void 0:c.maxDetected)||0,((d=t.face.detector)==null?void 0:d.iouThreshold)||0,((h=t.face.detector)==null?void 0:h.minConfidence)||0);let i=await a.nms.array(),o=[],l=await a.scores.data();for(let x=0;x(((m=t.face.detector)==null?void 0:m.minConfidence)||0)){let b={};b.bbox=Fe(a.boxes,[i[x],0],[1,-1]),b.slice=Fe(a.batch,[i[x],v9-1],[1,-1]),b.squeeze=Oe(b.slice),b.landmarks=Q(b.squeeze,[v9,-1]);let w=await b.bbox.data(),I=[w[0]*r[0]-n[0],w[1]*r[1]-n[1],w[2]*r[0]-n[0],w[3]*r[1]-n[1]],T={startPoint:[I[0],I[1]],endPoint:[I[2],I[3]],landmarks:await b.landmarks.array(),confidence:A};b.anchor=Fe(x0,[i[x],0],[1,2]);let N=await b.anchor.data(),M=m9(T,[(e.shape[2]||0)/pr,(e.shape[1]||0)/pr],N),$=g0(M,((f=t.face.detector)==null?void 0:f.scale)||1.4),E=y0($);E.size[0]>(((g=t.face.detector)==null?void 0:g.minSize)||0)&&E.size[1]>(((y=t.face.detector)==null?void 0:y.minSize)||0)&&o.push(E),Object.keys(b).forEach(S=>J(b[S]))}}return Object.keys(a).forEach(x=>J(a[x])),o}var nn,ks=0,ly=Pn.leftEyeLower0,uy=Pn.rightEyeLower0,Vu={leftBounds:[ly[0],ly[ly.length-1]],rightBounds:[uy[0],uy[uy.length-1]]},Uu={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function R9(e){var t,a;return ne.initial&&(nn=null),nn?e.debug&&K("cached model:",nn.modelUrl):nn=await $e((t=e.face.iris)==null?void 0:t.modelPath),ks=nn!=null&&nn.executor&&((a=nn.inputs)!=null&&a[0].shape)?nn.inputs[0].shape[2]:0,ks===-1&&(ks=64),nn}function A0(e,t,a,n){for(let r=0;r{let t=e[Vu.leftBounds[0]][2],a=e[Vu.rightBounds[0]][2];return t-a},C9=(e,t,a,n,r,s=!1,i=2.3)=>{let o=y0(g0(f9([e[a],e[n]]),i)),l=Wu(o),u=fe.cropAndResize(t,[[o.startPoint[1]/r,o.startPoint[0]/r,o.endPoint[1]/r,o.endPoint[0]/r]],[0],[ks,ks]);if(s&&ne.kernels.includes("flipleftright")){let p=fe.flipLeftRight(u);J(u),u=p}return{box:o,boxSize:l,crop:u}},T9=(e,t,a,n=!1)=>{let r=[];for(let s=0;s{let n=e[Pn[`${a}EyeUpper0`][Uu.upperCenter]][2],r=e[Pn[`${a}EyeLower0`][Uu.lowerCenter]][2],s=(n+r)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=r),[i[0],i[1],l]})};async function E9(e,t,a,n){var T,N;if(!(nn!=null&&nn.executor))return e;let{box:r,boxSize:s,crop:i}=C9(e,t,Vu.leftBounds[0],Vu.leftBounds[1],a,!0,((T=n.face.iris)==null?void 0:T.scale)||2.3),{box:o,boxSize:l,crop:u}=C9(e,t,Vu.rightBounds[0],Vu.rightBounds[1],a,!0,((N=n.face.iris)==null?void 0:N.scale)||2.3),p=lt([i,u]);J(i),J(u);let c=nn.execute(p);J(p);let d=await c.data();J(c);let h=d.slice(0,Uu.numCoordinates*3),{rawCoords:m,iris:f}=T9(h,r,s,!0),g=d.slice(Uu.numCoordinates*3),{rawCoords:y,iris:x}=T9(g,o,l,!1),A=i3e(e);Math.abs(A)<30?(A0(e,m,"left",null),A0(e,y,"right",null)):A<1?A0(e,m,"left",["EyeUpper0","EyeLower0"]):A0(e,y,"right",["EyeUpper0","EyeLower0"]);let b=N9(e,f,"left"),w=N9(e,x,"right");return e.concat(b).concat(w)}async function $9(e,t){var s,i,o,l,u,p,c,d,h,m;let a={lips:await((i=(s=t.filter(f=>f.size===160))==null?void 0:s[0])==null?void 0:i.data()),irisL:await((l=(o=t.filter(f=>f.size===10))==null?void 0:o[0])==null?void 0:l.data()),eyeL:await((p=(u=t.filter(f=>f.size===142))==null?void 0:u[0])==null?void 0:p.data()),irisR:await((d=(c=t.filter(f=>f.size===10))==null?void 0:c[1])==null?void 0:d.data()),eyeR:await((m=(h=t.filter(f=>f.size===142))==null?void 0:h[1])==null?void 0:m.data())};for(let f of Object.values(a))if(!f)return e;let n=Qo.reduce((f,g)=>f+=e[g][2],0)/Qo.length;for(let f=0;ff+=e[g][2],0)/el.length;for(let f=0;fae()-cr.timestamp,n=cr.skipped<(((u=t.face.detector)==null?void 0:u.skipFrames)||0);!t.skipAllowed||!a||!n||cr.boxes.length===0?(cr.boxes=await I9(e,t),cr.timestamp=ae(),cr.skipped=0):cr.skipped++;let r=[],s=[],i=0,o=Gp;for(let x=0;x[T[0]/(e.shape[2]||0),T[1]/(e.shape[1]||0),(T[2]||0)/o]);for(let T of Object.keys(Zo))I.annotations[T]=[I.mesh[Zo[T]]]}else if(!Ct)t.debug&&K("face mesh detection requested, but model is not loaded");else{if((h=t.face.attention)!=null&&h.enabled&&!ne.kernels.includes("atan2"))return t.face.attention.enabled=!1,J(I.tensor),r;let T=Ct.execute(I.tensor),M=await T.find($=>$.shape[$.shape.length-1]===1).data();if(I.faceScore=Math.round(100*M[0])/100,I.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(A.confidence=I.faceScore,t.face.mesh.keepInvalid){I.box=m0(A,e),I.boxRaw=f0(A,e),I.size=A.size,I.score=I.boxScore,I.mesh=A.landmarks,I.meshRaw=I.mesh.map($=>[$[0]/(e.shape[2]||1),$[1]/(e.shape[1]||1),($[2]||0)/o]);for(let $ of Object.keys(Zo))I.annotations[$]=[I.mesh[Zo[$]]]}}else{let $=T.find(O=>O.shape[O.shape.length-1]===1404),E=Q($,[-1,3]),S=await E.array();J(E),(f=t.face.attention)!=null&&f.enabled?S=await $9(S,T):(g=t.face.iris)!=null&&g.enabled&&(S=await E9(S,I.tensor,Gp,t)),I.mesh=x9(S,A,b,w,Gp),I.meshRaw=I.mesh.map(O=>[O[0]/(e.shape[2]||0),O[1]/(e.shape[1]||0),(O[2]||0)/o]);for(let O of Object.keys(Pn))I.annotations[O]=Pn[O].map(W=>I.mesh[W]);I.score=I.faceScore;let _={...b9(I.mesh,A),confidence:A.confidence,landmarks:A.landmarks,size:A.size};I.box=m0(_,e),I.boxRaw=f0(_,e),I.size=_.size,s.push(_)}J(T)}I.score>(((y=t.face.detector)==null?void 0:y.minConfidence)||1)?r.push(I):J(I.tensor)}return cr.boxes=s,r}async function _9(e){var t,a,n,r,s,i;return ne.initial&&(Ct=null),(t=e.face.attention)!=null&&t.enabled&&(Ct!=null&&Ct.signature)&&Object.keys(((a=Ct==null?void 0:Ct.signature)==null?void 0:a.outputs)||{}).length<6&&(Ct=null),Ct?e.debug&&K("cached model:",Ct.modelUrl):(n=e.face.attention)!=null&&n.enabled?Ct=await $e(e.face.attention.modelPath):Ct=await $e((r=e.face.mesh)==null?void 0:r.modelPath),Gp=Ct.executor&&((s=Ct==null?void 0:Ct.inputs)!=null&&s[0].shape)?(i=Ct==null?void 0:Ct.inputs)==null?void 0:i[0].shape[2]:256,Ct}var F9=Jo,D9=Vp;var cy=[],sa,b0=[],O9=0,z9=0,py=Number.MAX_SAFE_INTEGER,hy=!1;async function L9(e){var t,a,n;return ne.initial&&(sa=null),sa?e.debug&&K("cached model:",sa.modelUrl):(sa=await $e((t=e.face.emotion)==null?void 0:t.modelPath),hy=((n=(a=sa==null?void 0:sa.inputs)==null?void 0:a[0].shape)==null?void 0:n[3])===3,hy?cy=["angry","disgust","fear","happy","neutral","sad","surprise"]:cy=["angry","disgust","fear","happy","sad","surprise","neutral"]),sa}async function my(e,t,a,n){var i,o;if(!sa)return[];let r=py<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>ae()-z9;return t.skipAllowed&&s&&r&&O9===n&&b0[a]&&b0[a].length>0?(py++,b0[a]):(py=0,new Promise(async l=>{var p,c,d;let u=[];if((p=t.face.emotion)!=null&&p.enabled){let h={},m=sa!=null&&sa.inputs[0].shape?sa.inputs[0].shape[2]:0;if(((c=t.face.emotion)==null?void 0:c.crop)>0){let g=(d=t.face.emotion)==null?void 0:d.crop,y=[[g,g,1-g,1-g]];h.resize=fe.cropAndResize(e,y,[0],[m,m])}else h.resize=fe.resizeBilinear(e,[m,m],!1);hy?(h.mul=te(h.resize,255),h.normalize=xe(h.mul,[103.939,116.779,123.68]),h.emotion=sa==null?void 0:sa.execute(h.normalize)):(h.channels=te(h.resize,ze.rgb),h.grayscale=ot(h.channels,3,!0),h.grayscaleSub=xe(h.grayscale,ze.tf05),h.grayscaleMul=te(h.grayscaleSub,ze.tf2),h.emotion=sa==null?void 0:sa.execute(h.grayscaleMul)),z9=ae();let f=await h.emotion.data();for(let g=0;g(t.face.emotion.minConfidence||0)&&u.push({score:Math.min(.99,Math.trunc(100*f[g])/100),emotion:cy[g]});u.sort((g,y)=>y.score-g.score),Object.keys(h).forEach(g=>J(h[g]))}b0[a]=u,O9=n,l(u)}))}var ia,Is=[],B9=0,V9=0,fy=Number.MAX_SAFE_INTEGER;async function U9(e){var t;return ne.initial&&(ia=null),ia?e.debug&&K("cached model:",ia.modelUrl):ia=await $e((t=e.face.description)==null?void 0:t.modelPath),ia}function l3e(e,t){var s,i;let a=e.image||e.tensor||e;if(!(ia!=null&&ia.inputs[0].shape))return a;let n;if(((s=t.face.description)==null?void 0:s.crop)>0){let o=(i=t.face.description)==null?void 0:i.crop,l=[[o,o,1-o,1-o]];n=fe.cropAndResize(a,l,[0],[ia.inputs[0].shape[2],ia.inputs[0].shape[1]])}else n=fe.resizeBilinear(a,[ia.inputs[0].shape[2],ia.inputs[0].shape[1]],!1);let r=te(n,ze.tf255);return J(n),r}async function gy(e,t,a,n){var o,l,u,p;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(ia!=null&&ia.executor))return r;let s=fy<(((o=t.face.description)==null?void 0:o.skipFrames)||0),i=(((l=t.face.description)==null?void 0:l.skipTime)||0)>ae()-B9;return t.skipAllowed&&s&&i&&V9===n&&((u=Is==null?void 0:Is[a])==null?void 0:u.age)>0&&((p=Is==null?void 0:Is[a])==null?void 0:p.genderScore)>0?(fy++,Is[a]):(fy=0,new Promise(async c=>{var d;if((d=t.face.description)!=null&&d.enabled){let h=l3e(e,t),m=ia==null?void 0:ia.execute(h);B9=ae(),J(h);let g=await m.find(N=>N.shape[1]===1).data(),y=Math.trunc(200*Math.abs(g[0]-.5))/100;y>(t.face.description.minConfidence||0)&&(r.gender=g[0]<=.5?"female":"male",r.genderScore=Math.min(.99,y));let x=ir(m.find(N=>N.shape[1]===100),1),A=(await x.data())[0];J(x);let w=await m.find(N=>N.shape[1]===100).data();r.age=Math.round(w[A-1]>w[A+1]?10*A-100*w[A-1]:10*A+100*w[A+1])/10,(Number.isNaN(g[0])||Number.isNaN(w[0]))&&K("faceres error:",{model:ia,result:m});let I=m.find(N=>N.shape[1]===1024),T=I?await I.data():[];r.descriptor=Array.from(T),m.forEach(N=>J(N))}Is[a]=r,V9=n,c(r)}))}var Gu=.1,yy=.5;function u3e(e,t,a){let n=!1,r=a.length-1;for(let s=0;st!=a[r].y>t&&e<(a[r].x-a[s].x)*(t-a[s].y)/(a[r].y-a[s].y)+a[s].x&&(n=!n);return n}async function H9(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,a=e.tensor.shape[1]||0,n=await e.tensor.buffer(),r=[];for(let i of Pn.silhouette)r.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});Gu&&Gu>0&&(r=r.map(i=>({x:i.x>.5?i.x+Gu:i.x-Gu,y:i.y>.5?i.y+Gu:i.y-Gu})));for(let i=0;iae()-q9,s=xy<(((o=t.face.antispoof)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&r&&s&&j9===n&&v0[a]?(xy++,v0[a]):(xy=0,new Promise(async l=>{let u=fe.resizeBilinear(e,[oa!=null&&oa.inputs[0].shape?oa.inputs[0].shape[2]:0,oa!=null&&oa.inputs[0].shape?oa.inputs[0].shape[1]:0],!1),p=oa==null?void 0:oa.execute(u),c=(await p.data())[0];v0[a]=Math.round(100*c)/100,j9=n,q9=ae(),J([u,p]),l(v0[a])}))}var la,w0=[],by=Number.MAX_SAFE_INTEGER,Y9=0,Z9=0;async function J9(e){var t;return ne.initial&&(la=null),la?e.debug&&K("cached model:",la.modelUrl):la=await $e((t=e.face.liveness)==null?void 0:t.modelPath),la}async function vy(e,t,a,n){var i,o;if(!(la!=null&&la.executor))return 0;let r=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>ae()-Z9,s=by<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&r&&s&&Y9===n&&w0[a]?(by++,w0[a]):(by=0,new Promise(async l=>{let u=fe.resizeBilinear(e,[la!=null&&la.inputs[0].shape?la.inputs[0].shape[2]:0,la!=null&&la.inputs[0].shape?la.inputs[0].shape[1]:0],!1),p=la==null?void 0:la.execute(u),c=(await p.data())[0];w0[a]=Math.round(100*c)/100,Y9=n,Z9=ae(),J([u,p]),l(w0[a])}))}var _n,wy=[],p3e=["white","black","asian","indian","other"],c3e=[15,23,28,35.5,45.5,55.5,65],eI=0,tI=0,ky=Number.MAX_SAFE_INTEGER;async function aI(e){var t;return ne.initial&&(_n=null),_n?e.debug&&K("cached model:",_n.modelUrl):_n=await $e((t=e.face.gear)==null?void 0:t.modelPath),_n}async function Iy(e,t,a,n){var i,o;if(!_n)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=ky<(((i=t.face.gear)==null?void 0:i.skipFrames)||0),s=(((o=t.face.gear)==null?void 0:o.skipTime)||0)>ae()-tI;return t.skipAllowed&&s&&r&&eI===n&&wy[a]?(ky++,wy[a]):(ky=0,new Promise(async l=>{var y,x,A,b;if(!(_n!=null&&_n.inputs[0].shape))return;let u={},p=[[0,.1,.9,.9]];if(((y=t.face.gear)==null?void 0:y.crop)>0){let w=(x=t.face.gear)==null?void 0:x.crop;p=[[w,w,1-w,1-w]]}u.resize=fe.cropAndResize(e,p,[0],[_n.inputs[0].shape[2],_n.inputs[0].shape[1]]);let c={age:0,gender:"unknown",genderScore:0,race:[]};(A=t.face.gear)!=null&&A.enabled&&([u.age,u.gender,u.race]=_n.execute(u.resize,["age_output","gender_output","race_output"]));let d=await u.gender.data();c.gender=d[0]>d[1]?"male":"female",c.genderScore=Math.round(100*(d[0]>d[1]?d[0]:d[1]))/100;let h=await u.race.data();for(let w=0;w(((b=t.face.gear)==null?void 0:b.minConfidence)||.2)&&c.race.push({score:Math.round(100*h[w])/100,race:p3e[w]});c.race.sort((w,I)=>I.score-w.score);let f=Array.from(await u.age.data()).map((w,I)=>[c3e[I],w]).sort((w,I)=>I[1]-w[1]),g=f[0][0];for(let w=1;wJ(u[w])),wy[a]=c,eI=n,tI=ae(),l(c)}))}var $a,k0=[],rI=0,sI=0,Sy=Number.MAX_SAFE_INTEGER;async function iI(e){return ne.initial&&($a=null),$a?e.debug&&K("cached model:",$a.modelUrl):$a=await $e(e.face.ssrnet.modelPathAge),$a}async function Cy(e,t,a,n){var i,o,l,u;if(!$a)return{age:0};let r=Sy<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>ae()-sI;return t.skipAllowed&&r&&s&&rI===n&&((l=k0[a])!=null&&l.age)&&((u=k0[a])==null?void 0:u.age)>0?(Sy++,k0[a]):(Sy=0,new Promise(async p=>{var h,m,f;if(!($a!=null&&$a.inputs)||!$a.inputs[0]||!$a.inputs[0].shape)return;let c={};if(((h=t.face.ssrnet)==null?void 0:h.crop)>0){let g=(m=t.face.ssrnet)==null?void 0:m.crop,y=[[g,g,1-g,1-g]];c.resize=fe.cropAndResize(e,y,[0],[$a.inputs[0].shape[2],$a.inputs[0].shape[1]])}else c.resize=fe.resizeBilinear(e,[$a.inputs[0].shape[2],$a.inputs[0].shape[1]],!1);c.enhance=te(c.resize,ze.tf255);let d={age:0};if((f=t.face.ssrnet)!=null&&f.enabled&&(c.age=$a.execute(c.enhance)),c.age){let g=await c.age.data();d.age=Math.trunc(10*g[0])/10}Object.keys(c).forEach(g=>J(c[g])),k0[a]=d,rI=n,sI=ae(),p(d)}))}var xa,I0=[],lI=0,uI=0,Ty=Number.MAX_SAFE_INTEGER,Ny=[.2989,.587,.114];async function dI(e){var t;return ne.initial&&(xa=null),xa?e.debug&&K("cached model:",xa.modelUrl):xa=await $e((t=e.face.ssrnet)==null?void 0:t.modelPathGender),xa}async function Ry(e,t,a,n){var i,o,l,u;if(!xa)return{gender:"unknown",genderScore:0};let r=Ty<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>ae()-uI;return t.skipAllowed&&r&&s&&lI===n&&((l=I0[a])!=null&&l.gender)&&((u=I0[a])==null?void 0:u.genderScore)>0?(Ty++,I0[a]):(Ty=0,new Promise(async p=>{var m,f,g;if(!(xa!=null&&xa.inputs[0].shape))return;let c={};if(((m=t.face.ssrnet)==null?void 0:m.crop)>0){let y=(f=t.face.ssrnet)==null?void 0:f.crop,x=[[y,y,1-y,1-y]];c.resize=fe.cropAndResize(e,x,[0],[xa.inputs[0].shape[2],xa.inputs[0].shape[1]])}else c.resize=fe.resizeBilinear(e,[xa.inputs[0].shape[2],xa.inputs[0].shape[1]],!1);c.enhance=De(()=>{var x,A;let y;if(((A=(x=xa==null?void 0:xa.inputs)==null?void 0:x[0].shape)==null?void 0:A[3])===1){let[b,w,I]=Sa(c.resize,3,3),T=te(b,Ny[0]),N=te(w,Ny[1]),M=te(I,Ny[2]),$=gh([T,N,M]);y=te(xe($,ze.tf05),2)}else y=te(xe(c.resize,ze.tf05),2);return y});let d={gender:"unknown",genderScore:0};(g=t.face.ssrnet)!=null&&g.enabled&&(c.gender=xa.execute(c.enhance));let h=await c.gender.data();d.gender=h[0]>h[1]?"female":"male",d.genderScore=h[0]>h[1]?Math.trunc(100*h[0])/100:Math.trunc(100*h[1])/100,Object.keys(c).forEach(y=>J(c[y])),I0[a]=d,lI=n,uI=ae(),p(d)}))}var rn,Ey=[],cI=0,hI=0,mI=Number.MAX_SAFE_INTEGER;async function fI(e){var t;return ne.initial&&(rn=null),rn?e.debug&&K("cached model:",rn.modelUrl):rn=await $e((t=e.face.mobilefacenet)==null?void 0:t.modelPath),rn}async function My(e,t,a,n){var i,o;if(!(rn!=null&&rn.executor))return[];let r=mI<(((i=t.face.mobilefacenet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.mobilefacenet)==null?void 0:o.skipTime)||0)>ae()-hI;return t.skipAllowed&&s&&r&&cI===n&&Ey[a]?(mI++,Ey[a]):new Promise(async l=>{var p;let u=[];if((p=t.face.mobilefacenet)!=null&&p.enabled&&(rn!=null&&rn.inputs[0].shape)){let c={};c.crop=fe.resizeBilinear(e,[rn.inputs[0].shape[2],rn.inputs[0].shape[1]],!1),c.data=rn.execute(c.crop);let d=await c.data.data();u=Array.from(d),Object.keys(c).forEach(h=>J(c[h]))}Ey[a]=u,cI=n,hI=ae(),l(u)})}var sn,$y=[],yI=0,xI=0,AI=Number.MAX_SAFE_INTEGER;async function bI(e){return ne.initial&&(sn=null),sn?e.debug&&K("cached model:",sn.modelUrl):sn=await $e(e.face.insightface.modelPath),sn}async function Py(e,t,a,n){var i,o;if(!(sn!=null&&sn.executor))return[];let r=AI<(((i=t.face.insightface)==null?void 0:i.skipFrames)||0),s=(((o=t.face.insightface)==null?void 0:o.skipTime)||0)>ae()-xI;return t.skipAllowed&&s&&r&&yI===n&&$y[a]?(AI++,$y[a]):new Promise(async l=>{var p;let u=[];if((p=t.face.insightface)!=null&&p.enabled&&(sn!=null&&sn.inputs[0].shape)){let c={};c.crop=fe.resizeBilinear(e,[sn.inputs[0].shape[2],sn.inputs[0].shape[1]],!1),c.data=sn.execute(c.crop);let d=await c.data.data();u=Array.from(d),Object.keys(c).forEach(h=>J(c[h]))}$y[a]=u,yI=n,xI=ae(),l(u)})}var h3e=e=>{let t=(c,d)=>Math.atan2(c[1]-d[1],c[0]-d[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let 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y=Math.abs(e[i].mesh[145][1]-e[i].annotations.rightEyeIris[0][1])/e[i].box[3],x=Math.abs(e[i].mesh[374][1]-e[i].annotations.leftEyeIris[0][1])/e[i].box[3];(x<.01||y<.01||x>.022||y>.022)&&(h=!1),(x<.01||y<.01)&&t.push({iris:i,gesture:"looking down"}),(x>.022||y>.022)&&t.push({iris:i,gesture:"looking up"}),h&&t.push({iris:i,gesture:"looking center"})}return t},FI=e=>{if(!e)return[];let t=[];for(let a=0;a0){let r=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:a,gesture:`${r.name} forward`});let s=n.reduce((i,o)=>i.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:a,endPoint:n,palmLandmarks:r,confidence:e.confidence}}function T0(e,t=1.5){let a=Hp(e),n=C0(e),r=[t*n[0]/2,t*n[1]/2],s=[a[0]-r[0],a[1]-r[1]],i=[a[0]+r[0],a[1]+r[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function N0(e){let t=Hp(e),a=C0(e),r=Math.max(...a)/2,s=[t[0]-r,t[1]-r],i=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function k3e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function WI(e,t){let a=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return k3e(a)}var DI=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Es(e,t){let a=0;for(let n=0;n[i.x,i.y]),this.anchorsTensor=Qn(this.anchors),this.inputSize=((s=(r=(n=(a=this==null?void 0:this.model)==null?void 0:a.inputs)==null?void 0:n[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=Bt([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=Bt([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let a={};a.boxOffsets=Fe(t,[0,0],[-1,2]),a.boxSizes=Fe(t,[0,2],[-1,2]),a.div=ve(a.boxOffsets,this.inputSizeTensor),a.boxCenterPoints=we(a.div,this.anchorsTensor),a.halfBoxSizes=ve(a.boxSizes,this.doubleInputSizeTensor),a.sub=xe(a.boxCenterPoints,a.halfBoxSizes),a.startPoints=te(a.sub,this.inputSizeTensor),a.add=we(a.boxCenterPoints,a.halfBoxSizes),a.endPoints=te(a.add,this.inputSizeTensor);let n=Iu([a.startPoints,a.endPoints],1);return Object.keys(a).forEach(r=>J(a[r])),n}normalizeLandmarks(t,a){let n={};n.reshape=Q(t,[-1,7,2]),n.div=ve(n.reshape,this.inputSizeTensor),n.landmarks=we(n.div,this.anchors[a]?this.anchors[a]:0);let r=te(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>J(n[s])),r}async predict(t,a){var o;let n={};n.resize=fe.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=ve(n.resize,ze.tf127),n.image=xe(n.div,ze.tf1),n.batched=this.model.execute(n.image),n.predictions=Oe(n.batched),n.slice=Fe(n.predictions,[0,0],[-1,1]),n.sigmoid=za(n.slice),n.scores=Oe(n.sigmoid);let r=await n.scores.data();n.boxes=Fe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await fe.nonMaxSuppressionAsync(n.norm,n.scores,3*(((o=a.hand)==null?void 0:o.maxDetected)||1),a.hand.iouThreshold,a.hand.minConfidence);let s=await n.nms.array(),i=[];for(let l of s){let u={};u.box=Fe(n.norm,[l,0],[1,-1]),u.slice=Fe(n.predictions,[l,5],[1,14]),u.norm=this.normalizeLandmarks(u.slice,l),u.palmLandmarks=Q(u.norm,[-1,2]);let p=await u.box.data(),c=p.slice(0,2),d=p.slice(2,4),h=await u.palmLandmarks.array(),m={startPoint:c,endPoint:d,palmLandmarks:h,confidence:r[l]},f=LI(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);i.push(f),Object.keys(u).forEach(g=>J(u[g]))}return Object.keys(n).forEach(l=>J(n[l])),i}};var T3e=5,GI=1.65,HI=[0,5,9,13,17,1,2],N3e=0,R3e=2,jI=0,E0=class{constructor(t,a){he(this,"handDetector");he(this,"handPoseModel");he(this,"inputSize");he(this,"storedBoxes");he(this,"skipped");he(this,"detectedHands");var n,r,s;this.handDetector=t,this.handPoseModel=a,this.inputSize=((s=(r=(n=this.handPoseModel)==null?void 0:n.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let a=t.map(i=>i[0]),n=t.map(i=>i[1]),r=[Math.min(...a),Math.min(...n)],s=[Math.max(...a),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,a){let n=t.map(s=>zy([...s,1],a)),r=this.calculateLandmarksBoundingBox(n);return T0(N0(r),T3e)}getBoxForHandLandmarks(t){let a=this.calculateLandmarksBoundingBox(t),n=T0(N0(a),GI);n.palmLandmarks=[];for(let r=0;r[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=Oy(n,[0,0]),u=o.map(h=>[...zy(h,l),h[2]]),p=BI(r),c=[...Hp(a),1],d=[Es(c,p[0]),Es(c,p[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2])])}async estimateHands(t,a){let n=!1,r,s=(a.hand.skipTime||0)>ae()-jI,i=this.skipped<(a.hand.skipFrames||0);a.skipAllowed&&s&&i?this.skipped++:(r=await this.handDetector.predict(t,a),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==a.hand.maxDetected||!a.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l=a.hand.minConfidence/4){let w=Q(A,[-1,3]),I=await w.array();J(A),J(w);let T=this.transformRawCoords(I,f,p,m),N=this.getBoxForHandLandmarks(T);this.storedBoxes[l]={...N,confidence:b};let M={landmarks:T,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:N.startPoint,bottomRight:N.endPoint}};o.push(M)}else this.storedBoxes[l]=null;J(A)}else{let p=T0(N0(u),GI),c={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:p.startPoint,bottomRight:p.endPoint},landmarks:[]};o.push(c)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>a.hand.maxDetected&&(o.length=a.hand.maxDetected),o}};var qI={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},ol,ll,Ly;function M3e(){let e=ol?new R0(ol):void 0;e&&ll&&(Ly=new E0(e,ll))}async function Wy(e,t){Ly||M3e();let a=await Ly.estimateHands(e,t);if(!a)return[];let n=[];for(let r=0;ra[r].landmarks[c]);let i=a[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let p of i)p[0]o[2]&&(o[2]=p[0]),p[1]>o[3]&&(o[3]=p[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=a[r].box?[Math.trunc(Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.max(0,a[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,a[r].box.bottomRight[0])-Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,a[r].box.bottomRight[1])-Math.max(0,a[r].box.topLeft[1]))]:[0,0,0,0],l=[a[r].box.topLeft[0]/(e.shape[2]||0),a[r].box.topLeft[1]/(e.shape[1]||0),(a[r].box.bottomRight[0]-a[r].box.topLeft[0])/(e.shape[2]||0),(a[r].box.bottomRight[1]-a[r].box.topLeft[1])/(e.shape[1]||0)];let u=S0(i);n.push({id:r,score:Math.round(100*a[r].confidence)/100,boxScore:Math.round(100*a[r].boxConfidence)/100,fingerScore:Math.round(100*a[r].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function XI(e){var t;return ne.initial&&(ol=null),ol?e.debug&&K("cached model:",ol.modelUrl):ol=await $e((t=e.hand.detector)==null?void 0:t.modelPath),ol}async function KI(e){var t;return ne.initial&&(ll=null),ll?e.debug&&K("cached model:",ll.modelUrl):ll=await $e((t=e.hand.skeleton)==null?void 0:t.modelPath),ll}var Ot=[null,null],$3e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ms=[[0,0],[0,0]],P3e=["hand","fist","pinch","point","face","tip","pinchtip"],ZI=4,JI=1.6,_3e=512,F3e=1.4,M0=Number.MAX_SAFE_INTEGER,By=0,Dr=[0,0],Dt={boxes:[],hands:[]},QI={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function eS(e){var t;if(ne.initial&&(Ot[0]=null),Ot[0])e.debug&&K("cached model:",Ot[0].modelUrl);else{e0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Ot[0]=await $e((t=e.hand.detector)==null?void 0:t.modelPath);let a=Ot[0].executor?Object.values(Ot[0].modelSignature.inputs):void 0;Ms[0][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Ms[0][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Ot[0]}async function tS(e){var t;if(ne.initial&&(Ot[1]=null),Ot[1])e.debug&&K("cached model:",Ot[1].modelUrl);else{Ot[1]=await $e((t=e.hand.skeleton)==null?void 0:t.modelPath);let a=Ot[1].executor?Object.values(Ot[1].modelSignature.inputs):void 0;Ms[1][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Ms[1][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0}return Ot[1]}async function D3e(e,t){let a=[];if(!e||!Ot[0])return a;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,_3e),i=Math.round(s*r/8)*8;n.resize=fe.resizeBilinear(e,[s,i]),n.cast=Ue(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await Ot[0].executeAsync(n.cast,$3e),n.boxes=Oe(n.rawBoxes,[0,2]),n.scores=Oe(n.rawScores,[0]);let o=Na(n.scores,1);J(o[ZI]),o.splice(ZI,1),n.filtered=ha(o,1),J(o),n.max=ga(n.filtered,1),n.argmax=ir(n.filtered,1);let l=0;n.nms=await fe.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),p=await n.max.data(),c=await n.argmax.data();for(let d of Array.from(u)){let h=Fe(n.boxes,d,1),m=await h.data();J(h);let f=[m[1],m[0],m[3]-m[1],m[2]-m[0]],g=u0(f,F3e),y=[Math.trunc(f[0]*Dr[0]),Math.trunc(f[1]*Dr[1]),Math.trunc(f[2]*Dr[0]),Math.trunc(f[3]*Dr[1])],x=p[d],A=P3e[c[d]],b={id:l++,score:x,box:y,boxRaw:g,label:A};a.push(b)}return Object.keys(n).forEach(d=>J(n[d])),a.sort((d,h)=>h.score-d.score),a.length>(t.hand.maxDetected||1)&&(a.length=t.hand.maxDetected||1),a}async function Vy(e,t,a){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Ot[1]&&a.hand.landmarks&&t.score>(a.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=fe.cropAndResize(e,[s],[0],[Ms[1][0],Ms[1][1]],"bilinear"),r.div=ve(r.crop,ze.tf255),[r.score,r.keypoints]=Ot[1].execute(r.div,["Identity_1","Identity"]);let i=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(a.hand.minConfidence||0)){n.fingerScore=o,r.reshaped=Q(r.keypoints,[-1,3]);let p=(await r.reshaped.array()).map(c=>[c[0]/Ms[1][1],c[1]/Ms[1][0],c[2]||0]).map(c=>[c[0]*t.boxRaw[2],c[1]*t.boxRaw[3],c[2]||0]);n.keypoints=p.map(c=>[Dr[0]*(c[0]+t.boxRaw[0]),Dr[1]*(c[1]+t.boxRaw[1]),c[2]||0]),n.landmarks=S0(n.keypoints);for(let c of Object.keys(QI))n.annotations[c]=QI[c].map(d=>n.landmarks&&n.keypoints[d]?n.keypoints[d]:null)}Object.keys(r).forEach(l=>J(r[l]))}return n}async function Uy(e,t){var r,s;if(!((r=Ot[0])!=null&&r.executor)||!((s=Ot[1])!=null&&s.executor)||!Ot[0].inputs[0].shape||!Ot[1].inputs[0].shape)return[];Dr=[e.shape[2]||0,e.shape[1]||0],M0++;let a=(t.hand.skipTime||0)>ae()-By,n=M0<(t.hand.skipFrames||0);return t.skipAllowed&&a&&n?Dt.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>ae()-By,l=M0<3*(t.hand.skipFrames||0);t.skipAllowed&&Dt.hands.length===t.hand.maxDetected?Dt.hands=await Promise.all(Dt.boxes.map(p=>Vy(e,p,t))):t.skipAllowed&&o&&l&&Dt.hands.length>0?Dt.hands=await Promise.all(Dt.boxes.map(p=>Vy(e,p,t))):(Dt.boxes=await D3e(e,t),By=ae(),Dt.hands=await Promise.all(Dt.boxes.map(p=>Vy(e,p,t))),M0=0);let u=[...Dt.boxes];if(Dt.boxes.length=0,t.cacheSensitivity>0)for(let p=0;p.05&&c.box[3]/(e.shape[1]||1)>.05&&Dt.hands[p].fingerScore&&Dt.hands[p].fingerScore>(t.hand.minConfidence||0)){let d=u0(c.box,JI),h=u0(c.boxRaw,JI);Dt.boxes.push({...u[p],box:d,boxRaw:h})}}for(let p=0;p({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var jp={};Ar(jp,{connected:()=>P0,horizontal:()=>Gy,kpt:()=>$0,relative:()=>jy,vertical:()=>Hy});var $0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Gy=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Hy=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],jy=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],P0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Ae=hr(),qy=0;function nS(e,t){var i,o,l,u,p,c,d,h,m,f,g,y,x,A,b,w,I,T,N,M,$,E,S,_,O,W;let a=ae();if(!e)return hr();let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(Ae.canvas=e.canvas),e.error&&(Ae.error=e.error),!Ae.body||e.body.length!==Ae.body.length)Ae.body=JSON.parse(JSON.stringify(e.body));else for(let P=0;P((r-1)*Ae.body[P].box[X]+Z)/r),G=e.body[P].boxRaw.map((Z,X)=>((r-1)*Ae.body[P].boxRaw[X]+Z)/r),q=e.body[P].keypoints.map((Z,X)=>{var re,ee,ge,ie,be,Ce,Re,Le,qe;return{score:Z.score,part:Z.part,position:[Ae.body[P].keypoints[X]?((r-1)*(Ae.body[P].keypoints[X].position[0]||0)+(Z.position[0]||0))/r:Z.position[0],Ae.body[P].keypoints[X]?((r-1)*(Ae.body[P].keypoints[X].position[1]||0)+(Z.position[1]||0))/r:Z.position[1],Ae.body[P].keypoints[X]?((r-1)*(Ae.body[P].keypoints[X].position[2]||0)+(Z.position[2]||0))/r:Z.position[2]],positionRaw:[Ae.body[P].keypoints[X]?((r-1)*(Ae.body[P].keypoints[X].positionRaw[0]||0)+(Z.positionRaw[0]||0))/r:Z.positionRaw[0],Ae.body[P].keypoints[X]?((r-1)*(Ae.body[P].keypoints[X].positionRaw[1]||0)+(Z.positionRaw[1]||0))/r:Z.positionRaw[1],Ae.body[P].keypoints[X]?((r-1)*(Ae.body[P].keypoints[X].positionRaw[2]||0)+(Z.positionRaw[2]||0))/r:Z.positionRaw[2]],distance:[Ae.body[P].keypoints[X]?((r-1)*(((re=Ae.body[P].keypoints[X].distance)==null?void 0:re[0])||0)+(((ee=Z.distance)==null?void 0:ee[0])||0))/r:(ge=Z.distance)==null?void 0:ge[0],Ae.body[P].keypoints[X]?((r-1)*(((ie=Ae.body[P].keypoints[X].distance)==null?void 0:ie[1])||0)+(((be=Z.distance)==null?void 0:be[1])||0))/r:(Ce=Z.distance)==null?void 0:Ce[1],Ae.body[P].keypoints[X]?((r-1)*(((Re=Ae.body[P].keypoints[X].distance)==null?void 0:Re[2])||0)+(((Le=Z.distance)==null?void 0:Le[2])||0))/r:(qe=Z.distance)==null?void 0:qe[2]]}}),H={},V={connected:{}};(i=t.body.modelPath)!=null&&i.includes("efficientpose")?V=c0:(o=t.body.modelPath)!=null&&o.includes("blazepose")?V=o0:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(V=jp);for(let[Z,X]of Object.entries(V.connected)){let re=[];for(let ee=0;eebe.part===X[ee]),ie=q.find(be=>be.part===X[ee+1]);ge&&ie&&re.push([ge.position,ie.position])}H[Z]=re}Ae.body[P]={...e.body[P],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!Ae.hand||e.hand.length!==Ae.hand.length)Ae.hand=JSON.parse(JSON.stringify(e.hand));else for(let P=0;P((r-1)*Ae.hand[P].box[Z]+V)/r),G=e.hand[P].boxRaw.map((V,Z)=>((r-1)*Ae.hand[P].boxRaw[Z]+V)/r);Ae.hand[P].keypoints.length!==e.hand[P].keypoints.length&&(Ae.hand[P].keypoints=e.hand[P].keypoints);let q=e.hand[P].keypoints&&e.hand[P].keypoints.length>0?e.hand[P].keypoints.map((V,Z)=>V.map((X,re)=>((r-1)*(Ae.hand[P].keypoints[Z][re]||1)+(X||0))/r)):[],H={};if(Object.keys(Ae.hand[P].annotations).length!==Object.keys(e.hand[P].annotations).length)Ae.hand[P].annotations=e.hand[P].annotations,H=Ae.hand[P].annotations;else if(e.hand[P].annotations)for(let V of Object.keys(e.hand[P].annotations))H[V]=(c=(p=(u=e.hand[P])==null?void 0:u.annotations)==null?void 0:p[V])!=null&&c[0]?e.hand[P].annotations[V].map((Z,X)=>Z.map((re,ee)=>((r-1)*Ae.hand[P].annotations[V][X][ee]+re)/r)):null;Ae.hand[P]={...e.hand[P],box:U,boxRaw:G,keypoints:q,annotations:H}}if(!Ae.face||e.face.length!==Ae.face.length)Ae.face=JSON.parse(JSON.stringify(e.face));else for(let P=0;P((r-1)*Ae.face[P].box[V]+H)/r),G=e.face[P].boxRaw.map((H,V)=>((r-1)*Ae.face[P].boxRaw[V]+H)/r),q=e.face[P].annotations;if(Object.keys(Ae.face[P].annotations).length!==Object.keys(e.face[P].annotations).length)Ae.face[P].annotations=e.face[P].annotations,q=Ae.face[P].annotations;else if(e.face[P].annotations)for(let H of Object.keys(e.face[P].annotations))q[H]=(m=(h=(d=e.face[P])==null?void 0:d.annotations)==null?void 0:h[H])!=null&&m[0]?e.face[P].annotations[H].map((V,Z)=>V.map((X,re)=>((r-1)*Ae.face[P].annotations[H][Z][re]+X)/r)):null;if(e.face[P].rotation){let H={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};H.matrix=(f=e.face[P].rotation)==null?void 0:f.matrix,H.angle={roll:((r-1)*(((y=(g=Ae.face[P].rotation)==null?void 0:g.angle)==null?void 0:y.roll)||0)+(((A=(x=e.face[P].rotation)==null?void 0:x.angle)==null?void 0:A.roll)||0))/r,yaw:((r-1)*(((w=(b=Ae.face[P].rotation)==null?void 0:b.angle)==null?void 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c=[n[p][1],n[p][0]];r.push({score:Math.round(100*s)/100,part:$0[p],positionRaw:c,position:[Math.round((a.shape[2]||0)*c[0]),Math.round((a.shape[1]||0)*c[1])]})}s=r.reduce((p,c)=>c.score>p?c.score:p,0);let i=[],o=vs(r.map(p=>p.position),[a.shape[2],a.shape[1]]),l={};for(let[p,c]of Object.entries(P0)){let d=[];for(let h=0;hg.part===c[h]),f=r.find(g=>g.part===c[h+1]);m&&f&&m.score>(t.body.minConfidence||0)&&f.score>(t.body.minConfidence||0)&&d.push([m.position,f.position])}l[p]=d}let u={id:0,score:s,box:o.box,boxRaw:o.boxRaw,keypoints:r,annotations:l};return Yy(u),i.push(u),i}function V3e(e,t,a){let n=[];for(let r=0;rt.body.minConfidence){let o=[];for(let d=0;d<17;d++){let h=s[3*d+2];if(h>t.body.minConfidence){let m=[s[3*d+1],s[3*d+0]];o.push({part:$0[d],score:Math.round(100*h)/100,positionRaw:m,position:[Math.round((a.shape[2]||0)*m[0]),Math.round((a.shape[1]||0)*m[1])]})}}let 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r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await jS(n.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("meet")&&(r=await IS(n.tensor,this.config)),(o=this.config.segmentation.modelPath)!=null&&o.includes("selfie")&&(r=await XS(n.tensor,this.config)),J(n.tensor),r}compare(t,a){return o9(this.config,t,a)}async init(){await oc(this,!0),await this.tf.ready(),ny()}async load(t){this.state="load";let a=ae(),n=Object.values(this.models.models).filter(i=>i).length;t&&(this.config=Et(this.config,t)),this.env.initial&&(await oc(this,!1)||K("error: backend check failed"),await tl(),this.env.browser&&(this.config.debug&&K("configuration:",this.config),this.config.debug&&K("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&K("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(i=>i).length!==n&&(this.models.validate(),this.emit("load"));let s=Math.trunc(ae()-a);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return kS(t,this.config)}async warmup(t){let a=ae(),n=await ZS(this,t),r=ae();return this.performance.warmup=Math.trunc(r-a),n}async profile(t,a){let n=await this.tf.profile(()=>this.detect(t,a)),r={},s=0;for(let o of n.kernels){let l=Number(o.kernelTimeMs)||0;r[o.name]?r[o.name]+=l:r[o.name]=l,s+=l}let i=[];Object.entries(r).forEach(o=>i.push({kernel:o[0],time:o[1],perc:0}));for(let o of i)o.perc=Math.round(1e3*o.time/s)/1e3,o.time=Math.round(1e3*o.time)/1e3;return i.sort((o,l)=>l.time-o.time),i.length=20,i}async detect(t,a){return this.state="detect",new Promise(async n=>{var g,y,x,A,b,w,I,T,N,M,$,E,S,_,O,W,P,U,G,q,H;this.state="config";let r;this.config=Et(this.config,a),this.state="check";let s=qa(this,dm).call(this,t);s&&(K(s,t),this.emit("error"),n(hr(s)));let i=ae();await this.load(),r=ae(),this.state="image";let o=await y0(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(ae()-r):Math.trunc(ae()-r),this.analyze("Get Image:"),!o.tensor){this.config.debug&&K("could not convert input to tensor"),this.emit("error"),n(hr("could not convert input to tensor"));return}this.emit("image"),r=ae(),this.config.skipAllowed=await i9(this.config,o.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(ae()-r):Math.trunc(ae()-r),this.analyze("Check Changed:");let l=[],u=[],p=[],c=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?ex(this,o.tensor):[],this.performance.face&&delete this.performance.face):(r=ae(),l=this.config.face.enabled?await ex(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let d=this.config.body.maxDetected===-1?Et(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?Sx(o.tensor,d):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?yy(o.tensor,d):[]:(x=this.config.body.modelPath)!=null&&x.includes("efficientpose")?u=this.config.body.enabled?Iy(o.tensor,d):[]:(A=this.config.body.modelPath)!=null&&A.includes("movenet")&&(u=this.config.body.enabled?xx(o.tensor,d):[]),this.performance.body&&delete this.performance.body):(r=ae(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await Sx(o.tensor,d):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?u=this.config.body.enabled?await yy(o.tensor,d):[]:(I=this.config.body.modelPath)!=null&&I.includes("efficientpose")?u=this.config.body.enabled?await Iy(o.tensor,d):[]:(T=this.config.body.modelPath)!=null&&T.includes("movenet")&&(u=this.config.body.enabled?await xx(o.tensor,d):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Et(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((M=(N=this.config.hand.detector)==null?void 0:N.modelPath)!=null&&M.includes("handdetect")?p=this.config.hand.enabled?ix(o.tensor,h):[]:(E=($=this.config.hand.detector)==null?void 0:$.modelPath)!=null&&E.includes("handtrack")&&(p=this.config.hand.enabled?ux(o.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ae(),(_=(S=this.config.hand.detector)==null?void 0:S.modelPath)!=null&&_.includes("handdetect")?p=this.config.hand.enabled?await ix(o.tensor,h):[]:(W=(O=this.config.hand.detector)==null?void 0:O.modelPath)!=null&&W.includes("handtrack")&&(p=this.config.hand.enabled?await ux(o.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((P=this.config.object.modelPath)!=null&&P.includes("nanodet")?c=this.config.object.enabled?bx(o.tensor,this.config):[]:(U=this.config.object.modelPath)!=null&&U.includes("centernet")&&(c=this.config.object.enabled?by(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ae(),(G=this.config.object.modelPath)!=null&&G.includes("nanodet")?c=this.config.object.enabled?await bx(o.tensor,this.config):[]:(q=this.config.object.modelPath)!=null&&q.includes("centernet")&&(c=this.config.object.enabled?await by(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,p,c]=await Promise.all([l,u,p,c])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=ae(),m=[...QI(l),...JI(u),...tS(p),...eS(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ae()-i):Math.trunc(ae()-i);let f=((H=this.process.tensor)==null?void 0:H.shape)||[0,0,0,0];this.result={face:l,body:u,hand:p,gesture:m,object:c,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return YS(l,u,p,m,f)}},J(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}async sleep(t){return new Promise(a=>{setTimeout(a,t)})}async video(t,a=!0,n=0){a?(qa(this,Ps)[t.id]||(this.config.debug&&K("video start",t.id),qa(this,Ps)[t.id]=!0),!t.paused&&qa(this,Ps)[t.id]&&t.readyState>=2&&await this.detect(t),n>0&&await this.sleep(n),qa(this,Ps)[t.id]&&requestAnimationFrame(()=>this.video(t,a,n))):(this.config.debug&&K("video stop",t.id),qa(this,Ps)[t.id]=!1)}};md=new WeakMap,gc=new WeakMap,yc=new WeakMap,dm=new WeakMap,Ps=new WeakMap;return DC(dye);})(); +2Q==`;async function rye(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(i=>i.blob()),a,n;switch(e.config.warmup){case"face":a=await t(V0);break;case"body":case"full":a=await t(U0);break;default:a=null}if(a){let r=await createImageBitmap(a);n=await e.detect(r,e.config),r.close()}return n}async function sye(e){return new Promise(t=>{let a;switch(e.config.warmup){case"face":a="data:image/jpeg;base64,"+V0;break;case"full":case"body":a="data:image/jpeg;base64,"+U0;break;default:a=""}let n;if(typeof Image!="undefined")n=new Image;else if(ne.Image)n=new ne.Image;else{t(void 0);return}n.onload=async()=>{let r=$n(n.naturalWidth,n.naturalHeight);if(!r)K("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(n,0,0);let i=await e.image(r,!0),o=i.tensor?await e.detect(i.tensor,e.config):void 0;t(o)}},a?n.src=a:t(void 0)})}async function iye(e){let t=r=>Buffer.from(r,"base64"),a;e.config.warmup==="face"?a=t(V0):a=t(U0);let n;if("node"in Ke&&ea()==="tensorflow"){let r=P3.decodeJpeg(a),s=Wt(r,0);e.tf.dispose(r),n=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&K("Warmup tfjs-node not loaded");return n}async function oye(e){let t;return typeof createImageBitmap=="function"?t=await rye(e):typeof Image!="undefined"||ne.Canvas!==void 0?t=await sye(e):t=await iye(e),t}async function lye(e){var o,l,u,p;if(!B().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=ea(),a=Vn();if(t!=="webgl"&&t!=="humangl"||!(a!=null&&a.checkCompileCompletion))return;B().set("ENGINE_COMPILE_ONLY",!0);let n=It().state.numTensors,r=[];for(let[c,d]of Object.entries(e.models.models)){if(!d)continue;let h=d!=null&&d.modelSignature&&((l=(o=d==null?void 0:d.inputs)==null?void 0:o[0])!=null&&l.shape)?[...d.inputs[0].shape]:[1,64,64,3],m=d!=null&&d.modelSignature&&((p=(u=d==null?void 0:d.inputs)==null?void 0:u[0])!=null&&p.dtype)?d.inputs[0].dtype:"float32";for(let g=0;gJ(y)):J(g)}catch(g){e.config.debug&&K("compile fail model:",c)}J(f)}let s=await a.checkCompileCompletionAsync();a.getUniformLocations(),e.config.debug&&K("compile pass:",{models:r,kernels:s.length}),B().set("ENGINE_COMPILE_ONLY",!1);let i=It().state.numTensors;i-n>0&&K("tensor leak:",i-n)}async function MS(e,t){await Bp(e,!1);let a=ae();return e.state="warmup",t&&(e.config=Et(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?hr():new Promise(async n=>{await e.models.load(),await zo(),await lye(e);let r=await oye(e),s=ae();e.config.debug&&K("warmup",e.config.warmup,Math.round(s-a),"ms"),e.emit("warmup"),n(r)})}var Ku,Yp,Zp,G0,$s,px=class{constructor(t){he(this,"version");he(this,"config");he(this,"result");he(this,"state");he(this,"process");he(this,"tf");he(this,"env",ne);he(this,"draw",i0);he(this,"match",_0);he(this,"models");he(this,"events");he(this,"faceTriangulation");he(this,"faceUVMap");he(this,"performance");Kn(this,Ku);Kn(this,Yp);Kn(this,Zp);he(this,"analyze",(...t)=>{if(!qa(this,Yp))return;let a=this.tf.engine().state.numTensors,n=qa(this,Ku);br(this,Ku,a);let r=a-n;r!==0&&K(...t,r)});Kn(this,G0,t=>{if(!qa(this,Zp))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof yt))return"input must be a tensor";try{this.tf.getBackend()}catch(a){return"backend not loaded"}return null});he(this,"webcam",new Qh);he(this,"emit",t=>{var a;(a=this.events)!=null&&a.dispatchEvent&&this.events.dispatchEvent(new Event(t))});Kn(this,$s,{});let a=(Dp.tfjs||Wg).replace(/-(.*)/,"");Xo.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${a}/dist/`,Xo.modelBasePath=ne.browser?"../models/":"file://models/",this.version=L3,Object.defineProperty(this,"version",{value:L3}),this.config=JSON.parse(JSON.stringify(Xo)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Et(this.config,t)),Vk(this.config),this.tf=Ke,this.state="idle",br(this,Ku,0),br(this,Yp,!1),br(this,Zp,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new Kp(this),j3(),this.result=hr(),this.process={tensor:null,canvas:null},this.faceTriangulation=F9,this.faceUVMap=D9,B0(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&K(`version: ${this.version}`),this.config.debug&&K(`tfjs version: ${this.tf.version["tfjs-core"]}`);let n=JSON.parse(JSON.stringify(this.env));delete n.kernels,delete n.initial,delete n.perfadd,this.config.debug&&K("environment:",n)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Xo)),this.config.backend=t,O3(),ne.initial=!0}validate(t){let a=_3(Xo,t||this.config);return a.length===0&&(this.config=Et(this.config,t)),a}now(){return ae()}image(t,a=!1){return Zh(t,this.config,a)}async segmentation(t,a){var s,i,o;if(a&&(this.config=Et(this.config,a)),!this.config.segmentation.enabled)return null;let n=await Zh(t,this.config);if(!n.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await CS(n.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("meet")&&(r=await rS(n.tensor,this.config)),(o=this.config.segmentation.modelPath)!=null&&o.includes("selfie")&&(r=await NS(n.tensor,this.config)),J(n.tensor),r}compare(t,a){return Bk(this.config,t,a)}async init(){await Bp(this,!0),await this.tf.ready(),O3()}async load(t){this.state="load";let a=ae(),n=Object.values(this.models.models).filter(i=>i).length;t&&(this.config=Et(this.config,t)),this.env.initial&&(await Bp(this,!1)||K("error: backend check failed"),await zo(),this.env.browser&&(this.config.debug&&K("configuration:",this.config),this.config.debug&&K("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&K("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(i=>i).length!==n&&(this.models.validate(),this.emit("load"));let s=Math.trunc(ae()-a);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return nS(t,this.config)}async warmup(t){let a=ae(),n=await MS(this,t),r=ae();return this.performance.warmup=Math.trunc(r-a),n}async profile(t,a){let n=await this.tf.profile(()=>this.detect(t,a)),r={},s=0;for(let o of n.kernels){let l=Number(o.kernelTimeMs)||0;r[o.name]?r[o.name]+=l:r[o.name]=l,s+=l}let i=[];Object.entries(r).forEach(o=>i.push({kernel:o[0],time:o[1],perc:0}));for(let o of i)o.perc=Math.round(1e3*o.time/s)/1e3,o.time=Math.round(1e3*o.time)/1e3;return i.sort((o,l)=>l.time-o.time),i.length=20,i}async detect(t,a){return this.state="detect",new Promise(async n=>{var g,y,x,A,b,w,I,T,N,M,$,E,S,_,O,W,P,U,G,q,H;this.state="config";let r;this.config=Et(this.config,a),this.state="check";let s=qa(this,G0).call(this,t);s&&(K(s,t),this.emit("error"),n(hr(s)));let i=ae();await this.load(),r=ae(),this.state="image";let o=await Zh(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(ae()-r):Math.trunc(ae()-r),this.analyze("Get Image:"),!o.tensor){this.config.debug&&K("could not convert input to tensor"),this.emit("error"),n(hr("could not convert input to tensor"));return}this.emit("image"),r=ae(),this.config.skipAllowed=await Wk(this.config,o.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(ae()-r):Math.trunc(ae()-r),this.analyze("Check Changed:");let l=[],u=[],p=[],c=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?_y(this,o.tensor):[],this.performance.face&&delete this.performance.face):(r=ae(),l=this.config.face.enabled?await _y(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let d=this.config.body.maxDetected===-1?Et(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?sx(o.tensor,d):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?Z3(o.tensor,d):[]:(x=this.config.body.modelPath)!=null&&x.includes("efficientpose")?u=this.config.body.enabled?ry(o.tensor,d):[]:(A=this.config.body.modelPath)!=null&&A.includes("movenet")&&(u=this.config.body.enabled?Jy(o.tensor,d):[]),this.performance.body&&delete this.performance.body):(r=ae(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await sx(o.tensor,d):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?u=this.config.body.enabled?await Z3(o.tensor,d):[]:(I=this.config.body.modelPath)!=null&&I.includes("efficientpose")?u=this.config.body.enabled?await ry(o.tensor,d):[]:(T=this.config.body.modelPath)!=null&&T.includes("movenet")&&(u=this.config.body.enabled?await Jy(o.tensor,d):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Et(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((M=(N=this.config.hand.detector)==null?void 0:N.modelPath)!=null&&M.includes("handdetect")?p=this.config.hand.enabled?Wy(o.tensor,h):[]:(E=($=this.config.hand.detector)==null?void 0:$.modelPath)!=null&&E.includes("handtrack")&&(p=this.config.hand.enabled?Uy(o.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ae(),(_=(S=this.config.hand.detector)==null?void 0:S.modelPath)!=null&&_.includes("handdetect")?p=this.config.hand.enabled?await Wy(o.tensor,h):[]:(W=(O=this.config.hand.detector)==null?void 0:O.modelPath)!=null&&W.includes("handtrack")&&(p=this.config.hand.enabled?await Uy(o.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((P=this.config.object.modelPath)!=null&&P.includes("nanodet")?c=this.config.object.enabled?ex(o.tensor,this.config):[]:(U=this.config.object.modelPath)!=null&&U.includes("centernet")&&(c=this.config.object.enabled?ey(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ae(),(G=this.config.object.modelPath)!=null&&G.includes("nanodet")?c=this.config.object.enabled?await ex(o.tensor,this.config):[]:(q=this.config.object.modelPath)!=null&&q.includes("centernet")&&(c=this.config.object.enabled?await ey(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,p,c]=await Promise.all([l,u,p,c])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=ae(),m=[...PI(l),...$I(u),...FI(p),..._I(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ae()-r):Math.trunc(ae()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ae()-i):Math.trunc(ae()-i);let f=((H=this.process.tensor)==null?void 0:H.shape)||[0,0,0,0];this.result={face:l,body:u,hand:p,gesture:m,object:c,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return ES(l,u,p,m,f)}},J(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}async sleep(t){return new Promise(a=>{setTimeout(a,t)})}async video(t,a=!0,n=0){a?(qa(this,$s)[t.id]||(this.config.debug&&K("video 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p=Math.cos(g),u=Math.sin(g),k=.213,P=.715,N=.072;T.colorMatrix([k+p*(1-k)+u*-k,P+p*-P+u*-P,N+p*-N+u*(1-N),0,0,k+p*-k+u*.143,P+p*(1-P)+u*.14,N+p*-N+u*-.283,0,0,k+p*-k+u*-(1-k),P+p*-P+u*P,N+p*(1-N)+u*N,0,0,0,0,0,1,0])},desaturateLuminance:()=>{T.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{T.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{T.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{T.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{T.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{T.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{T.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{T.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:g=>{let p=new Float32Array(g),u=1/l.width,k=1/l.height,P=h(G1);P&&(i.uniform1fv(P.uniform.m,p),i.uniform2f(P.uniform.px,u,k),f())},detectEdges:()=>{T.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{T.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{T.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:g=>{let p=g||1;T.convolution.call(this,[0,-1*p,0,-1*p,1+4*p,-1*p,0,-1*p,0])},emboss:g=>{let p=g||1;T.convolution.call(this,[-2*p,-1*p,0,-1*p,1,1*p,0,1*p,2*p])},blur:g=>{let p=g/7/l.width,u=g/7/l.height,k=h(H1);k&&(i.uniform2f(k.uniform.px,0,u),f(d.INTERMEDIATE),i.uniform2f(k.uniform.px,p,0),f())},pixelate:g=>{let p=g/l.width,u=g/l.height,k=h(B1);k&&(i.uniform2f(k.uniform.size,p,u),f())}};this.add=function(g){let p=Array.prototype.slice.call(arguments,1),u=T[g];s.push({func:u,args:p})},this.reset=function(){s=[]},this.get=function(){return s},this.apply=function(g){y(g.width,g.height),e=0,t||(t=i.createTexture()),i.bindTexture(i.TEXTURE_2D,t),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.NEAREST),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,g);for(let p=0;pd.data())),A=Math.max(s[0][0],s[1][0],s[2][0]),l=(A>1?255:1)/A,c;if(l>1){let d=[U.sub(n[0],o[0]),U.sub(n[1],o[1]),U.sub(n[2],o[2])],i=[U.sub(r[0],o[0]),U.sub(r[1],o[1]),U.sub(r[2],o[2])],y=[U.mul(d[0],l),U.mul(d[1],l),U.mul(d[2],l)],x=U.stack([y[0],y[1],y[2]],2);c=U.reshape(x,[1,t.shape[0]||0,t.shape[1]||0,3]),U.dispose([...d,...i,...y,x])}else c=U.expandDims(t,0);return U.dispose([...n,...o,...r,n,t,e]),c}var q2=3840,M0=null,P0=null,y2=null,t0,ne={inputSum:0,cacheDiff:1,sumMethod:0,inputTensor:void 0};function 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s=o,A=r;if(s>q2&&(s=q2,A=Math.trunc(s*r/o)),A>q2&&(A=q2,s=Math.trunc(A*o/r)),(((y=t.filter)==null?void 0:y.width)||0)>0?s=t.filter.width:(((x=t.filter)==null?void 0:x.height)||0)>0&&(s=o*((t.filter.height||0)/r)),(t.filter.height||0)>0?A=t.filter.height:(t.filter.width||0)>0&&(A=r*((t.filter.width||0)/o)),!s||!A)throw new Error("input error: cannot determine dimension");(!M0||M0.width!==s||M0.height!==A)&&(M0=Ae(s,A));let a=M0.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?a.putImageData(e,0,0):t.filter.flip&&typeof a.translate!="undefined"?(a.translate(o,0),a.scale(-1,1),a.drawImage(e,0,0,o,r,0,0,M0.width,M0.height),a.setTransform(1,0,0,1,0,0)):a.drawImage(e,0,0,o,r,0,0,M0.width,M0.height),(!P0||M0.width!==P0.width||M0.height!==P0.height)&&(P0=Ae(M0.width,M0.height)),t.filter.enabled&&R.webgl.supported?(t0||(t0=R.browser?new V1:null),R.filter=!!t0,t0!=null&&t0.add?(t0.reset(),t.filter.brightness!==0&&t0.add("brightness",t.filter.brightness),t.filter.contrast!==0&&t0.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&t0.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&t0.add("blur",t.filter.blur),t.filter.saturation!==0&&t0.add("saturation",t.filter.saturation),t.filter.hue!==0&&t0.add("hue",t.filter.hue),t.filter.negative&&t0.add("negative"),t.filter.sepia&&t0.add("sepia"),t.filter.vintage&&t0.add("brownie"),t.filter.sepia&&t0.add("sepia"),t.filter.kodachrome&&t0.add("kodachrome"),t.filter.technicolor&&t0.add("technicolor"),t.filter.polaroid&&t0.add("polaroid"),t.filter.pixelate!==0&&t0.add("pixelate",t.filter.pixelate),((m=t0.get())==null?void 0:m.length)>1?P0=t0.apply(M0):P0=t0.draw(M0)):(t.debug&&b("input process error: cannot initialize filters"),R.webgl.supported=!1,t.filter.enabled=!1,X2(M0,P0))):(X2(M0,P0),t0&&(t0=null),R.filter=!!t0),!n)return{tensor:null,canvas:P0};if(!P0)throw new 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s=o,A=r;if(s>U2&&(s=U2,A=Math.trunc(s*r/o)),A>U2&&(A=U2,s=Math.trunc(A*o/r)),(((y=t.filter)==null?void 0:y.width)||0)>0?s=t.filter.width:(((x=t.filter)==null?void 0:x.height)||0)>0&&(s=o*((t.filter.height||0)/r)),(t.filter.height||0)>0?A=t.filter.height:(t.filter.width||0)>0&&(A=r*((t.filter.width||0)/o)),!s||!A)throw new Error("input error: cannot determine dimension");(!M0||M0.width!==s||M0.height!==A)&&(M0=Ae(s,A));let a=M0.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?a.putImageData(e,0,0):t.filter.flip&&typeof a.translate!="undefined"?(a.translate(o,0),a.scale(-1,1),a.drawImage(e,0,0,o,r,0,0,M0.width,M0.height),a.setTransform(1,0,0,1,0,0)):a.drawImage(e,0,0,o,r,0,0,M0.width,M0.height),(!P0||M0.width!==P0.width||M0.height!==P0.height)&&(P0=Ae(M0.width,M0.height)),t.filter.enabled&&R.webgl.supported?(t0||(t0=R.browser?new 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p=new Float32Array(g),u=1/l.width,k=1/l.height,P=h(V1);P&&(i.uniform1fv(P.uniform.m,p),i.uniform2f(P.uniform.px,u,k),f())},detectEdges:()=>{T.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{T.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{T.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:g=>{let p=g||1;T.convolution.call(this,[0,-1*p,0,-1*p,1+4*p,-1*p,0,-1*p,0])},emboss:g=>{let p=g||1;T.convolution.call(this,[-2*p,-1*p,0,-1*p,1,1*p,0,1*p,2*p])},blur:g=>{let p=g/7/l.width,u=g/7/l.height,k=h(G1);k&&(i.uniform2f(k.uniform.px,0,u),f(d.INTERMEDIATE),i.uniform2f(k.uniform.px,p,0),f())},pixelate:g=>{let p=g/l.width,u=g/l.height,k=h(H1);k&&(i.uniform2f(k.uniform.size,p,u),f())}};this.add=function(g){let p=Array.prototype.slice.call(arguments,1),u=T[g];s.push({func:u,args:p})},this.reset=function(){s=[]},this.get=function(){return 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n.getContext("2d").drawImage(e,0,0),n}async function K2(e,t,n=!0){var y,x,m;if(!e)return t.debug&&b("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof I.Tensor)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input error: type not recognized");if(e instanceof I.Tensor){let f=null;if(e.isDisposedInternal)throw new Error("input error: attempted to use tensor but it is disposed");if(!e.shape)throw new 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Z1:null),R.filter=!!t0,t0!=null&&t0.add?(t0.reset(),t.filter.brightness!==0&&t0.add("brightness",t.filter.brightness),t.filter.contrast!==0&&t0.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&t0.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&t0.add("blur",t.filter.blur),t.filter.saturation!==0&&t0.add("saturation",t.filter.saturation),t.filter.hue!==0&&t0.add("hue",t.filter.hue),t.filter.negative&&t0.add("negative"),t.filter.sepia&&t0.add("sepia"),t.filter.vintage&&t0.add("brownie"),t.filter.sepia&&t0.add("sepia"),t.filter.kodachrome&&t0.add("kodachrome"),t.filter.technicolor&&t0.add("technicolor"),t.filter.polaroid&&t0.add("polaroid"),t.filter.pixelate!==0&&t0.add("pixelate",t.filter.pixelate),((m=t0.get())==null?void 0:m.length)>1?P0=t0.apply(M0):P0=t0.draw(M0)):(t.debug&&b("input process error: cannot initialize filters"),R.webgl.supported=!1,t.filter.enabled=!1,Y2(M0,P0))):(Y2(M0,P0),t0&&(t0=null),R.filter=!!t0),!n)return{tensor:null,canvas:P0};if(!P0)throw new 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D=Z(H());var se,n2=224,n3,eA=5,At=[8,16,32,32,32];function tA(){let e=[],t=0;for(;tn.x)),y:D.tensor1d(e.map(n=>n.y))}}async function o3(e){if(R.initial&&(se=null),!se&&e.body.detector&&e.body.detector.modelPath){se=await L(e.body.detector.modelPath);let t=se!=null&&se.executor?Object.values(se.modelSignature.inputs):void 0;n2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&se&&b("cached model:",se.modelUrl);return tA(),se}var t3=[5,5];function nA(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,n2),t.x),r=D.add(D.div(r,n2),t.y),s=D.mul(D.div(s,n2),t3[0]),A=D.mul(D.div(A,n2),t3[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),d=D.add(l,A);return D.stack([a,l,c,d],1)})}async function oA(e,t,n,o){var c,d;let r=[],s={};s.boxes=nA(e,n3),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 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n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function at(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var Z0,y5=256,x5=Number.MAX_SAFE_INTEGER,rA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},lt=[],Ce=[[0,0],[0,0],[0,0],[0,0]],A3=0,a3=e=>1-1/(1+Math.exp(e)),l3=e=>o3(e);async function c3(e){if(R.initial&&(Z0=null),Z0)e.debug&&b("cached model:",Z0.modelUrl);else{Z0=await L(e.body.modelPath);let t=Z0!=null&&Z0.executor?Object.values(Z0.modelSignature.inputs):void 0;y5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Z0}function i3(e,t,n){var s,A;let o={};if(!((s=e==null?void 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t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,pt),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,u2),t.centersNormalized=O.div(t.centers,u2),t.halfBoxSize=O.div(t.boxSizesNormalized,C.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,u2),t.endNormalized=O.mul(t.ends,u2);let n=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>O.dispose(t[o])),n}async function S3(e,t){var c,d,i,y,x,m,f,h,T;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={},o=[0,0],r=[1,1];if((d=(c=t==null?void 0:t.face)==null?void 0:c.detector)!=null&&d.square){let g=Math.max(e.shape[2],e.shape[1]);o=[Math.floor((g-e.shape[2])/2),Math.floor((g-e.shape[1])/2)],n.padded=O.pad(e,[[0,0],[o[1],o[1]],[o[0],o[0]],[0,0]]),r=[e.shape[2]/g,e.shape[1]/g],o=[o[0]/ge,o[1]/ge]}else n.padded=e.clone();n.resized=O.image.resizeBilinear(n.padded,[ge,ge]),n.div=O.div(n.resized,C.tf127),n.normalized=O.sub(n.div,C.tf1);let s=xe==null?void 0:xe.execute(n.normalized);if(Array.isArray(s)&&s.length>2){let g=s.sort((p,u)=>p.size-u.size);n.concat384=O.concat([g[0],g[2]],2),n.concat512=O.concat([g[1],g[3]],2),n.concat=O.concat([n.concat512,n.concat384],1),n.batch=O.squeeze(n.concat,[0])}else Array.isArray(s)?n.batch=O.squeeze(s[0]):n.batch=O.squeeze(s);O.dispose(s),n.boxes=pA(n.batch),n.logits=O.slice(n.batch,[0,0],[-1,1]),n.sigmoid=O.sigmoid(n.logits),n.scores=O.squeeze(n.sigmoid),n.nms=await O.image.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((y=t.face.detector)==null?void 0:y.iouThreshold)||0,((x=t.face.detector)==null?void 0:x.minConfidence)||0);let A=await n.nms.array(),a=[],l=await n.scores.data();for(let g=0;g(((m=t.face.detector)==null?void 0:m.minConfidence)||0)){let u={};u.bbox=O.slice(n.boxes,[A[g],0],[1,-1]),u.slice=O.slice(n.batch,[A[g],w3-1],[1,-1]),u.squeeze=O.squeeze(u.slice),u.landmarks=O.reshape(u.squeeze,[w3,-1]);let k=await u.bbox.data(),P=[k[0]*r[0]-o[0],k[1]*r[1]-o[1],k[2]*r[0]-o[0],k[3]*r[1]-o[1]],N={startPoint:[P[0],P[1]],endPoint:[P[2],P[3]],landmarks:await u.landmarks.array(),confidence:p};u.anchor=O.slice(pt,[A[g],0],[1,2]);let B=await u.anchor.data(),J=g3(N,[(e.shape[2]||0)/ge,(e.shape[1]||0)/ge],B),G=ft(J,((f=t.face.detector)==null?void 0:f.scale)||1.4),K=mt(G);K.size[0]>(((h=t.face.detector)==null?void 0:h.minSize)||0)&&K.size[1]>(((T=t.face.detector)==null?void 0:T.minSize)||0)&&a.push(K),Object.keys(u).forEach(e0=>O.dispose(u[e0]))}}return Object.keys(n).forEach(g=>O.dispose(n[g])),a}var ye=Z(H());var K0,We=0,P5=ae.leftEyeLower0,k5=ae.rightEyeLower0,h2={leftBounds:[P5[0],P5[P5.length-1]],rightBounds:[k5[0],k5[k5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function L3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&b("cached model:",K0.modelUrl):K0=await L((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function ut(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},N3=(e,t,n,o,r,s=!1,A=2.3)=>{let a=mt(ft(T3([e[n],e[o]]),A)),l=p2(a),c=ye.image.cropAndResize(t,[[a.startPoint[1]/r,a.startPoint[0]/r,a.endPoint[1]/r,a.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let d=ye.image.flipLeftRight(c);ye.dispose(c),c=d}return{box:a,boxSize:l,crop:c}},I3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function C3(e,t,n,o){var N,B;if(!(K0!=null&&K0.executor))return e;let{box:r,boxSize:s,crop:A}=N3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0,((N=o.face.iris)==null?void 0:N.scale)||2.3),{box:a,boxSize:l,crop:c}=N3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0,((B=o.face.iris)==null?void 0:B.scale)||2.3),d=ye.concat([A,c]);ye.dispose(A),ye.dispose(c);let i=K0.execute(d);ye.dispose(d);let y=await i.data();ye.dispose(i);let x=y.slice(0,b2.numCoordinates*3),{rawCoords:m,iris:f}=I3(x,r,s,!0),h=y.slice(b2.numCoordinates*3),{rawCoords:T,iris:g}=I3(h,a,l,!1),p=uA(e);Math.abs(p)<30?(ut(e,m,"left",null),ut(e,T,"right",null)):p<1?ut(e,m,"left",["EyeUpper0","EyeLower0"]):ut(e,T,"right",["EyeUpper0","EyeLower0"]);let u=O3(e,f,"left"),k=O3(e,g,"right");return e.concat(u).concat(k)}async function D3(e,t){var s,A,a,l,c,d,i,y,x,m;let n={lips:await((A=(s=t.filter(f=>f.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(f=>f.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((d=(c=t.filter(f=>f.size===142))==null?void 0:c[0])==null?void 0:d.data()),irisR:await((y=(i=t.filter(f=>f.size===10))==null?void 0:i[1])==null?void 0:y.data()),eyeR:await((m=(x=t.filter(f=>f.size===142))==null?void 0:x[1])==null?void 0:m.data())};for(let f of Object.values(n))if(!f)return e;let o=e2.reduce((f,h)=>f+=e[h][2],0)/e2.length;for(let f=0;ff+=e[h][2],0)/t2.length;for(let f=0;fv()-Te.timestamp,o=Te.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||Te.boxes.length===0?(Te.boxes=await S3(e,t),Te.timestamp=v(),Te.skipped=0):Te.skipped++;let r=[],s=[],A=0,a=W2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!s0)t.debug&&b("face mesh detection requested, but model is not loaded");else{if((x=t.face.attention)!=null&&x.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=s0.execute(P.tensor),J=await N.find(G=>G.shape[G.shape.length-1]===1).data();if(P.faceScore=Math.round(100*J[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=xt(p,e),P.boxRaw=yt(p,e),P.size=p.size,P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(G=>[G[0]/(e.shape[2]||1),G[1]/(e.shape[1]||1),(G[2]||0)/a]);for(let G of Object.keys(_e))P.annotations[G]=[P.mesh[_e[G]]]}}else{let G=N.find(o0=>o0.shape[o0.shape.length-1]===1404),K=De.reshape(G,[-1,3]),e0=await K.array();De.dispose(K),(f=t.face.attention)!=null&&f.enabled?e0=await D3(e0,N):(h=t.face.iris)!=null&&h.enabled&&(e0=await C3(e0,P.tensor,W2,t)),P.mesh=M3(e0,p,u,k,W2),P.meshRaw=P.mesh.map(o0=>[o0[0]/(e.shape[2]||0),o0[1]/(e.shape[1]||0),(o0[2]||0)/a]);for(let o0 of Object.keys(ae))P.annotations[o0]=ae[o0].map(N0=>P.mesh[N0]);P.score=P.faceScore;let n0={...k3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=xt(n0,e),P.boxRaw=yt(n0,e),P.size=n0.size,s.push(n0)}De.dispose(N)}P.score>(((T=t.face.detector)==null?void 0:T.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return Te.boxes=s,r}async function B3(e){var t,n,o,r,s,A;return R.initial&&(s0=null),(t=e.face.attention)!=null&&t.enabled&&(s0!=null&&s0.signature)&&Object.keys(((n=s0==null?void 0:s0.signature)==null?void 0:n.outputs)||{}).length<6&&(s0=null),s0?e.debug&&b("cached model:",s0.modelUrl):(o=e.face.attention)!=null&&o.enabled?s0=await L(e.face.attention.modelPath):s0=await L((r=e.face.mesh)==null?void 0:r.modelPath),W2=s0.executor&&((s=s0==null?void 0:s0.inputs)!=null&&s[0].shape)?(A=s0==null?void 0:s0.inputs)==null?void 0:A[0].shape[2]:256,s0}var H3=$e,G3=L2;var J0=Z(H());var z5=[],k0,ht=[],V3=0,Z3=0,E5=Number.MAX_SAFE_INTEGER,S5=!1;async function q3(e){var t,n,o;return R.initial&&(k0=null),k0?e.debug&&b("cached model:",k0.modelUrl):(k0=await L((t=e.face.emotion)==null?void 0:t.modelPath),S5=((o=(n=k0==null?void 0:k0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,S5?z5=["angry","disgust","fear","happy","neutral","sad","surprise"]:z5=["angry","disgust","fear","happy","sad","surprise","neutral"]),k0}async function j5(e,t,n,o){var A,a;if(!k0)return[];let r=E5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-Z3;return t.skipAllowed&&s&&r&&V3===o&&ht[n]&&ht[n].length>0?(E5++,ht[n]):(E5=0,new Promise(async l=>{var d,i,y;let c=[];if((d=t.face.emotion)!=null&&d.enabled){let x={},m=k0!=null&&k0.inputs[0].shape?k0.inputs[0].shape[2]:0;if(((i=t.face.emotion)==null?void 0:i.crop)>0){let h=(y=t.face.emotion)==null?void 0:y.crop,T=[[h,h,1-h,1-h]];x.resize=J0.image.cropAndResize(e,T,[0],[m,m])}else x.resize=J0.image.resizeBilinear(e,[m,m],!1);S5?(x.mul=J0.mul(x.resize,255),x.normalize=J0.sub(x.mul,[103.939,116.779,123.68]),x.emotion=k0==null?void 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g=ie.argMax(m.find(B=>B.shape[1]===100),1),p=(await g.data())[0];ie.dispose(g);let k=await m.find(B=>B.shape[1]===100).data();r.age=Math.round(k[p-1]>k[p+1]?10*p-100*k[p-1]:10*p+100*k[p+1])/10,(Number.isNaN(h[0])||Number.isNaN(k[0]))&&b("faceres error:",{model:w0,result:m});let P=m.find(B=>B.shape[1]===1024),N=P?await P.data():[];r.descriptor=Array.from(N),m.forEach(B=>ie.dispose(B))}Fe[n]=r,Y3=o,i(r)}))}var g2=.1,O5=.5;function gA(e,t,n){let o=!1,r=n.length-1;for(let s=0;st!=n[r].y>t&&e<(n[r].x-n[s].x)*(t-n[s].y)/(n[r].y-n[s].y)+n[s].x&&(o=!o);return o}async function Q3(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,o=await e.tensor.buffer(),r=[];for(let A of ae.silhouette)r.push({x:(e.mesh[A][0]-e.box[0])/e.box[2],y:(e.mesh[A][1]-e.box[1])/e.box[3]});g2&&g2>0&&(r=r.map(A=>({x:A.x>.5?A.x+g2:A.x-g2,y:A.y>.5?A.y+g2:A.y-g2})));for(let A=0;Av()-$3,s=L5<(((a=t.face.antispoof)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&_3===o&&bt[n]?(L5++,bt[n]):(L5=0,new Promise(async l=>{let c=gt.image.resizeBilinear(e,[E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[2]:0,E0!=null&&E0.inputs[0].shape?E0.inputs[0].shape[1]:0],!1),d=E0==null?void 0:E0.execute(c),i=(await d.data())[0];bt[n]=Math.round(100*i)/100,_3=o,$3=v(),gt.dispose([c,d]),l(bt[n])}))}var vt=Z(H());var z0,Tt=[],W5=Number.MAX_SAFE_INTEGER,nn=0,on=0;async function rn(e){var t;return R.initial&&(z0=null),z0?e.debug&&b("cached model:",z0.modelUrl):z0=await L((t=e.face.liveness)==null?void 0:t.modelPath),z0}async function D5(e,t,n,o){var A,a;if(!(z0!=null&&z0.executor))return 0;let r=(((A=t.face.liveness)==null?void 0:A.skipTime)||0)>v()-on,s=W5<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&s&&nn===o&&Tt[n]?(W5++,Tt[n]):(W5=0,new Promise(async l=>{let c=vt.image.resizeBilinear(e,[z0!=null&&z0.inputs[0].shape?z0.inputs[0].shape[2]:0,z0!=null&&z0.inputs[0].shape?z0.inputs[0].shape[1]:0],!1),d=z0==null?void 0:z0.execute(c),i=(await d.data())[0];Tt[n]=Math.round(100*i)/100,nn=o,on=v(),vt.dispose([c,d]),l(Tt[n])}))}var Rt=Z(H());var le,F5=[],vA=["white","black","asian","indian","other"],RA=[15,23,28,35.5,45.5,55.5,65],An=0,an=0,B5=Number.MAX_SAFE_INTEGER;async function ln(e){var t;return R.initial&&(le=null),le?e.debug&&b("cached model:",le.modelUrl):le=await L((t=e.face.gear)==null?void 0:t.modelPath),le}async function H5(e,t,n,o){var A,a;if(!le)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=B5<(((A=t.face.gear)==null?void 0:A.skipFrames)||0),s=(((a=t.face.gear)==null?void 0:a.skipTime)||0)>v()-an;return t.skipAllowed&&s&&r&&An===o&&F5[n]?(B5++,F5[n]):(B5=0,new Promise(async l=>{var T,g,p,u;if(!(le!=null&&le.inputs[0].shape))return;let c={},d=[[0,.1,.9,.9]];if(((T=t.face.gear)==null?void 0:T.crop)>0){let 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G0.all)n[G0.getName(o)]={curl:He.getName(t.curls[o]),direction:d0.getName(t.directions[o])};return n}function Dn(e){let t=[];if(!e||e.length===0)return t;let n=Wn(e);for(let o of jn){let r=o.matchAgainst(n.curls,n.directions);r>=zA&&t.push({name:o.name,confidence:r})}return t}var Fn=e=>{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&o&&r&&o.position[1]l.part==="leftShoulder"),a=e[n].keypoints.find(l=>l.part==="rightShoulder");A&&a&&Math.abs(A.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${A.position[1]>a.position[1]?"left":"right"}`})}return t},Bn=e=>{if(!e)return[];let t=[];for(let n=0;n450){let o=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(o/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing 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T=Math.abs(e[A].mesh[145][1]-e[A].annotations.rightEyeIris[0][1])/e[A].box[3],g=Math.abs(e[A].mesh[374][1]-e[A].annotations.leftEyeIris[0][1])/e[A].box[3];(g<.01||T<.01||g>.022||T>.022)&&(x=!1),(g<.01||T<.01)&&t.push({iris:A,gesture:"looking down"}),(g>.022||T>.022)&&t.push({iris:A,gesture:"looking up"}),x&&t.push({iris:A,gesture:"looking center"})}return t},Gn=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=o.reduce((A,a)=>(A.position[2]||0)<(a.position[2]||0)?A:a);t.push({hand:n,gesture:`${r.name} forward`});let s=o.reduce((A,a)=>A.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function St(e,t=1.5){let n=D2(e),o=zt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function jt(e){let t=D2(e),n=zt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function OA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Yn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return OA(n)}var Vn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function qe(e,t){let n=0;for(let o=0;o[A.x,A.y]),this.anchorsTensor=W.tensor2d(this.anchors),this.inputSize=((s=(r=(o=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:o[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=W.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=W.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=W.slice(t,[0,0],[-1,2]),n.boxSizes=W.slice(t,[0,2],[-1,2]),n.div=W.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=W.add(n.div,this.anchorsTensor),n.halfBoxSizes=W.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=W.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=W.mul(n.sub,this.inputSizeTensor),n.add=W.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=W.mul(n.add,this.inputSizeTensor);let o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let d=await c.box.data(),i=d.slice(0,2),y=d.slice(2,4),x=await c.palmLandmarks.array(),m={startPoint:i,endPoint:y,palmLandmarks:x,confidence:r[l]},f=Un(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(f),Object.keys(c).forEach(h=>W.dispose(c[h]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var $0=Z(H());var DA=5,_n=1.65,$n=[0,5,9,13,17,1,2],FA=0,BA=2,eo=0,It=class{constructor(t,n){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>t1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return St(jt(r),DA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=St(jt(n),_n);o.palmLandmarks=[];for(let r=0;r<$n.length;r++)o.palmLandmarks.push(t[$n[r]].slice(0,2));return o}transformRawCoords(t,n,o,r){let s=zt(n),A=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],a=t.map(x=>[A[0]*(x[0]-this.inputSize/2),A[1]*(x[1]-this.inputSize/2),A[2]*x[2]]),l=e1(o,[0,0]),c=a.map(x=>[...t1(x,l),x[2]]),d=Kn(r),i=[...D2(n),1],y=[qe(i,d[0]),qe(i,d[1])];return c.map(x=>[Math.trunc(x[0]+y[0]),Math.trunc(x[1]+y[1]),Math.trunc(x[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>v()-eo,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A?this.skipped++:(r=await this.handDetector.predict(t,n),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,f,d,m),B=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...B,confidence:u};let J={landmarks:N,confidence:u,boxConfidence:c.confidence,fingerConfidence:u,box:{topLeft:B.startPoint,bottomRight:B.endPoint}};a.push(J)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let d=St(jt(c),_n),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var to={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,n1;function GA(){let e=l2?new Nt(l2):void 0;e&&c2&&(n1=new It(e,c2))}async function o1(e,t){n1||GA();let n=await n1.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let d of A)d[0]a[2]&&(a[2]=d[0]),d[1]>a[3]&&(a[3]=d[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else 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0;Xe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function lo(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&b("cached model:",p0[1].modelUrl);else{p0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;Xe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function UA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,qA),A=Math.round(s*r/8)*8;o.resize=$.image.resizeBilinear(e,[s,A]),o.cast=$.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,VA),o.boxes=$.squeeze(o.rawBoxes,[0,2]),o.scores=$.squeeze(o.rawScores,[0]);let a=$.unstack(o.scores,1);$.dispose(a[so]),a.splice(so,1),o.filtered=$.stack(a,1),$.dispose(a),o.max=$.max(o.filtered,1),o.argmax=$.argMax(o.filtered,1);let l=0;o.nms=await $.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),d=await o.max.data(),i=await o.argmax.data();for(let y of Array.from(c)){let x=$.slice(o.boxes,y,1),m=await x.data();$.dispose(x);let f=[m[1],m[0],m[3]-m[1],m[2]-m[0]],h=at(f,XA),T=[Math.trunc(f[0]*Ee[0]),Math.trunc(f[1]*Ee[1]),Math.trunc(f[2]*Ee[0]),Math.trunc(f[3]*Ee[1])],g=d[y],p=ZA[i[y]],u={id:l++,score:g,box:T,boxRaw:h,label:p};n.push(u)}return Object.keys(o).forEach(y=>$.dispose(o[y])),n.sort((y,x)=>x.score-y.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function s1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=$.image.cropAndResize(e,[s],[0],[Xe[1][0],Xe[1][1]],"bilinear"),r.div=$.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=$.reshape(r.keypoints,[-1,3]);let d=(await r.reshaped.array()).map(i=>[i[0]/Xe[1][1],i[1]/Xe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=d.map(i=>[Ee[0]*(i[0]+t.boxRaw[0]),Ee[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Et(o.keypoints);for(let i of Object.keys(ao))o.annotations[i]=ao[i].map(y=>o.landmarks&&o.keypoints[y]?o.keypoints[y]:null)}Object.keys(r).forEach(l=>$.dispose(r[l]))}return o}async function A1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];Ee=[e.shape[2]||0,e.shape[1]||0],Ot++;let n=(t.hand.skipTime||0)>v()-r1,o=Ot<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-r1,l=Ot<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(d=>s1(e,d,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(d=>s1(e,d,t))):(m0.boxes=await UA(e,t),r1=v(),m0.hands=await Promise.all(m0.boxes.map(d=>s1(e,d,t))),Ot=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[d].fingerScore&&m0.hands[d].fingerScore>(t.hand.minConfidence||0)){let y=at(i.box,Ao),x=at(i.boxRaw,Ao);m0.boxes.push({...c[d],box:y,boxRaw:x})}}for(let d=0;d({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var F2={};Se(F2,{connected:()=>Ct,horizontal:()=>a1,kpt:()=>Lt,relative:()=>l1,vertical:()=>i1});var Lt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],a1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],i1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],l1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Ct={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=ve(),c1=0;function xo(e,t){var A,a,l,c,d,i,y,x,m,f,h,T,g,p,u,k,P,N,B,J,G,K,e0,n0,o0,N0;let n=v();if(!e)return ve();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let M=0;M((r-1)*z.body[M].box[q]+V)/r),C0=e.body[M].boxRaw.map((V,q)=>((r-1)*z.body[M].boxRaw[q]+V)/r),x0=e.body[M].keypoints.map((V,q)=>{var 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M=0;M((r-1)*z.face[M].box[E]+Q)/r),C0=e.face[M].boxRaw.map((Q,E)=>((r-1)*z.face[M].boxRaw[E]+Q)/r),x0=e.face[M].annotations;if(Object.keys(z.face[M].annotations).length!==Object.keys(e.face[M].annotations).length)z.face[M].annotations=e.face[M].annotations,x0=z.face[M].annotations;else if(e.face[M].annotations)for(let Q of Object.keys(e.face[M].annotations))x0[Q]=(m=(x=(y=e.face[M])==null?void 0:y.annotations)==null?void 0:x[Q])!=null&&m[0]?e.face[M].annotations[Q].map((E,V)=>E.map((q,U0)=>((r-1)*z.face[M].annotations[Q][V][U0]+q)/r)):null;if(e.face[M].rotation){let Q={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Q.matrix=(f=e.face[M].rotation)==null?void 0:f.matrix,Q.angle={roll:((r-1)*(((T=(h=z.face[M].rotation)==null?void 0:h.angle)==null?void 0:T.roll)||0)+(((p=(g=e.face[M].rotation)==null?void 0:g.angle)==null?void 0:p.roll)||0))/r,yaw:((r-1)*(((k=(u=z.face[M].rotation)==null?void 0:u.angle)==null?void 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M=e.persons;if(!z.persons||M.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(M));else for(let T0=0;T0((r-1)*z.persons[T0].box[x0]+C0)/r)}e.gesture&&(z.gesture=e.gesture),z.width=e.width,z.height=e.height;let s=v();return c1=R.perfadd?c1+Math.round(s-n):Math.round(s-n),e.performance&&(z.performance={...e.performance,interpolate:c1}),z}var A0=Z(H());var O0;async function d1(e){return!O0||R.initial?O0=await L(e.segmentation.modelPath):e.debug&&b("cached model:",O0.modelUrl),O0}async function yo(e,t){var r;if(O0||(O0=await d1(t)),!(O0!=null&&O0.executor)||!((r=O0==null?void 0:O0.inputs)!=null&&r[0].shape))return null;let n={};n.resize=A0.image.resizeBilinear(e,[O0.inputs[0].shape?O0.inputs[0].shape[1]:0,O0.inputs[0].shape?O0.inputs[0].shape[2]:0],!1),n.norm=A0.div(n.resize,C.tf255),n.res=O0.execute(n.norm),n.squeeze=A0.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=A0.unstack(n.squeeze,2),n.fg=A0.softmax(n.fgRaw),n.mul=A0.mul(n.fg,C.tf255),n.expand=A0.expandDims(n.mul,2),n.output=A0.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let o;switch(t.segmentation.mode||"default"){case"default":n.input=A0.squeeze(e),n.concat=A0.concat([n.input,n.output],-1),o=A0.cast(n.concat,"int32");break;case"alpha":o=A0.cast(n.output,"int32");break;default:o=A0.tensor(0)}return Object.keys(n).forEach(s=>A0.dispose(n[s])),o}var Wt={};Se(Wt,{distance:()=>x1,find:()=>JA,similarity:()=>KA});function x1(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let s=0;s{if(e===0)return 1;let s=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(o-n);return 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n=e.keypoints.findIndex(r=>r.part===t[0]),o=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[0]r&&r.part===t[0]),o=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[1]c&&c.part===t[0]),r=e.keypoints.findIndex(c=>c&&c.part===t[1]),s=e.keypoints.findIndex(c=>c&&c.part===n[0]),A=e.keypoints.findIndex(c=>c&&c.part===n[1]);if(!e.keypoints[s]||!e.keypoints[A])continue;let a=e.keypoints[o]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[o].position[0]),Math.abs(e.keypoints[A].position[0]-e.keypoints[o].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[A].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0])]:[0,0];if(a[0]>a[1]||l[0]>l[1]){let c=e.keypoints[o];e.keypoints[o]=e.keypoints[r],e.keypoints[r]=c}}}function uo(e){for(let 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c=dt(t),d=[c[0]/n.shape[2],c[1]/n.shape[1]],i=Pe.image.rotateWithOffset(n,s,0,[d[0],d[1]]);A=v3(-s,c),a=v5(t,i,[o,o]),Pe.dispose(i)}else a=v5(t,n,[o,o]);else a=v5(t,n,[o,o]);return[s,A,a]}var mA=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},k3=(e,t)=>{let n=mA(e),o=p2(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var w3=6,xe,pt=null,ge=0,u2=null,E3=()=>ge;async function z3(e){var t;return R.initial&&(xe=null),xe?e.debug&&b("cached model:",xe.modelUrl):xe=await O((t=e.face.detector)==null?void 0:t.modelPath),ge=xe.executor&&xe.inputs[0].shape?xe.inputs[0].shape[2]:256,u2=L.scalar(ge,"int32"),pt=L.tensor2d(R3(ge)),xe}function pA(e){if(!pt||!u2)return L.zeros([0,0]);let t={};t.boxStarts=L.slice(e,[0,1],[-1,2]),t.centers=L.add(t.boxStarts,pt),t.boxSizes=L.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=L.div(t.boxSizes,u2),t.centersNormalized=L.div(t.centers,u2),t.halfBoxSize=L.div(t.boxSizesNormalized,C.tf2),t.starts=L.sub(t.centersNormalized,t.halfBoxSize),t.ends=L.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=L.mul(t.starts,u2),t.endNormalized=L.mul(t.ends,u2);let n=L.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>L.dispose(t[o])),n}async function S3(e,t){var c,d,i,y,x,m,f,h,T;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={},o=[0,0],r=[1,1];if((d=(c=t==null?void 0:t.face)==null?void 0:c.detector)!=null&&d.square){let g=Math.max(e.shape[2],e.shape[1]);o=[Math.floor((g-e.shape[2])/2),Math.floor((g-e.shape[1])/2)],n.padded=L.pad(e,[[0,0],[o[1],o[1]],[o[0],o[0]],[0,0]]),r=[e.shape[2]/g,e.shape[1]/g],o=[o[0]/ge,o[1]/ge]}else n.padded=e.clone();n.resized=L.image.resizeBilinear(n.padded,[ge,ge]),n.div=L.div(n.resized,C.tf127),n.normalized=L.sub(n.div,C.tf1);let s=xe==null?void 0:xe.execute(n.normalized);if(Array.isArray(s)&&s.length>2){let g=s.sort((p,u)=>p.size-u.size);n.concat384=L.concat([g[0],g[2]],2),n.concat512=L.concat([g[1],g[3]],2),n.concat=L.concat([n.concat512,n.concat384],1),n.batch=L.squeeze(n.concat,[0])}else Array.isArray(s)?n.batch=L.squeeze(s[0]):n.batch=L.squeeze(s);L.dispose(s),n.boxes=pA(n.batch),n.logits=L.slice(n.batch,[0,0],[-1,1]),n.sigmoid=L.sigmoid(n.logits),n.scores=L.squeeze(n.sigmoid),n.nms=await L.image.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((y=t.face.detector)==null?void 0:y.iouThreshold)||0,((x=t.face.detector)==null?void 0:x.minConfidence)||0);let A=await n.nms.array(),a=[],l=await n.scores.data();for(let g=0;g(((m=t.face.detector)==null?void 0:m.minConfidence)||0)){let u={};u.bbox=L.slice(n.boxes,[A[g],0],[1,-1]),u.slice=L.slice(n.batch,[A[g],w3-1],[1,-1]),u.squeeze=L.squeeze(u.slice),u.landmarks=L.reshape(u.squeeze,[w3,-1]);let k=await u.bbox.data(),P=[k[0]*r[0]-o[0],k[1]*r[1]-o[1],k[2]*r[0]-o[0],k[3]*r[1]-o[1]],N={startPoint:[P[0],P[1]],endPoint:[P[2],P[3]],landmarks:await u.landmarks.array(),confidence:p};u.anchor=L.slice(pt,[A[g],0],[1,2]);let B=await u.anchor.data(),J=g3(N,[(e.shape[2]||0)/ge,(e.shape[1]||0)/ge],B),G=ft(J,((f=t.face.detector)==null?void 0:f.scale)||1.4),K=mt(G);K.size[0]>(((h=t.face.detector)==null?void 0:h.minSize)||0)&&K.size[1]>(((T=t.face.detector)==null?void 0:T.minSize)||0)&&a.push(K),Object.keys(u).forEach(e0=>L.dispose(u[e0]))}}return Object.keys(n).forEach(g=>L.dispose(n[g])),a}var ye=Z(H());var K0,We=0,P5=ae.leftEyeLower0,k5=ae.rightEyeLower0,h2={leftBounds:[P5[0],P5[P5.length-1]],rightBounds:[k5[0],k5[k5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function O3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&b("cached model:",K0.modelUrl):K0=await O((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function ut(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},N3=(e,t,n,o,r,s=!1,A=2.3)=>{let a=mt(ft(T3([e[n],e[o]]),A)),l=p2(a),c=ye.image.cropAndResize(t,[[a.startPoint[1]/r,a.startPoint[0]/r,a.endPoint[1]/r,a.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let d=ye.image.flipLeftRight(c);ye.dispose(c),c=d}return{box:a,boxSize:l,crop:c}},I3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function C3(e,t,n,o){var N,B;if(!(K0!=null&&K0.executor))return e;let{box:r,boxSize:s,crop:A}=N3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0,((N=o.face.iris)==null?void 0:N.scale)||2.3),{box:a,boxSize:l,crop:c}=N3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0,((B=o.face.iris)==null?void 0:B.scale)||2.3),d=ye.concat([A,c]);ye.dispose(A),ye.dispose(c);let i=K0.execute(d);ye.dispose(d);let y=await i.data();ye.dispose(i);let x=y.slice(0,b2.numCoordinates*3),{rawCoords:m,iris:f}=I3(x,r,s,!0),h=y.slice(b2.numCoordinates*3),{rawCoords:T,iris:g}=I3(h,a,l,!1),p=uA(e);Math.abs(p)<30?(ut(e,m,"left",null),ut(e,T,"right",null)):p<1?ut(e,m,"left",["EyeUpper0","EyeLower0"]):ut(e,T,"right",["EyeUpper0","EyeLower0"]);let u=L3(e,f,"left"),k=L3(e,g,"right");return e.concat(u).concat(k)}async function D3(e,t){var s,A,a,l,c,d,i,y,x,m;let n={lips:await((A=(s=t.filter(f=>f.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(f=>f.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((d=(c=t.filter(f=>f.size===142))==null?void 0:c[0])==null?void 0:d.data()),irisR:await((y=(i=t.filter(f=>f.size===10))==null?void 0:i[1])==null?void 0:y.data()),eyeR:await((m=(x=t.filter(f=>f.size===142))==null?void 0:x[1])==null?void 0:m.data())};for(let f of Object.values(n))if(!f)return e;let o=e2.reduce((f,h)=>f+=e[h][2],0)/e2.length;for(let f=0;ff+=e[h][2],0)/t2.length;for(let f=0;fv()-Te.timestamp,o=Te.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||Te.boxes.length===0?(Te.boxes=await S3(e,t),Te.timestamp=v(),Te.skipped=0):Te.skipped++;let r=[],s=[],A=0,a=W2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!s0)t.debug&&b("face mesh detection requested, but model is not loaded");else{if((x=t.face.attention)!=null&&x.enabled&&!R.kernels.includes("atan2"))return t.face.attention.enabled=!1,De.dispose(P.tensor),r;let N=s0.execute(P.tensor),J=await N.find(G=>G.shape[G.shape.length-1]===1).data();if(P.faceScore=Math.round(100*J[0])/100,P.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=P.faceScore,t.face.mesh.keepInvalid){P.box=xt(p,e),P.boxRaw=yt(p,e),P.size=p.size,P.score=P.boxScore,P.mesh=p.landmarks,P.meshRaw=P.mesh.map(G=>[G[0]/(e.shape[2]||1),G[1]/(e.shape[1]||1),(G[2]||0)/a]);for(let G of Object.keys(_e))P.annotations[G]=[P.mesh[_e[G]]]}}else{let G=N.find(o0=>o0.shape[o0.shape.length-1]===1404),K=De.reshape(G,[-1,3]),e0=await K.array();De.dispose(K),(f=t.face.attention)!=null&&f.enabled?e0=await D3(e0,N):(h=t.face.iris)!=null&&h.enabled&&(e0=await C3(e0,P.tensor,W2,t)),P.mesh=M3(e0,p,u,k,W2),P.meshRaw=P.mesh.map(o0=>[o0[0]/(e.shape[2]||0),o0[1]/(e.shape[1]||0),(o0[2]||0)/a]);for(let o0 of Object.keys(ae))P.annotations[o0]=ae[o0].map(N0=>P.mesh[N0]);P.score=P.faceScore;let n0={...k3(P.mesh,p),confidence:p.confidence,landmarks:p.landmarks,size:p.size};P.box=xt(n0,e),P.boxRaw=yt(n0,e),P.size=n0.size,s.push(n0)}De.dispose(N)}P.score>(((T=t.face.detector)==null?void 0:T.minConfidence)||1)?r.push(P):De.dispose(P.tensor)}return Te.boxes=s,r}async function B3(e){var t,n,o,r,s,A;return R.initial&&(s0=null),(t=e.face.attention)!=null&&t.enabled&&(s0!=null&&s0.signature)&&Object.keys(((n=s0==null?void 0:s0.signature)==null?void 0:n.outputs)||{}).length<6&&(s0=null),s0?e.debug&&b("cached model:",s0.modelUrl):(o=e.face.attention)!=null&&o.enabled?s0=await O(e.face.attention.modelPath):s0=await O((r=e.face.mesh)==null?void 0:r.modelPath),W2=s0.executor&&((s=s0==null?void 0:s0.inputs)!=null&&s[0].shape)?(A=s0==null?void 0:s0.inputs)==null?void 0:A[0].shape[2]:256,s0}var H3=$e,G3=O2;var J0=Z(H());var z5=[],k0,ht=[],V3=0,Z3=0,E5=Number.MAX_SAFE_INTEGER,S5=!1;async function q3(e){var t,n,o;return R.initial&&(k0=null),k0?e.debug&&b("cached model:",k0.modelUrl):(k0=await O((t=e.face.emotion)==null?void 0:t.modelPath),S5=((o=(n=k0==null?void 0:k0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,S5?z5=["angry","disgust","fear","happy","neutral","sad","surprise"]:z5=["angry","disgust","fear","happy","sad","surprise","neutral"]),k0}async function j5(e,t,n,o){var A,a;if(!k0)return[];let r=E5<(((A=t.face.emotion)==null?void 0:A.skipFrames)||0),s=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-Z3;return t.skipAllowed&&s&&r&&V3===o&&ht[n]&&ht[n].length>0?(E5++,ht[n]):(E5=0,new Promise(async l=>{var d,i,y;let c=[];if((d=t.face.emotion)!=null&&d.enabled){let x={},m=k0!=null&&k0.inputs[0].shape?k0.inputs[0].shape[2]:0;if(((i=t.face.emotion)==null?void 0:i.crop)>0){let h=(y=t.face.emotion)==null?void 0:y.crop,T=[[h,h,1-h,1-h]];x.resize=J0.image.cropAndResize(e,T,[0],[m,m])}else x.resize=J0.image.resizeBilinear(e,[m,m],!1);S5?(x.mul=J0.mul(x.resize,255),x.normalize=J0.sub(x.mul,[103.939,116.779,123.68]),x.emotion=k0==null?void 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i={};i.crop=wt.image.resizeBilinear(e,[_0.inputs[0].shape[2],_0.inputs[0].shape[1]],!1),i.data=_0.execute(i.crop);let y=await i.data.data();c=Array.from(y),Object.keys(i).forEach(x=>wt.dispose(i[x]))}K5[n]=c,Mn=o,Pn=v(),l(c)})}var MA=e=>{let t=(i,y)=>Math.atan2(i[1]-y[1],i[0]-y[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],o=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=r?e.mesh[473]:e.mesh[468],A=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],a=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(A[0]-s[0])/a[0]-n[0],o*(s[1]-A[1])/a[1]-n[1]],c=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return c=Math.min(c,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:c}},zn=(e,t)=>{let n=f=>{let h=Math.sqrt(f[0]*f[0]+f[1]*f[1]+f[2]*f[2]);return 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Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?c=(T=e.config.face.antispoof)!=null&&T.enabled?C5(x[E].tensor||c0.tensor([]),e.config,E,x.length):0:(e.state="run:antispoof",n=v(),c=(g=e.config.face.antispoof)!=null&&g.enabled?await C5(x[E].tensor||c0.tensor([]),e.config,E,x.length):0,e.performance.antispoof=R.perfadd?(e.performance.antispoof||0)+Math.trunc(v()-n):Math.trunc(v()-n)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?d=(p=e.config.face.liveness)!=null&&p.enabled?D5(x[E].tensor||c0.tensor([]),e.config,E,x.length):0:(e.state="run:liveness",n=v(),d=(u=e.config.face.liveness)!=null&&u.enabled?await D5(x[E].tensor||c0.tensor([]),e.config,E,x.length):0,e.performance.liveness=R.perfadd?(e.performance.antispoof||0)+Math.trunc(v()-n):Math.trunc(v()-n)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?r=(k=e.config.face.gear)!=null&&k.enabled?H5(x[E].tensor||c0.tensor([]),e.config,E,x.length):null:(e.state="run:gear",n=v(),r=(P=e.config.face.gear)!=null&&P.enabled?await H5(x[E].tensor||c0.tensor([]),e.config,E,x.length):null,e.performance.gear=Math.trunc(v()-n)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(o=(N=e.config.face.ssrnet)!=null&&N.enabled?V5(x[E].tensor||c0.tensor([]),e.config,E,x.length):null,s=(B=e.config.face.ssrnet)!=null&&B.enabled?X5(x[E].tensor||c0.tensor([]),e.config,E,x.length):null):(e.state="run:ssrnet",n=v(),o=(J=e.config.face.ssrnet)!=null&&J.enabled?await V5(x[E].tensor||c0.tensor([]),e.config,E,x.length):null,s=(G=e.config.face.ssrnet)!=null&&G.enabled?await X5(x[E].tensor||c0.tensor([]),e.config,E,x.length):null,e.performance.ssrnet=Math.trunc(v()-n)),e.analyze("End SSRNet:"),e.analyze("Start 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I5(x[E].tensor||c0.tensor([]),e.config,E,x.length),e.performance.description=R.perfadd?(e.performance.description||0)+Math.trunc(v()-n):Math.trunc(v()-n)),e.analyze("End Description:"),e.config.async&&([o,s,A,a,l,i,r,c,d]=await Promise.all([o,s,A,a,l,i,r,c,d])),e.analyze("Finish Face:"),(N0=e.config.face.ssrnet)!=null&&N0.enabled&&o&&s&&(i={...i,age:o.age,gender:s.gender,genderScore:s.genderScore}),(M=e.config.face.gear)!=null&&M.enabled&&r&&(i={...i,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),(T0=e.config.face.mobilefacenet)!=null&&T0.enabled&&a&&(i.descriptor=a),(C0=e.config.face.insightface)!=null&&C0.enabled&&l&&(i.descriptor=l);let q=(x0=e.config.face.iris)!=null&&x0.enabled?Sn(x[E],t.shape[2]):0,U0=(Q=e.config.face.detector)!=null&&Q.return?c0.squeeze(x[E].tensor):null;c0.dispose(x[E].tensor),x[E].tensor&&delete x[E].tensor;let y0={...x[E],id:E};i.age&&(y0.age=i.age),i.gender&&(y0.gender=i.gender),i.genderScore&&(y0.genderScore=i.genderScore),i.descriptor&&(y0.embedding=i.descriptor),i.race&&(y0.race=i.race),A&&(y0.emotion=A),c&&(y0.real=c),d&&(y0.live=d),q>0&&(y0.distance=q),V&&(y0.rotation=V),U0&&(y0.tensor=U0),y.push(y0),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),y};var 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this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([n,o])}direction(t,n,o){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([n,o])}weight(t,n){this.weights[t]=n;let o=this.weights.reduce((r,s)=>r+s,0);this.weightsRelative=this.weights.map(r=>r*5/o)}matchAgainst(t,n){let o=0;for(let r in t){let s=t[r],A=this.curls[r];if(typeof A=="undefined"){o+=this.weightsRelative[r];continue}for(let[a,l]of A)if(s===a){o+=l*this.weightsRelative[r];break}}for(let r in n){let s=n[r],A=this.directions[r];if(typeof A=="undefined"){o+=this.weightsRelative[r];continue}for(let[a,l]of A)if(s===a){o+=l*this.weightsRelative[r];break}}return o/10}};var{thumb:fe,index:ke,middle:we,ring:A2,pinky:a2}=G0,{none:me,half:kA,full:pe}=He,{verticalUp:T2,verticalDown:_i,horizontalLeft:_5,horizontalRight:wA,diagonalUpRight:EA,diagonalUpLeft:v2,diagonalDownRight:$i,diagonalDownLeft:e7}=d0,Ge=new Be("thumbs 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G0.all)n[G0.getName(o)]={curl:He.getName(t.curls[o]),direction:d0.getName(t.directions[o])};return n}function Dn(e){let t=[];if(!e||e.length===0)return t;let n=Wn(e);for(let o of jn){let r=o.matchAgainst(n.curls,n.directions);r>=zA&&t.push({name:o.name,confidence:r})}return t}var Fn=e=>{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&o&&r&&o.position[1]l.part==="leftShoulder"),a=e[n].keypoints.find(l=>l.part==="rightShoulder");A&&a&&Math.abs(A.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${A.position[1]>a.position[1]?"left":"right"}`})}return t},Bn=e=>{if(!e)return[];let t=[];for(let n=0;n450){let o=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(o/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing 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T=Math.abs(e[A].mesh[145][1]-e[A].annotations.rightEyeIris[0][1])/e[A].box[3],g=Math.abs(e[A].mesh[374][1]-e[A].annotations.leftEyeIris[0][1])/e[A].box[3];(g<.01||T<.01||g>.022||T>.022)&&(x=!1),(g<.01||T<.01)&&t.push({iris:A,gesture:"looking down"}),(g>.022||T>.022)&&t.push({iris:A,gesture:"looking up"}),x&&t.push({iris:A,gesture:"looking center"})}return t},Gn=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=o.reduce((A,a)=>(A.position[2]||0)<(a.position[2]||0)?A:a);t.push({hand:n,gesture:`${r.name} forward`});let s=o.reduce((A,a)=>A.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function St(e,t=1.5){let n=D2(e),o=zt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function jt(e){let t=D2(e),n=zt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function LA(e){return 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o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let d=await c.box.data(),i=d.slice(0,2),y=d.slice(2,4),x=await c.palmLandmarks.array(),m={startPoint:i,endPoint:y,palmLandmarks:x,confidence:r[l]},f=Un(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(f),Object.keys(c).forEach(h=>W.dispose(c[h]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var $0=Z(H());var DA=5,_n=1.65,$n=[0,5,9,13,17,1,2],FA=0,BA=2,eo=0,It=class{constructor(t,n){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>t1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return St(jt(r),DA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=St(jt(n),_n);o.palmLandmarks=[];for(let r=0;r<$n.length;r++)o.palmLandmarks.push(t[$n[r]].slice(0,2));return o}transformRawCoords(t,n,o,r){let s=zt(n),A=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],a=t.map(x=>[A[0]*(x[0]-this.inputSize/2),A[1]*(x[1]-this.inputSize/2),A[2]*x[2]]),l=e1(o,[0,0]),c=a.map(x=>[...t1(x,l),x[2]]),d=Kn(r),i=[...D2(n),1],y=[qe(i,d[0]),qe(i,d[1])];return c.map(x=>[Math.trunc(x[0]+y[0]),Math.trunc(x[1]+y[1]),Math.trunc(x[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>v()-eo,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A?this.skipped++:(r=await this.handDetector.predict(t,n),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,f,d,m),B=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...B,confidence:u};let J={landmarks:N,confidence:u,boxConfidence:c.confidence,fingerConfidence:u,box:{topLeft:B.startPoint,bottomRight:B.endPoint}};a.push(J)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let d=St(jt(c),_n),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var to={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,n1;function GA(){let e=l2?new Nt(l2):void 0;e&&c2&&(n1=new It(e,c2))}async function o1(e,t){n1||GA();let n=await n1.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let d of A)d[0]a[2]&&(a[2]=d[0]),d[1]>a[3]&&(a[3]=d[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else 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a=$.unstack(o.scores,1);$.dispose(a[so]),a.splice(so,1),o.filtered=$.stack(a,1),$.dispose(a),o.max=$.max(o.filtered,1),o.argmax=$.argMax(o.filtered,1);let l=0;o.nms=await $.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),d=await o.max.data(),i=await o.argmax.data();for(let y of Array.from(c)){let x=$.slice(o.boxes,y,1),m=await x.data();$.dispose(x);let f=[m[1],m[0],m[3]-m[1],m[2]-m[0]],h=at(f,XA),T=[Math.trunc(f[0]*Ee[0]),Math.trunc(f[1]*Ee[1]),Math.trunc(f[2]*Ee[0]),Math.trunc(f[3]*Ee[1])],g=d[y],p=ZA[i[y]],u={id:l++,score:g,box:T,boxRaw:h,label:p};n.push(u)}return Object.keys(o).forEach(y=>$.dispose(o[y])),n.sort((y,x)=>x.score-y.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function s1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=$.image.cropAndResize(e,[s],[0],[Xe[1][0],Xe[1][1]],"bilinear"),r.div=$.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=$.reshape(r.keypoints,[-1,3]);let d=(await r.reshaped.array()).map(i=>[i[0]/Xe[1][1],i[1]/Xe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=d.map(i=>[Ee[0]*(i[0]+t.boxRaw[0]),Ee[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Et(o.keypoints);for(let i of Object.keys(ao))o.annotations[i]=ao[i].map(y=>o.landmarks&&o.keypoints[y]?o.keypoints[y]:null)}Object.keys(r).forEach(l=>$.dispose(r[l]))}return o}async function A1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];Ee=[e.shape[2]||0,e.shape[1]||0],Lt++;let n=(t.hand.skipTime||0)>v()-r1,o=Lt<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-r1,l=Lt<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(d=>s1(e,d,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(d=>s1(e,d,t))):(m0.boxes=await UA(e,t),r1=v(),m0.hands=await Promise.all(m0.boxes.map(d=>s1(e,d,t))),Lt=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[d].fingerScore&&m0.hands[d].fingerScore>(t.hand.minConfidence||0)){let y=at(i.box,Ao),x=at(i.boxRaw,Ao);m0.boxes.push({...c[d],box:y,boxRaw:x})}}for(let d=0;d({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var F2={};Se(F2,{connected:()=>Ct,horizontal:()=>a1,kpt:()=>Ot,relative:()=>l1,vertical:()=>i1});var Ot=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],a1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],i1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],l1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Ct={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=ve(),c1=0;function xo(e,t){var A,a,l,c,d,i,y,x,m,f,h,T,g,p,u,k,P,N,B,J,G,K,e0,n0,o0,N0;let n=v();if(!e)return ve();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let M=0;M((r-1)*z.body[M].box[q]+V)/r),C0=e.body[M].boxRaw.map((V,q)=>((r-1)*z.body[M].boxRaw[q]+V)/r),x0=e.body[M].keypoints.map((V,q)=>{var 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M=0;M((r-1)*z.face[M].box[E]+Q)/r),C0=e.face[M].boxRaw.map((Q,E)=>((r-1)*z.face[M].boxRaw[E]+Q)/r),x0=e.face[M].annotations;if(Object.keys(z.face[M].annotations).length!==Object.keys(e.face[M].annotations).length)z.face[M].annotations=e.face[M].annotations,x0=z.face[M].annotations;else if(e.face[M].annotations)for(let Q of Object.keys(e.face[M].annotations))x0[Q]=(m=(x=(y=e.face[M])==null?void 0:y.annotations)==null?void 0:x[Q])!=null&&m[0]?e.face[M].annotations[Q].map((E,V)=>E.map((q,U0)=>((r-1)*z.face[M].annotations[Q][V][U0]+q)/r)):null;if(e.face[M].rotation){let Q={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Q.matrix=(f=e.face[M].rotation)==null?void 0:f.matrix,Q.angle={roll:((r-1)*(((T=(h=z.face[M].rotation)==null?void 0:h.angle)==null?void 0:T.roll)||0)+(((p=(g=e.face[M].rotation)==null?void 0:g.angle)==null?void 0:p.roll)||0))/r,yaw:((r-1)*(((k=(u=z.face[M].rotation)==null?void 0:u.angle)==null?void 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M=e.persons;if(!z.persons||M.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(M));else for(let T0=0;T0((r-1)*z.persons[T0].box[x0]+C0)/r)}e.gesture&&(z.gesture=e.gesture),z.width=e.width,z.height=e.height;let s=v();return c1=R.perfadd?c1+Math.round(s-n):Math.round(s-n),e.performance&&(z.performance={...e.performance,interpolate:c1}),z}var A0=Z(H());var L0;async function d1(e){return!L0||R.initial?L0=await O(e.segmentation.modelPath):e.debug&&b("cached model:",L0.modelUrl),L0}async function yo(e,t){var r;if(L0||(L0=await d1(t)),!(L0!=null&&L0.executor)||!((r=L0==null?void 0:L0.inputs)!=null&&r[0].shape))return null;let n={};n.resize=A0.image.resizeBilinear(e,[L0.inputs[0].shape?L0.inputs[0].shape[1]:0,L0.inputs[0].shape?L0.inputs[0].shape[2]:0],!1),n.norm=A0.div(n.resize,C.tf255),n.res=L0.execute(n.norm),n.squeeze=A0.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=A0.unstack(n.squeeze,2),n.fg=A0.softmax(n.fgRaw),n.mul=A0.mul(n.fg,C.tf255),n.expand=A0.expandDims(n.mul,2),n.output=A0.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let o;switch(t.segmentation.mode||"default"){case"default":n.input=A0.squeeze(e),n.concat=A0.concat([n.input,n.output],-1),o=A0.cast(n.concat,"int32");break;case"alpha":o=A0.cast(n.output,"int32");break;default:o=A0.tensor(0)}return Object.keys(n).forEach(s=>A0.dispose(n[s])),o}var Wt={};Se(Wt,{distance:()=>x1,find:()=>JA,similarity:()=>KA});function x1(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let s=0;s{if(e===0)return 1;let s=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(o-n);return Math.round(100*Math.max(Math.min(s,1),0))/100};function KA(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let o=x1(e,t,n);return mo(o,n.order||2,n.min||0,n.max||1)}function JA(e,t,n={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let o=Number.MAX_SAFE_INTEGER,r=-1;for(let A=0;AV2,validateModel:()=>Zt});var B2=Z(H());var Ue=Z(H());var po=.005,ee={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function y1(e){for(let t of a1){let n=e.keypoints.findIndex(r=>r.part===t[0]),o=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[0]r&&r.part===t[0]),o=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[1]c&&c.part===t[0]),r=e.keypoints.findIndex(c=>c&&c.part===t[1]),s=e.keypoints.findIndex(c=>c&&c.part===n[0]),A=e.keypoints.findIndex(c=>c&&c.part===n[1]);if(!e.keypoints[s]||!e.keypoints[A])continue;let a=e.keypoints[o]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[o].position[0]),Math.abs(e.keypoints[A].position[0]-e.keypoints[o].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[A].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0])]:[0,0];if(a[0]>a[1]||l[0]>l[1]){let c=e.keypoints[o];e.keypoints[o]=e.keypoints[r],e.keypoints[r]=c}}}function uo(e){for(let 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s=r.getContext("2d");s&&s.drawImage(o,0,0);let A=await e.image(r,!0),a=A.tensor?await e.detect(A.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function ua(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(qt):n=t(Xt);let o;if("node"in a0&&a0.getBackend()==="tensorflow"){let r=a0.node.decodeJpeg(n),s=a0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&b("Warmup tfjs-node not loaded");return o}async function ha(e){let t;return typeof createImageBitmap=="function"?t=await ma(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await pa(e):t=await ua(e),t}async function ba(e){var a,l,c,d;if(!a0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=a0.getBackend(),n=a0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;a0.env().set("ENGINE_COMPILE_ONLY",!0);let o=a0.engine().state.numTensors,r=[];for(let[i,y]of Object.entries(e.models.models)){if(!y)continue;let x=y!=null&&y.modelSignature&&((l=(a=y==null?void 0:y.inputs)==null?void 0:a[0])!=null&&l.shape)?[...y.inputs[0].shape]:[1,64,64,3],m=y!=null&&y.modelSignature&&((d=(c=y==null?void 0:y.inputs)==null?void 0:c[0])!=null&&d.dtype)?y.inputs[0].dtype:"float32";for(let h=0;ha0.dispose(T)):a0.dispose(h)}catch(h){e.config.debug&&b("compile fail model:",i)}a0.dispose(f)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&b("compile pass:",{models:r,kernels:s.length}),a0.env().set("ENGINE_COMPILE_ONLY",!1);let A=a0.engine().state.numTensors;A-o>0&&b("tensor leak:",A-o)}async function Bo(e,t){await O2(e,!1);let n=v();return e.state="warmup",t&&(e.config=i0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?ve():new Promise(async o=>{await e.models.load(),await a0.ready(),await ba(e);let r=await ha(e),s=v();e.config.debug&&b("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var P2,Z2,q2,Ut,Ye,E1=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env",R);w(this,"draw",rt);w(this,"match",Wt);w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");ue(this,P2);ue(this,Z2);ue(this,q2);w(this,"analyze",(...t)=>{if(!Y0(this,Z2))return;let n=this.tf.engine().state.numTensors,o=Y0(this,P2);Me(this,P2,n);let r=n-o;r!==0&&b(...t,r)});ue(this,Ut,t=>{if(!Y0(this,q2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof de.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});w(this,"webcam",new Q2);w(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(de.version.tfjs||de.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=n5,Object.defineProperty(this,"version",{value:n5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=i0(this.config,t)),U1(this.config),this.tf=de,this.state="idle",Me(this,P2,0),Me(this,Z2,!1),Me(this,q2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new V2(this),l5(),this.result=ve(),this.process={tensor:null,canvas:null},this.faceTriangulation=H3,this.faceUVMap=G3,Zt(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&b(`version: ${this.version}`),this.config.debug&&b(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&b("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,e5(),R.initial=!0}validate(t){let n=Qt(Ke,t||this.config);return n.length===0&&(this.config=i0(this.config,t)),n}now(){return v()}image(t,n=!1){return K2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=i0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await K2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await Lo(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await yo(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await Wo(o.tensor,this.config)),de.dispose(o.tensor),r}compare(t,n){return X1(this.config,t,n)}async init(){await O2(this,!0),await this.tf.ready(),e5()}async load(t){this.state="load";let 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R.Image;else{t(void 0);return}o.onload=async()=>{let r=Ae(o.naturalWidth,o.naturalHeight);if(!r)b("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(o,0,0);let A=await e.image(r,!0),a=A.tensor?await e.detect(A.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function ua(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(qt):n=t(Xt);let o;if("node"in a0&&a0.getBackend()==="tensorflow"){let r=a0.node.decodeJpeg(n),s=a0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&b("Warmup tfjs-node not loaded");return o}async function ha(e){let t;return typeof createImageBitmap=="function"?t=await ma(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await pa(e):t=await ua(e),t}async function ba(e){var a,l,c,d;if(!a0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=a0.getBackend(),n=a0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;a0.env().set("ENGINE_COMPILE_ONLY",!0);let o=a0.engine().state.numTensors,r=[];for(let[i,y]of Object.entries(e.models.models)){if(!y)continue;let x=y!=null&&y.modelSignature&&((l=(a=y==null?void 0:y.inputs)==null?void 0:a[0])!=null&&l.shape)?[...y.inputs[0].shape]:[1,64,64,3],m=y!=null&&y.modelSignature&&((d=(c=y==null?void 0:y.inputs)==null?void 0:c[0])!=null&&d.dtype)?y.inputs[0].dtype:"float32";for(let h=0;ha0.dispose(T)):a0.dispose(h)}catch(h){e.config.debug&&b("compile fail model:",i)}a0.dispose(f)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&b("compile pass:",{models:r,kernels:s.length}),a0.env().set("ENGINE_COMPILE_ONLY",!1);let A=a0.engine().state.numTensors;A-o>0&&b("tensor leak:",A-o)}async function Bo(e,t){await L2(e,!1);let n=v();return e.state="warmup",t&&(e.config=i0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?ve():new Promise(async o=>{await e.models.load(),await a0.ready(),await ba(e);let r=await ha(e),s=v();e.config.debug&&b("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var P2,Z2,q2,Ut,Ye,E1=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env",R);w(this,"draw",rt);w(this,"match",Wt);w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");ue(this,P2);ue(this,Z2);ue(this,q2);w(this,"analyze",(...t)=>{if(!Y0(this,Z2))return;let n=this.tf.engine().state.numTensors,o=Y0(this,P2);Me(this,P2,n);let r=n-o;r!==0&&b(...t,r)});ue(this,Ut,t=>{if(!Y0(this,q2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof de.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});w(this,"webcam",new Q2);w(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(de.version.tfjs||de.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=n5,Object.defineProperty(this,"version",{value:n5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=i0(this.config,t)),U1(this.config),this.tf=de,this.state="idle",Me(this,P2,0),Me(this,Z2,!1),Me(this,q2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new V2(this),l5(),this.result=ve(),this.process={tensor:null,canvas:null},this.faceTriangulation=H3,this.faceUVMap=G3,Zt(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&b(`version: ${this.version}`),this.config.debug&&b(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&b("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,e5(),R.initial=!0}validate(t){let n=Qt(Ke,t||this.config);return n.length===0&&(this.config=i0(this.config,t)),n}now(){return v()}image(t,n=!1){return K2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=i0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await K2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await Oo(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await yo(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await 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n=v(),o=await Bo(this,t),r=v();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var h,T,g,p,u,k,P,N,B,J,G,K,e0,n0,o0,N0,M,T0,C0,x0,Q;this.state="config";let r;this.config=i0(this.config,n),this.state="check";let s=Y0(this,Ut).call(this,t);s&&(b(s,t),this.emit("error"),o(ve(s)));let A=v();await this.load(),r=v(),this.state="image";let a=await K2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&b("could not convert input to tensor"),this.emit("error"),o(ve("could not convert input to tensor"));return}this.emit("image"),r=v(),this.config.skipAllowed=await q1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Check Changed:");let l=[],c=[],d=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?Q5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=v(),l=this.config.face.enabled?await Q5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let y=this.config.body.maxDetected===-1?i0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((h=this.config.body.modelPath)!=null&&h.includes("posenet")?c=this.config.body.enabled?v1(a.tensor,y):[]:(T=this.config.body.modelPath)!=null&&T.includes("blazepose")?c=this.config.body.enabled?f5(a.tensor,y):[]:(g=this.config.body.modelPath)!=null&&g.includes("efficientpose")?c=this.config.body.enabled?T5(a.tensor,y):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(c=this.config.body.enabled?m1(a.tensor,y):[]),this.performance.body&&delete this.performance.body):(r=v(),(u=this.config.body.modelPath)!=null&&u.includes("posenet")?c=this.config.body.enabled?await v1(a.tensor,y):[]:(k=this.config.body.modelPath)!=null&&k.includes("blazepose")?c=this.config.body.enabled?await f5(a.tensor,y):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await T5(a.tensor,y):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(c=this.config.body.enabled?await m1(a.tensor,y):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let x=this.config.hand.maxDetected===-1?i0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((J=(B=this.config.hand.detector)==null?void 0:B.modelPath)!=null&&J.includes("handdetect")?d=this.config.hand.enabled?o1(a.tensor,x):[]:(K=(G=this.config.hand.detector)==null?void 0:G.modelPath)!=null&&K.includes("handtrack")&&(d=this.config.hand.enabled?A1(a.tensor,x):[]),this.performance.hand&&delete this.performance.hand):(r=v(),(n0=(e0=this.config.hand.detector)==null?void 0:e0.modelPath)!=null&&n0.includes("handdetect")?d=this.config.hand.enabled?await o1(a.tensor,x):[]:(N0=(o0=this.config.hand.detector)==null?void 0:o0.modelPath)!=null&&N0.includes("handtrack")&&(d=this.config.hand.enabled?await A1(a.tensor,x):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((M=this.config.object.modelPath)!=null&&M.includes("nanodet")?i=this.config.object.enabled?u1(a.tensor,this.config):[]:(T0=this.config.object.modelPath)!=null&&T0.includes("centernet")&&(i=this.config.object.enabled?u5(a.tensor,this.config):[]),this.performance.object&&delete 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p=Math.cos(g),u=Math.sin(g),k=.213,P=.715,N=.072;T.colorMatrix([k+p*(1-k)+u*-k,P+p*-P+u*-P,N+p*-N+u*(1-N),0,0,k+p*-k+u*.143,P+p*(1-P)+u*.14,N+p*-N+u*-.283,0,0,k+p*-k+u*-(1-k),P+p*-P+u*P,N+p*(1-N)+u*N,0,0,0,0,0,1,0])},desaturateLuminance:()=>{T.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{T.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{T.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{T.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{T.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{T.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{T.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{T.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:g=>{let p=new Float32Array(g),u=1/l.width,k=1/l.height,P=h(G1);P&&(i.uniform1fv(P.uniform.m,p),i.uniform2f(P.uniform.px,u,k),f())},detectEdges:()=>{T.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{T.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{T.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:g=>{let p=g||1;T.convolution.call(this,[0,-1*p,0,-1*p,1+4*p,-1*p,0,-1*p,0])},emboss:g=>{let p=g||1;T.convolution.call(this,[-2*p,-1*p,0,-1*p,1,1*p,0,1*p,2*p])},blur:g=>{let p=g/7/l.width,u=g/7/l.height,k=h(H1);k&&(i.uniform2f(k.uniform.px,0,u),f(d.INTERMEDIATE),i.uniform2f(k.uniform.px,p,0),f())},pixelate:g=>{let p=g/l.width,u=g/l.height,k=h(B1);k&&(i.uniform2f(k.uniform.size,p,u),f())}};this.add=function(g){let p=Array.prototype.slice.call(arguments,1),u=T[g];s.push({func:u,args:p})},this.reset=function(){s=[]},this.get=function(){return s},this.apply=function(g){y(g.width,g.height),e=0,t||(t=i.createTexture()),i.bindTexture(i.TEXTURE_2D,t),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.NEAREST),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,g);for(let p=0;pd.data())),A=Math.max(s[0][0],s[1][0],s[2][0]),l=(A>1?255:1)/A,c;if(l>1){let d=[U.sub(n[0],o[0]),U.sub(n[1],o[1]),U.sub(n[2],o[2])],i=[U.sub(r[0],o[0]),U.sub(r[1],o[1]),U.sub(r[2],o[2])],y=[U.mul(d[0],l),U.mul(d[1],l),U.mul(d[2],l)],x=U.stack([y[0],y[1],y[2]],2);c=U.reshape(x,[1,t.shape[0]||0,t.shape[1]||0,3]),U.dispose([...d,...i,...y,x])}else c=U.expandDims(t,0);return U.dispose([...n,...o,...r,n,t,e]),c}var q2=3840,M0=null,P0=null,y2=null,t0,ne={inputSum:0,cacheDiff:1,sumMethod:0,inputTensor:void 0};function $t(){ne.inputSum=0,ne.cacheDiff=1,ne.sumMethod=0,ne.inputTensor=void 0}function Ae(e,t){let n;if(R.browser)if(R.worker){if(typeof OffscreenCanvas=="undefined")throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");n=new OffscreenCanvas(e,t)}else if(typeof document!="undefined")n=document.createElement("canvas"),n.width=e,n.height=t;else if(typeof navigator!="undefined"&&navigator.product==="ReactNative")if(typeof R.Canvas!="undefined")n=new R.Canvas(e,t);else if(typeof globalThis.Canvas!="undefined")n=new globalThis.Canvas(e,t);else throw new Error("canvas error: attempted to use canvas in react-native without canvas support installed");else throw new Error("canvas error: attempted to run in browser but DOM is not defined");else typeof R.Canvas!="undefined"?n=new R.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t));return n}function X2(e,t){let n=t||Ae(e.width,e.height);return n.getContext("2d").drawImage(e,0,0),n}async function U2(e,t,n=!0){var y,x,m;if(!e)return t.debug&&b("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof I.Tensor)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input error: type not recognized");if(e instanceof I.Tensor){let f=null;if(e.isDisposedInternal)throw new Error("input error: attempted to use tensor but it is disposed");if(!e.shape)throw new Error("input error: attempted to use tensor without a shape");if(e.shape.length===3){if(e.shape[2]===3)f=I.expandDims(e,0);else if(e.shape[2]===4){let h=I.slice3d(e,[0,0,0],[-1,-1,3]);f=I.expandDims(h,0),I.dispose(h)}}else e.shape.length===4&&(e.shape[3]===3?f=I.clone(e):e.shape[3]===4&&(f=I.slice4d(e,[0,0,0,0],[-1,-1,-1,3])));if(f==null||f.shape.length!==4||f.shape[0]!==1||f.shape[3]!==3)throw new Error(`input error: attempted to use tensor with unrecognized shape: ${e.shape.toString()}`);if(f.dtype==="int32"){let h=I.cast(f,"float32");I.dispose(f),f=h}return{tensor:f,canvas:t.filter.return?P0:null}}if(typeof e.readyState!="undefined"&&e.readyState<=2)return t.debug&&b("input stream is not ready"),{tensor:null,canvas:M0};let o=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,r=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!o||!r)return t.debug&&b("cannot determine input dimensions"),{tensor:null,canvas:M0};let s=o,A=r;if(s>q2&&(s=q2,A=Math.trunc(s*r/o)),A>q2&&(A=q2,s=Math.trunc(A*o/r)),(((y=t.filter)==null?void 0:y.width)||0)>0?s=t.filter.width:(((x=t.filter)==null?void 0:x.height)||0)>0&&(s=o*((t.filter.height||0)/r)),(t.filter.height||0)>0?A=t.filter.height:(t.filter.width||0)>0&&(A=r*((t.filter.width||0)/o)),!s||!A)throw new Error("input error: cannot determine dimension");(!M0||M0.width!==s||M0.height!==A)&&(M0=Ae(s,A));let a=M0.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?a.putImageData(e,0,0):t.filter.flip&&typeof a.translate!="undefined"?(a.translate(o,0),a.scale(-1,1),a.drawImage(e,0,0,o,r,0,0,M0.width,M0.height),a.setTransform(1,0,0,1,0,0)):a.drawImage(e,0,0,o,r,0,0,M0.width,M0.height),(!P0||M0.width!==P0.width||M0.height!==P0.height)&&(P0=Ae(M0.width,M0.height)),t.filter.enabled&&R.webgl.supported?(t0||(t0=R.browser?new V1:null),R.filter=!!t0,t0!=null&&t0.add?(t0.reset(),t.filter.brightness!==0&&t0.add("brightness",t.filter.brightness),t.filter.contrast!==0&&t0.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&t0.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&t0.add("blur",t.filter.blur),t.filter.saturation!==0&&t0.add("saturation",t.filter.saturation),t.filter.hue!==0&&t0.add("hue",t.filter.hue),t.filter.negative&&t0.add("negative"),t.filter.sepia&&t0.add("sepia"),t.filter.vintage&&t0.add("brownie"),t.filter.sepia&&t0.add("sepia"),t.filter.kodachrome&&t0.add("kodachrome"),t.filter.technicolor&&t0.add("technicolor"),t.filter.polaroid&&t0.add("polaroid"),t.filter.pixelate!==0&&t0.add("pixelate",t.filter.pixelate),((m=t0.get())==null?void 0:m.length)>1?P0=t0.apply(M0):P0=t0.draw(M0)):(t.debug&&b("input process error: cannot initialize filters"),R.webgl.supported=!1,t.filter.enabled=!1,X2(M0,P0))):(X2(M0,P0),t0&&(t0=null),R.filter=!!t0),!n)return{tensor:null,canvas:P0};if(!P0)throw new Error("canvas error: cannot create output");let l,c=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(R.browser&&I.browser)l=I.browser?I.browser.fromPixels(e):null;else{c=e.data.length/e.height/e.width;let f=new Uint8Array(e.data.buffer);l=I.tensor(f,[e.height,e.width,c],"int32")}else if((!y2||P0.width!==y2.width||P0.height!==y2.height)&&(y2=Ae(P0.width,P0.height)),I.browser&&R.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=I.browser.fromPixels(P0):(y2=X2(P0),l=I.browser.fromPixels(y2));else{let T=X2(P0).getContext("2d").getImageData(0,0,s,A);c=T.data.length/s/A;let g=new Uint8Array(T.data.buffer);l=I.tensor(g,[s,A,c])}if(c===4){let f=I.slice3d(l,[0,0,0],[-1,-1,3]);I.dispose(l),l=f}if(!l)throw new Error("input error: cannot create tensor");let d=I.cast(l,"float32"),i=t.filter.equalization?await Z2(d):I.expandDims(d,0);if(I.dispose([l,d]),t.filter.autoBrightness){let f=I.max(i),h=await f.data();t.filter.brightness=h[0]>1?1-h[0]/255:1-h[0],I.dispose(f)}return{tensor:i,canvas:t.filter.return?P0:null}}async function Z1(e,t){let n=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>3840||t.shape[2]>2160)return n;if(!ne.inputTensor)ne.inputTensor=I.clone(t);else if(ne.inputTensor.shape[1]!==t.shape[1]||ne.inputTensor.shape[2]!==t.shape[2])I.dispose(ne.inputTensor),ne.inputTensor=I.clone(t);else{let o={};o.diff=I.sub(t,ne.inputTensor),o.squared=I.mul(o.diff,o.diff),o.sum=I.sum(o.squared);let s=(await o.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;I.dispose([ne.inputTensor,o.diff,o.squared,o.sum]),ne.inputTensor=I.clone(t),n=s<=(e.cacheSensitivity||0)}return n}async function q1(e,t,n){let o={};if(!t||!n||t.shape.length!==4||t.shape.length!==n.shape.length)return e.debug||b("invalid input tensor or tensor shapes do not match:",t.shape,n.shape),0;if(t.shape[0]!==1||n.shape[0]!==1||t.shape[3]!==3||n.shape[3]!==3)return e.debug||b("input tensors must 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0,flags:[]});w(this,"kernels",[]);ue(this,z2);ue(this,S2);ue(this,j2);if(this.browser=typeof navigator!="undefined"&&typeof navigator.appVersion!="undefined",this.node=typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined",this.tfjs={version:W0.version["tfjs-core"]},this.offscreen=typeof OffscreenCanvas!="undefined",this.initial=!0,this.worker=this.browser&&this.offscreen?typeof WorkerGlobalScope!="undefined":void 0,typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined"){let t=navigator.userAgent||"",n=t.match(/\(([^()]+)\)/g);if(n!=null&&n[0]){let o=n[0].match(/\(([^()]+)\)/g);this.platform=o!=null&&o[0]?o[0].replace(/\(|\)/g,""):"",this.agent=t.replace(n[0],""),this.platform[1]&&(this.agent=this.agent.replace(n[1],"")),this.agent=this.agent.replace(/ /g," ")}}else typeof process!="undefined"&&(this.platform=`${process.platform} ${process.arch}`,this.agent=`NodeJS ${process.version}`)}get Canvas(){return Y0(this,z2)}set Canvas(t){Me(this,z2,t),globalThis.Canvas=t}get Image(){return Y0(this,S2)}set Image(t){Me(this,S2,t),globalThis.Image=t}get ImageData(){return Y0(this,j2)}set ImageData(t){Me(this,j2,t),globalThis.ImageData=t}async updateBackend(){this.backends=Object.keys(W0.engine().registryFactory);try{this.tensorflow={version:W0.backend().binding?W0.backend().binding.TF_Version:void 0,gpu:W0.backend().binding?W0.backend().binding.isUsingGpuDevice():void 0}}catch(o){}this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&(this.wasm.simd=await W0.env().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await W0.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Ae(100,100),n=t?t.getContext("webgl2"):void 0;this.webgl.supported=typeof n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&n&&(this.webgl.version=n.getParameter(n.VERSION),this.webgl.vendor=n.getParameter(n.VENDOR),this.webgl.renderer=n.getParameter(n.RENDERER),this.webgl.shader=n.getParameter(n.SHADING_LANGUAGE_VERSION)),this.webgpu.supported=this.browser&&typeof navigator!="undefined"&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{if(this.webgpu.supported){let o=await navigator.gpu.requestAdapter();o&&("requestAdapterInfo"in o?this.webgpu.adapter=await o.requestAdapterInfo():this.webgpu.adapter=await o.info)}}catch(o){this.webgpu.supported=!1}try{this.kernels=W0.getKernelsForBackend(W0.getBackend()).map(o=>o.kernelName.toLowerCase())}catch(o){}}updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}};z2=new WeakMap,S2=new WeakMap,j2=new WeakMap;var R=new E2;var 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p=new Float32Array(g),u=1/l.width,k=1/l.height,P=h(G1);P&&(i.uniform1fv(P.uniform.m,p),i.uniform2f(P.uniform.px,u,k),f())},detectEdges:()=>{T.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{T.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{T.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:g=>{let p=g||1;T.convolution.call(this,[0,-1*p,0,-1*p,1+4*p,-1*p,0,-1*p,0])},emboss:g=>{let p=g||1;T.convolution.call(this,[-2*p,-1*p,0,-1*p,1,1*p,0,1*p,2*p])},blur:g=>{let p=g/7/l.width,u=g/7/l.height,k=h(H1);k&&(i.uniform2f(k.uniform.px,0,u),f(d.INTERMEDIATE),i.uniform2f(k.uniform.px,p,0),f())},pixelate:g=>{let p=g/l.width,u=g/l.height,k=h(B1);k&&(i.uniform2f(k.uniform.size,p,u),f())}};this.add=function(g){let p=Array.prototype.slice.call(arguments,1),u=T[g];s.push({func:u,args:p})},this.reset=function(){s=[]},this.get=function(){return 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Y0(this,z2)}set Canvas(t){Me(this,z2,t),globalThis.Canvas=t}get Image(){return Y0(this,S2)}set Image(t){Me(this,S2,t),globalThis.Image=t}get ImageData(){return Y0(this,j2)}set ImageData(t){Me(this,j2,t),globalThis.ImageData=t}async updateBackend(){this.backends=Object.keys(W0.engine().registryFactory);try{this.tensorflow={version:W0.backend().binding?W0.backend().binding.TF_Version:void 0,gpu:W0.backend().binding?W0.backend().binding.isUsingGpuDevice():void 0}}catch(o){}this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&(this.wasm.simd=await W0.env().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await W0.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Ae(100,100),n=t?t.getContext("webgl2"):void 0;this.webgl.supported=typeof 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t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=Sw(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Sw(e)){let{kernelName:f,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=Xp(f,this.backendName);E(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let S=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,S);o=this.saveTensorsForBackwardMode(k)}return S}}else{let{forwardFunc:f}=e,h=g=>{n&&(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return 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s={x:o},a={blockShape:e,crops:t};return T.runKernel(Js,s,a)}var fd=N({batchToSpaceND_:SH});function qk(r){let e;return r.rank===0||r.rank===1?e=W(r,[1,1,1,r.size]):r.rank===2?e=W(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?e=W(r,[1,r.shape[0],r.shape[1],r.shape[2]]):e=r,e}function IH(r,e,t,o,n,s){s==null&&(s=.001);let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),p=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;o!=null&&(c=v(o,"offset","batchNorm")),E(i.rank===p.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),E(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),E(u==null||i.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let m={x:qk(a),scale:u,offset:c,mean:i,variance:p},d={varianceEpsilon:s},f=T.runKernel(In,m,d);return W(f,a.shape)}var nu=N({batchNorm_:IH});function vH(r,e,t,o,n,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),p=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;return o!=null&&(c=v(o,"offset","batchNorm")),E(a.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`),E(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),E(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),nu(a,i,p,c,u,s)}var jk=N({batchNorm2d_:vH});function kH(r,e,t,o,n,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),p=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;return o!=null&&(c=v(o,"offset","batchNorm")),E(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),E(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),E(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),nu(a,i,p,c,u,s)}var Xk=N({batchNorm3d_:kH});function NH(r,e,t,o,n,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),p=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;return o!=null&&(c=v(o,"offset","batchNorm")),E(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),E(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),E(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),nu(a,i,p,c,u,s)}var Yk=N({batchNorm4d_:NH});function TH(r,e,t){let o=v(r,"x","bincount"),n=v(e,"weights","bincount");E(o.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${o.dtype}`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(n.size===o.size||n.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`);let s={x:o,weights:n},a={size:t};return T.runKernel(Jo,s,a)}var hd=N({bincount_:TH});function _H(r,e){let t=v(r,"x","bitwiseAnd"),o=v(e,"y","bitwiseAnd");if(!br(t.shape,o.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${t.shape}, y: 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T.runKernel(Rs,p,u)}var TN=N({transform_:Ej});function $j(r,e,t){let o=v(r,"a","bandPart");E(o.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${o.rank}.`);let n=o.shape,[s,a]=o.shape.slice(-2),i,p;typeof e=="number"?(E(e%1===0,()=>`bandPart(): numLower must be an integer, got ${e}.`),E(e<=s,()=>`bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`),i=v(e<0?s:e,"numLower","bandPart")):(E(e.dtype==="int32",()=>"bandPart(): numLower's dtype must be an int32."),i=lo(Tl(e,0),s,Hu(e,s))),typeof t=="number"?(E(t%1===0,()=>`bandPart(): numUpper must be an integer, got ${t}.`),E(t<=a,()=>`bandPart(): numUpper (${t}) must not be greater than the number of columns (${a}).`),p=v(t<0?a:t,"numUpper","bandPart")):(E(t.dtype==="int32",()=>"bandPart(): numUpper's dtype must be an int32."),p=lo(Tl(t,0),a,Hu(t,a)));let u=W(cu(0,s,1,"int32"),[-1,1]),c=cu(0,a,1,"int32"),l=Te(u,c),m=Uu(ac(l,i),Id(l,pr(p))),d=Gr([s,a],o.dtype);return 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T.tidy(()=>{E(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let t=r.shape[0],o=r.shape[1],n=Cd(t),s=Ur(r),a=mu([[1]],[1,1]),i=Ur(a),p=t>=o?o:t;for(let u=0;u{let d=Xe(s,[u,u],[t-u,1]),f=Vu(d),h=Xe(s,[u,u],[1,1]),g=lo(Wu(h,0),mu([[-1]]),mu([[1]])),x=Te(h,se(g,f)),b=je(d,x);b.shape[0]===1?i=Ur(a):i=yt([a,Xe(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let C=pr(je(Ze(g,x),f)),S=Xe(s,[u,0],[t-u,o]),k=se(C,i),_=mc(i);if(u===0)s=Te(S,Ze(k,Ze(_,S)));else{let D=Te(S,Ze(k,Ze(_,S)));s=yt([Xe(s,[0,0],[u,o]),D],0)}let $=mc(k),R=Xe(n,[0,u],[t,n.shape[1]-u]);if(u===0)n=Te(R,Ze(Ze(R,i),$));else{let D=Te(R,Ze(Ze(R,i),$));n=yt([Xe(n,[0,0],[t,u]),D],1)}return[i,s,n]}),Ot([c,l,m])}return!e&&t>o&&(n=Xe(n,[0,0],[t,o]),s=Xe(s,[0,0],[o,o])),[n,s]})}var RN=N({qr_:Dj});var $t;(function(r){r[r.NONE=0]="NONE",r[r.MEAN=1]="MEAN",r[r.SUM=2]="SUM",r[r.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})($t||($t={}));function Aj(r,e,t=$t.SUM_BY_NONZERO_WEIGHTS){let o=v(r,"losses","computeWeightedLoss"),n=null;e!=null&&(n=v(e,"weights","computeWeightedLoss"));let s=n==null?o:se(o,n);if(t===$t.NONE)return s;if(t===$t.SUM)return ot(s);if(t===$t.MEAN){if(n==null)return Gu(s);{let a=o.size/n.size,i=je(ot(s),ot(n));return a>1?je(i,ke(a)):i}}if(t===$t.SUM_BY_NONZERO_WEIGHTS){if(n==null)return je(ot(s),ke(o.size));{let a=se(n,Da(o.shape)),i=Ue(ot(Fd(a,ke(0))),"float32");return je(ot(s),i)}}throw Error(`Unknown reduction: ${t}`)}var cr=N({computeWeightedLoss_:Aj});function Fj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","absoluteDifference"),s=v(e,"predictions","absoluteDifference"),a=null;t!=null&&(a=v(t,"weights","absoluteDifference")),xt(n.shape,s.shape,"Error in absoluteDifference: ");let i=Qt(Te(n,s));return cr(i,a,o)}var DN=N({absoluteDifference_:Fj});function Pj(r,e,t,o,n=$t.SUM_BY_NONZERO_WEIGHTS){let 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s=v(r,"labels","logLoss"),a=v(e,"predictions","logLoss"),i=null;t!=null&&(i=v(t,"weights","logLoss")),xt(s.shape,a.shape,"Error in logLoss: ");let p=ke(1),u=ke(o),c=pr(se(s,pi(Ce(a,u)))),l=se(Te(p,s),pi(Ce(Te(p,a),u))),m=Te(c,l);return cr(m,i,n)}var ON=N({logLoss_:Lj});function Bj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","meanSquaredError"),s=v(e,"predictions","meanSquaredError"),a=null;t!=null&&(a=v(t,"weights","meanSquaredError")),xt(n.shape,s.shape,"Error in meanSquaredError: ");let i=Kd(n,s);return cr(i,a,o)}var MN=N({meanSquaredError_:Bj});function zj(r,e){let t=v(r,"labels","sigmoidCrossEntropyWithLogits"),o=v(e,"logits","sigmoidCrossEntropyWithLogits");xt(t.shape,o.shape,"Error in sigmoidCrossEntropyWithLogits: ");let n=lu(o),s=se(o,t),a=kd(_o(pr(Qt(o))));return Ce(Te(n,s),a)}function Vj(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let 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Nj(r,e,t=!1,o=!1){let n=v(r,"images","resizeNearestNeighbor");E(n.rank===3||n.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`),E(e.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`),E(n.dtype==="float32"||n.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),E(o===!1||t===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=n,a=!1;n.rank===3&&(a=!0,s=W(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},p={alignCorners:t,halfPixelCenters:o,size:e},u=T.runKernel(as,i,p);return a?W(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var kN=N({resizeNearestNeighbor_:Nj});function Tj(r,e="binary",t=!1,o=.5){let n=v(r,"image","threshold"),s=.2989,a=.587,i=.114,p=n.shape[0]*n.shape[1],u=se(Jt([o]),255),c,l,m,d;if(E(n.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${n.rank}.`),E(n.shape[2]===3||n.shape[2]===1,()=>`Error 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T.runKernel(Rs,p,u)}var TN=N({transform_:Ej});function $j(r,e,t){let o=v(r,"a","bandPart");E(o.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${o.rank}.`);let n=o.shape,[s,a]=o.shape.slice(-2),i,p;typeof e=="number"?(E(e%1===0,()=>`bandPart(): numLower must be an integer, got ${e}.`),E(e<=s,()=>`bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`),i=v(e<0?s:e,"numLower","bandPart")):(E(e.dtype==="int32",()=>"bandPart(): numLower's dtype must be an int32."),i=lo(Tl(e,0),s,Hu(e,s))),typeof t=="number"?(E(t%1===0,()=>`bandPart(): numUpper must be an integer, got ${t}.`),E(t<=a,()=>`bandPart(): numUpper (${t}) must not be greater than the number of columns (${a}).`),p=v(t<0?a:t,"numUpper","bandPart")):(E(t.dtype==="int32",()=>"bandPart(): numUpper's dtype must be an int32."),p=lo(Tl(t,0),a,Hu(t,a)));let u=W(cu(0,s,1,"int32"),[-1,1]),c=cu(0,a,1,"int32"),l=Te(u,c),m=Uu(ac(l,i),Id(l,pr(p))),d=Gr([s,a],o.dtype);return 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T.tidy(()=>{E(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let t=r.shape[0],o=r.shape[1],n=Cd(t),s=Ur(r),a=mu([[1]],[1,1]),i=Ur(a),p=t>=o?o:t;for(let u=0;u{let d=Xe(s,[u,u],[t-u,1]),f=Vu(d),h=Xe(s,[u,u],[1,1]),g=lo(Wu(h,0),mu([[-1]]),mu([[1]])),x=Te(h,se(g,f)),b=je(d,x);b.shape[0]===1?i=Ur(a):i=yt([a,Xe(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let C=pr(je(Ze(g,x),f)),S=Xe(s,[u,0],[t-u,o]),k=se(C,i),_=mc(i);if(u===0)s=Te(S,Ze(k,Ze(_,S)));else{let D=Te(S,Ze(k,Ze(_,S)));s=yt([Xe(s,[0,0],[u,o]),D],0)}let $=mc(k),R=Xe(n,[0,u],[t,n.shape[1]-u]);if(u===0)n=Te(R,Ze(Ze(R,i),$));else{let D=Te(R,Ze(Ze(R,i),$));n=yt([Xe(n,[0,0],[t,u]),D],1)}return[i,s,n]}),Ot([c,l,m])}return!e&&t>o&&(n=Xe(n,[0,0],[t,o]),s=Xe(s,[0,0],[o,o])),[n,s]})}var RN=N({qr_:Dj});var $t;(function(r){r[r.NONE=0]="NONE",r[r.MEAN=1]="MEAN",r[r.SUM=2]="SUM",r[r.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})($t||($t={}));function Aj(r,e,t=$t.SUM_BY_NONZERO_WEIGHTS){let o=v(r,"losses","computeWeightedLoss"),n=null;e!=null&&(n=v(e,"weights","computeWeightedLoss"));let s=n==null?o:se(o,n);if(t===$t.NONE)return s;if(t===$t.SUM)return ot(s);if(t===$t.MEAN){if(n==null)return Gu(s);{let a=o.size/n.size,i=je(ot(s),ot(n));return a>1?je(i,ke(a)):i}}if(t===$t.SUM_BY_NONZERO_WEIGHTS){if(n==null)return je(ot(s),ke(o.size));{let a=se(n,Da(o.shape)),i=Ue(ot(Fd(a,ke(0))),"float32");return je(ot(s),i)}}throw Error(`Unknown reduction: ${t}`)}var cr=N({computeWeightedLoss_:Aj});function Fj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","absoluteDifference"),s=v(e,"predictions","absoluteDifference"),a=null;t!=null&&(a=v(t,"weights","absoluteDifference")),xt(n.shape,s.shape,"Error in absoluteDifference: ");let i=Qt(Te(n,s));return cr(i,a,o)}var DN=N({absoluteDifference_:Fj});function Pj(r,e,t,o,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","cosineDistance"),a=v(e,"predictions","cosineDistance"),i=null;o!=null&&(i=v(o,"weights","cosineDistance")),xt(s.shape,a.shape,"Error in cosineDistance: ");let p=ke(1),u=Te(p,ot(se(s,a),t,!0));return cr(u,i,n)}var AN=N({cosineDistance_:Pj});function Oj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","hingeLoss"),s=v(e,"predictions","hingeLoss"),a=null;t!=null&&(a=v(t,"weights","hingeLoss")),xt(n.shape,s.shape,"Error in hingeLoss: ");let i=ke(1);n=Te(se(ke(2),n),i);let p=lu(Te(i,se(n,s)));return cr(p,a,o)}var FN=N({hingeLoss_:Oj});function Mj(r,e,t,o=1,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","huberLoss"),a=v(e,"predictions","huberLoss"),i=null;t!=null&&(i=v(t,"weights","huberLoss")),xt(s.shape,a.shape,"Error in huberLoss: ");let p=ke(o),u=Qt(Te(a,s)),c=Hu(u,p),l=Te(u,c),m=Ce(se(ke(.5),Zt(c)),se(p,l));return cr(m,i,n)}var PN=N({huberLoss_:Mj});function Lj(r,e,t,o=1e-7,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","logLoss"),a=v(e,"predictions","logLoss"),i=null;t!=null&&(i=v(t,"weights","logLoss")),xt(s.shape,a.shape,"Error in logLoss: ");let p=ke(1),u=ke(o),c=pr(se(s,pi(Ce(a,u)))),l=se(Te(p,s),pi(Ce(Te(p,a),u))),m=Te(c,l);return cr(m,i,n)}var ON=N({logLoss_:Lj});function Bj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","meanSquaredError"),s=v(e,"predictions","meanSquaredError"),a=null;t!=null&&(a=v(t,"weights","meanSquaredError")),xt(n.shape,s.shape,"Error in meanSquaredError: ");let i=Kd(n,s);return cr(i,a,o)}var MN=N({meanSquaredError_:Bj});function zj(r,e){let t=v(r,"labels","sigmoidCrossEntropyWithLogits"),o=v(e,"logits","sigmoidCrossEntropyWithLogits");xt(t.shape,o.shape,"Error in sigmoidCrossEntropyWithLogits: ");let n=lu(o),s=se(o,t),a=kd(_o(pr(Qt(o))));return Ce(Te(n,s),a)}function Vj(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let 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this.applyGradients(s);return Ot(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Vw(e,t)}dispose(){this.iterations_!=null&&Ot(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ke(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(kr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var Ju=class extends kr{static get className(){return"Adadelta"}constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:De(()=>Gt(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;De(()=>{let c=Ce(se(p,this.rho),se(Zt(i),1-this.rho)),l=se(je(Rr(Ce(u,this.epsilon)),Rr(Ce(p,this.epsilon))),i),m=Ce(se(u,this.rho),se(Zt(l),1-this.rho));p.assign(c),u.assign(m);let d=Ce(se(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(e=>e.variable)),Ot(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};var ep=class extends kr{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:De(()=>$a(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;De(()=>{let p=Ce(i,Zt(a));i.assign(p);let u=Ce(se(je(a,Rr(Ce(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};var tp=class extends kr{static get className(){return"Adam"}constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=Te(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:De(()=>Gt(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Gt(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=Ce(se(l,this.beta2),se(Zt(u),1-this.beta2)),f=je(m,o),h=je(d,n);c.assign(m),l.assign(d);let g=Ce(se(je(f,Ce(Rr(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(se(this.accBeta1,this.beta1)),this.accBeta2.assign(se(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ot(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),De(()=>{this.accBeta1.assign(ui(this.beta1,this.iterations_+1)),this.accBeta2.assign(ui(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};var rp=class extends kr{static get className(){return"Adamax"}constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=je(-this.learningRate,Ce(se(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Gt(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Gt(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=se(l,this.beta2),f=Qt(u),h=Ad(d,f);c.assign(m),l.assign(h);let g=Ce(se(je(n,o),je(m,Ce(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Ce(this.iteration,1)),this.accBeta1.assign(se(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};var mi=class extends kr{static get className(){return"SGD"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];De(()=>{let i=Ce(se(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=$r(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};var op=class extends mi{static get className(){return"Momentum"}constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:De(()=>Gt(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&De(()=>{let p,u=Ce(se(this.m,a),i);this.useNesterov?p=Ce(se(this.c,Ce(i,se(u,this.m))),s):p=Ce(se(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ot(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};var np=class extends kr{static get className(){return"RMSProp"}constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:De(()=>Gt(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;De(()=>{let c=Ce(se(p,this.decay),se(Zt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=Ce(se(l,this.decay),se(i,1-this.decay)),d=je(se(i,this.learningRate),Rr(Te(c,Ce(Zt(m),this.epsilon)))),f=Ce(se(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Te(s,f);s.assign(h)}else{let l=Ce(se(p,this.decay),se(Zt(i),1-this.decay)),m=Ce(se(u,this.momentum),je(se(i,this.learningRate),Rr(Ce(l,this.epsilon))));p.assign(l),u.assign(m);let d=Te(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ot(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ot(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ot(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};var iX=[Ju,ep,tp,rp,op,np,mi];function XN(){for(let r of iX)rS(r)}var 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n=I("images",r,e,t),s=I("transforms",r,e,t),a=I("outputShape",r,e,t),i=I("fillValue",r,e,t),p=I("interpolation",r,e,t),u=I("fillMode",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UT=(r,e,t,o=Je)=>{switch(r.op){case"Equal":return[o.equal(I("a",r,e,t),I("b",r,e,t))];case"NotEqual":return[o.notEqual(I("a",r,e,t),I("b",r,e,t))];case"Greater":return[o.greater(I("a",r,e,t),I("b",r,e,t))];case"GreaterEqual":return[o.greaterEqual(I("a",r,e,t),I("b",r,e,t))];case"Less":return[o.less(I("a",r,e,t),I("b",r,e,t))];case"LessEqual":return[o.lessEqual(I("a",r,e,t),I("b",r,e,t))];case"LogicalAnd":return[o.logicalAnd(I("a",r,e,t),I("b",r,e,t))];case"LogicalNot":return[o.logicalNot(I("a",r,e,t))];case"LogicalOr":return[o.logicalOr(I("a",r,e,t),I("b",r,e,t))];case"Select":case"SelectV2":return[o.where(I("condition",r,e,t),I("a",r,e,t),I("b",r,e,t))];case"BitwiseAnd":return[o.bitwiseAnd(I("a",r,e,t),I("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GT=(r,e,t,o=Je)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[o.matMul(I("a",r,e,t),I("b",r,e,t),I("transposeA",r,e,t),I("transposeB",r,e,t))];case"Einsum":return[o.einsum(I("equation",r,e,t),...I("tensors",r,e,t))];case"Transpose":return[o.transpose(I("x",r,e,t),I("perm",r,e,t))];case"_FusedMatMul":let[n,s]=I("fusedOps",r,e,t),a=n==="biasadd",i=s==="prelu",p=I("numArgs",r,e,t),u=I("leakyreluAlpha",r,e,t);if(a){if(i&&p!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&p!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,l]=I("args",r,e,t);return[o.fused.matMul({a:I("a",r,e,t),b:I("b",r,e,t),transposeA:I("transposeA",r,e,t),transposeB:I("transposeB",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];case"MatrixBandPart":return[o.linalg.bandPart(I("a",r,e,t),I("numLower",r,e,t),I("numUpper",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HT=(r,e,t,o=Je)=>{switch(r.op){case"EuclideanNorm":return[o.euclideanNorm(I("x",r,e,t),I("axis",r,e,t),I("keepDims",r,e,t))];case"FusedBatchNorm":case"FusedBatchNormV2":return[o.batchNorm(I("x",r,e,t),I("mean",r,e,t),I("variance",r,e,t),I("offset",r,e,t),I("scale",r,e,t),I("epsilon",r,e,t))];case"FusedBatchNormV3":return[o.batchNorm(I("x",r,e,t),I("mean",r,e,t),I("variance",r,e,t),I("offset",r,e,t),I("scale",r,e,t),I("epsilon",r,e,t))];case"LRN":return[o.localResponseNormalization(I("x",r,e,t),I("radius",r,e,t),I("bias",r,e,t),I("alpha",r,e,t),I("beta",r,e,t))];case"Softmax":return[o.softmax(I("x",r,e,t))];case"LogSoftmax":return[o.logSoftmax(I("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KT=(r,e,t,o=Je)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I("paramsNestedSplits",r,e,t),I("paramsDenseValues",r,e,t),I("indices",r,e,t),I("outputRaggedRank",r,e,t));return n.concat(s)}case"RaggedRange":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I("starts",r,e,t),I("limits",r,e,t),I("splits",r,e,t));return[n,s]}case"RaggedTensorToTensor":return[o.raggedTensorToTensor(I("shape",r,e,t),I("values",r,e,t),I("defaultValue",r,e,t),I("rowPartitionTensors",r,e,t),I("rowPartitionTypes",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var qT=(r,e,t,o=Je)=>{switch(r.op){case"Max":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.max(I("x",r,e,t),i,p)]}case"Mean":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.mean(I("x",r,e,t),i,p)]}case"Min":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.min(I("x",r,e,t),i,p)]}case"Sum":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.sum(I("x",r,e,t),i,p)]}case"All":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.all(I("x",r,e,t),i,p)]}case"Any":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.any(I("x",r,e,t),i,p)]}case"ArgMax":{let i=I("axis",r,e,t);return[o.argMax(I("x",r,e,t),i)]}case"ArgMin":{let i=I("axis",r,e,t);return[o.argMin(I("x",r,e,t),i)]}case"Prod":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.prod(I("x",r,e,t),i,p)]}case"Cumprod":{let i=I("axis",r,e,t),p=I("exclusive",r,e,t),u=I("reverse",r,e,t);return[o.cumprod(I("x",r,e,t),i,p,u)]}case"Cumsum":{let i=I("axis",r,e,t),p=I("exclusive",r,e,t),u=I("reverse",r,e,t);return[o.cumsum(I("x",r,e,t),i,p,u)]}case"Bincount":let n=I("x",r,e,t),s=I("weights",r,e,t),a=I("size",r,e,t);return[o.bincount(n,s,a)];case"DenseBincount":{let i=I("x",r,e,t),p=I("weights",r,e,t),u=I("size",r,e,t),c=I("binaryOutput",r,e,t);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var jT=(r,e,t,o=Je)=>{switch(r.op){case"ConcatV2":case"Concat":{let n=I("n",r,e,t),s=I("axis",r,e,t),a=I("tensors",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case"Gather":{let n=I("x",r,e,t),s=I("indices",r,e,t);return[o.gather(n,o.cast(s,"int32"),0)]}case"GatherV2":{let n=I("axis",r,e,t),s=I("batchDims",r,e,t),a=I("x",r,e,t),i=I("indices",r,e,t);return[o.gather(a,o.cast(i,"int32"),n,s)]}case"Reverse":{let n=I("dims",r,e,t),s=[];for(let i=0;i{let n=I("axis",r,e,t),s=I("tensors",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error("the input tensors shape does not match");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case"Unpack":{let n=I("axis",r,e,t),s=I("tensor",r,e,t);return o.unstack(s,n)}case"Tile":{let n=I("reps",r,e,t);return[o.tile(I("x",r,e,t),n)]}case"Split":case"SplitV":{let n=I("axis",r,e,t),s=I("numOrSizeSplits",r,e,t),a=I("x",r,e,t);return o.split(a,s,n)}case"ScatterNd":{let n=I("indices",r,e,t),s=I("values",r,e,t),a=I("shape",r,e,t);return[o.scatterND(n,s,a)]}case"GatherNd":{let n=I("x",r,e,t),s=I("indices",r,e,t);return[o.gatherND(n,s)]}case"SparseToDense":{let n=I("sparseIndices",r,e,t),s=I("outputShape",r,e,t),a=I("sparseValues",r,e,t),i=I("defaultValue",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}case"TensorScatterUpdate":{let n=I("indices",r,e,t),s=I("values",r,e,t),a=I("tensor",r,e,t);return[o.tensorScatterUpdate(a,n,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var XT=(r,e,t,o=Je)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I("indices",r,e,t),I("values",r,e,t),I("denseShape",r,e,t),I("defaultValue",r,e,t));return[n,s,a,i]}case"SparseReshape":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I("inputIndices",r,e,t),I("inputShape",r,e,t),I("newShape",r,e,t));return[n,s]}case"SparseSegmentMean":return[o.sparse.sparseSegmentMean(I("data",r,e,t),I("indices",r,e,t),I("segmentIds",r,e,t))];case"SparseSegmentSum":return[o.sparse.sparseSegmentSum(I("data",r,e,t),I("indices",r,e,t),I("segmentIds",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YT=(r,e,t,o=Je)=>{switch(r.op){case"FFT":return[o.fft(I("x",r,e,t))];case"IFFT":return[o.ifft(I("x",r,e,t))];case"RFFT":return[o.rfft(I("x",r,e,t))];case"IRFFT":return[o.irfft(I("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QT=(r,e,t,o=Je)=>{switch(r.op){case"StaticRegexReplace":return[o.string.staticRegexReplace(I("input",r,e,t),I("pattern",r,e,t),I("rewrite",r,e,t),I("replaceGlobal",r,e,t))];case"StringNGrams":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I("data",r,e,t),I("dataSplits",r,e,t),I("separator",r,e,t),I("nGramWidths",r,e,t),I("leftPad",r,e,t),I("rightPad",r,e,t),I("padWidth",r,e,t),I("preserveShortSequences",r,e,t));return[n,s]}case"StringSplit":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I("input",r,e,t),I("delimiter",r,e,t),I("skipEmpty",r,e,t));return[n,s,a]}case"StringToHashBucketFast":return[o.string.stringToHashBucketFast(I("input",r,e,t),I("numBuckets",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZT=(r,e,t,o=Je)=>{switch(r.op){case"Cast":return[o.cast(I("x",r,e,t),I("dtype",r,e,t))];case"ExpandDims":{let n=I("axis",r,e,t);return[o.expandDims(I("x",r,e,t),n)]}case"Squeeze":{let n=I("axis",r,e,t);return[o.squeeze(I("x",r,e,t),n)]}case"Reshape":return[o.reshape(I("x",r,e,t),I("shape",r,e,t))];case"EnsureShape":return[o.ensureShape(I("x",r,e,t),I("shape",r,e,t))];case"MirrorPad":return[o.mirrorPad(I("x",r,e,t),I("padding",r,e,t),I("mode",r,e,t))];case"PadV2":case"Pad":return[o.pad(I("x",r,e,t),I("padding",r,e,t),I("constantValue",r,e,t))];case"SpaceToBatchND":{let n=I("blockShape",r,e,t),s=I("paddings",r,e,t);return[o.spaceToBatchND(I("x",r,e,t),n,s)]}case"BatchToSpaceND":{let n=I("blockShape",r,e,t),s=I("crops",r,e,t);return[o.batchToSpaceND(I("x",r,e,t),n,s)]}case"DepthToSpace":{let n=I("blockSize",r,e,t),s=I("dataFormat",r,e,t).toUpperCase();return[o.depthToSpace(I("x",r,e,t),n,s)]}case"BroadcastTo":return[o.broadcastTo(I("x",r,e,t),I("shape",r,e,t))];case"BroadcastArgs":return[o.broadcastArgs(I("s0",r,e,t),I("s1",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function MS(r,e,t,o,n=De){let s=((a,i,p)=>{switch(a.category){case"arithmetic":return n(()=>TT(a,i,p));case"basic_math":return n(()=>_T(a,i,p));case"control":return FT(a,i,p);case"convolution":return n(()=>OT(a,i,p));case"creation":return n(()=>MT(a,i,p));case"dynamic":return LT(a,i,p);case"evaluation":return n(()=>BT(a,i,p));case"image":return n(()=>WT(a,i,p));case"graph":return n(()=>zT(a,i,p));case"logical":return n(()=>UT(a,i,p));case"matrices":return n(()=>GT(a,i,p));case"normalization":return n(()=>HT(a,i,p));case"ragged":return n(()=>KT(a,i,p));case"reduction":return n(()=>qT(a,i,p));case"slice_join":return n(()=>jT(a,i,p));case"sparse":return n(()=>XT(a,i,p));case"spectral":return n(()=>YT(a,i,p));case"string":return n(()=>QT(a,i,p));case"transformation":return n(()=>ZT(a,i,p));case"hash_table":return VT(a,i,p,o);case"custom":let u=pf(a.op);if(u&&u.customExecutor)return u.customExecutor(new wf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var Ml=class{constructor(e={},t={},o={},n={},s){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function LS(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=new Set(Object.keys(r).map(m=>Nr(m)[0]));o=o||[];let c=new Set(o.map(m=>Nr(m.name)[0])),l=[...e];for(;l.length>0;){let m=l.pop();if((fu(m)||A8(m)||F8(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&!u.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function JT(r,e){let{usedNodes:t,inputs:o}=e,n=Object.keys(o).map(g=>Nr(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],a=g=>t.has(typeof g=="string"?g:g.name);function i(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let p=i([...n,...r.weights,...s]).filter(a),u=i([...p,...Object.values(r.nodes)]).filter(a),c=new Map(u.map(g=>[g.name,g])),l={};for(let g of u){l[g.name]=l[g.name]||0;for(let x of g.children)a(x)||(l[x.name]=Number.POSITIVE_INFINITY),l[x.name]=(l[x.name]||0)+1}let m=Object.entries(l).filter(([,g])=>g===0).map(([g])=>g),d=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(a))--l[b.name]===0&&(d.push(b.name),m.push(b.name))}let f=d.map(g=>c.get(g)),h=_8(f,p);return E8(h,p),h}function _8(r,e){let t=new Map(r.map(a=>[a.name,a])),o=e.map(a=>a.name),n=new Set(o);for(;o.length>0;){let a=o.pop(),i=t.get(a);for(let p of i.children)!t.has(p.name)||n.has(p.name)||(n.add(p.name),o.push(p.name))}return r.filter(a=>n.has(a.name))}var gc=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function E8(r,e){let t=new Map(r.map((i,p)=>[i.name,p])),o=new Set(e.map(i=>i.name)),n=i=>o.has(typeof i=="string"?i:i.name),s=new Set(r.map(i=>i.name)),a=i=>s.has(typeof i=="string"?i:i.name);for(let i of r){for(let p of i.children.filter(a)){if(!t.has(p.name))throw new gc(`Child ${p.name} of node ${i.name} is unreachable.`);if(t.get(i.name)>t.get(p.name))throw new gc(`Node ${i.name} is scheduled to run after its child ${p.name}.`)}if(!n(i))for(let p of i.inputs){if(!t.has(p.name))throw new gc(`Input ${p.name} of node ${i.name} is unreachable.`);if(t.get(p.name)>t.get(i.name))throw new gc(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function e_(r){let e=new Map(r.map((i,p)=>[i.name,p])),t=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>fu(i)?t:p),n=i=>{let p=o[e.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,c)=>Math.max(u,c),o[p])),a=new Map;for(let i=0;ie[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new r(e.functions[o],this)})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPARATOR)+"--"+n.join(this.SEPARATOR)}compile(e,t){let o=LS(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let u=t.map(l=>l.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`)}let i=JT(this.graph,o),p=e_(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return $r(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(m=>this.graph.nodes[Nr(m)[0]]),s=t.map(m=>Nr(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(e,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=A().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},l={};return De(()=>{let m=new Ml(this.weightMap,c,l,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(x=>{let[b,C]=Nr(x,m),S=[];S[C]=e[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=MS(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);d[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,d,m,f,a,g.get(x.name))}return this.parent==null&&m.dispose(f),t.map(x=>Bt(x,d,m))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){if(!(fu(t)||a.has(e))){for(let p of o[e])p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length);for(let p of t.inputs){if(fu(p))continue;let u=hS(p.name,o,n);if(u!=null)for(let c of u){if(!c||c.kept||s.has(c.id))continue;let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,o,n,s,a){function i(p){return fu(p)||s.has(p.name)}if(!(fu(e)||a==null))for(let p of a){if(i(p))continue;let u=hS(p.name,t,o);for(let c of u)!c||c.kept||n.has(c.id)||c.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=A().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new Ml(this.weightMap,n,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Bt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(S=>this.graph.nodes[Nr(S)[0]]),i=o.map(S=>Nr(S)[0]),p=new Set(i),u=i.map(S=>this.graph.nodes[S]);u.length===0&&(u=this._outputs);let{usedNodes:c,missingInputs:l,dynamicNode:m,syncInputs:d}=LS(e,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(S=>({node:S,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(S=>{let[k,_]=Nr(S),$=[];$[_]=e[S],h[k]=$});let g={},x=this.getFrozenTensorIds(h),b={};for(;f.length>0;){let S=this.processStack(a,f,t,h,b,x,p,g,c);await Promise.all(S)}m==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let C=u.filter(S=>!fu(S)&&!Bt(S.name,h,t)).map(S=>S.name);if(C.length>0){let S="";throw m!=null&&(S=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${C}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`)}return h}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m="";if(l.node.op==="Enter"&&I("isConstant",l.node,n,o)&&([m]=Ls(l.node.name,o)),n[l.node.name]==null){let d=MS(l.node,n,o,this._resourceManager);m||([m]=Ls(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=Ls(i.name,o);s[p]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Nr(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Nr(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Nr(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var kf=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}};var P8="?tfjs-format=file",O8="model.json",Bl=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},o=di){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=o,t==null&&(this.loadOptions={}),this.resourceManager=new kf}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else 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this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let o=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=n,this.version=`${o.versions.producer}.${o.versions.minConsumer}`,this.executor=new Ll(Ol.Instance.transformGraph(o,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=Ol.Instance.transformGraph(e.modelInitializer);this.initializer=new Ll(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof mt?[e]:e,o={};return t.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return e}predict(e,t){let o=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(e,t){let o=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(e){var t;if(!(e instanceof mt)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let o=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+o!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-o} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((s,a)=>{var i,p,u;let c=(u=(p=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||p===void 0?void 0:p[a])===null||u===void 0?void 0:u.resourceId;return c!=null?s[a]=this.resourceIdToCapturedInput[c]:s[a]=e[n++],s},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return 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MY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Q([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=c.strideHeight,m=c.strideWidth,d=c.filterHeight,f=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,C=b-1-c.padInfo.left,S=x-1-c.padInfo.top,k=me(a.shape,"float32"),_=1/(d*f),$=t.data.get(n.dataId).values,R=me(n.shape,"float32",$);for(let D=0;D=c.outHeight||Math.floor(j)!==j))for(let q=0;q=c.outWidth||Math.floor(Y)!==Y)continue;let J=R.get(D,j,Y,P);z+=J}}k.set(z*_,D,O,M,P)}return t.makeTensorInfo(k.shape,k.dtype,k.values)}var cE={kernelName:$i,backendName:"cpu",kernelFunc:MY};function LY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,scale:s,offset:a,mean:i,variance:p}=e;y.assert(i.shape.length===p.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient 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i=s.reduce((x,b)=>x*b),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=We({inputs:{x:n},backend:t,attrs:{shape:p}}),f=St({inputs:{x:d},backend:t,attrs:{perm:u}}),h=We({inputs:{x:f},backend:t,attrs:{shape:c}}),g=Ao({inputs:{x:h},backend:t,attrs:{begin:l,size:m}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var mE={kernelName:Js,backendName:"cpu",kernelFunc:BY};function zY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,p=t.data.get(s.dataId).values,u=Cc(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var dE={kernelName:Jo,backendName:"cpu",kernelFunc:zY};function VY(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.data.get(o.dataId).values,a=t.data.get(n.dataId).values,i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var fE={kernelName:ea,backendName:"cpu",kernelFunc:VY};var WY=Ie(bo,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r{let{x:e}=r.inputs,t=r.backend,o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=t.data.get(s.dataId).values,p=t.data.get(a.dataId).values;for(let u=0;uh.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(e.map(h=>h.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(h=>y.sizeFromShape(h.shape)>0);if(p.length===1)return lr({inputs:{x:p[0]},backend:t});if(p[0].dtype==="complex64"){let h=p.map(S=>$o({inputs:{input:S},backend:t})),g=p.map(S=>Oa({inputs:{input:S},backend:t})),x=hu({inputs:h,backend:t,attrs:{axis:s}}),b=hu({inputs:g,backend:t,attrs:{axis:s}}),C=Ht({inputs:{real:x,imag:b},backend:t});return h.forEach(S=>t.disposeIntermediateTensorInfo(S)),g.forEach(S=>t.disposeIntermediateTensorInfo(S)),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(b),C}let u=p.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return We({inputs:{x:h},backend:t,attrs:{shape:x}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));i=w.computeOutShape(u.map(h=>h.shape),1);let l=u[0].shape[0]===1,m=ap(c,i,e[0].dtype,l),d=w.computeOutShape(p.map(h=>h.shape),s),f=t.makeTensorInfo(d,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),f}var yE={kernelName:ta,backendName:"cpu",kernelFunc:hu};function CI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o;Q([n,s],"conv2d");let l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat==="channelsLast",S=new tt(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),$=k[0],R=C?k[1]:k[2],D=C?k[2]:1,P=C?1:k[1],O=S.strides[0],M=C?S.strides[1]:S.strides[2],L=C?S.strides[2]:1,B=C?1:S.strides[1],z=t.data.get(n.dataId).values,U=t.data.get(s.dataId).values,j=S.values;for(let q=0;q=m.inHeight)continue;let le=oe*_[0],be=Y+ie*R;for(let _e=0;_e=m.inWidth)continue;let ct=le+Pe*_[1],He=be+st*D,lt=ct;for(let it=0;it=u.inDepth)continue;let q=U*D[0],Y=O+j*R[1];for(let J=0;J=u.inHeight)continue;let ie=q+ee*D[1],le=Y+oe*R[2];for(let be=0;be=u.inWidth)continue;let st=ie+Fe*D[2],ct=le+Pe*u.inChannels,He=st;for(let lt=0;ltMath.cos(r)),kE={kernelName:sn,backendName:"cpu",kernelFunc:XY};var YY=Ie(an,r=>Math.cosh(r)),NE={kernelName:an,backendName:"cpu",kernelFunc:YY};function QY(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=me([f,h,g,d],"float32"),b=t.data.get(s.dataId).values,C=t.data.get(a.dataId).values,S=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let $=0;$=c)continue;let B=h>1?(O-D)*(l-1)/(h-1):0,z=g>1?(M-P)*(m-1)/(g-1):0;for(let U=0;U1?D*(l-1)+U*B:.5*(D+O)*(l-1);if(j<0||j>l-1){for(let q=0;q1?P*(m-1)+re*z:.5*(P+M)*(m-1);if(ne<0||ne>m-1){for(let le=0;le1?P*(m-1)+q*z:.5*(P+M)*(m-1);if(Y<0||Y>m-1){for(let ne=0;nex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=t.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:f,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=d,C=b.left,S=b.top,k=d.outChannels/d.inChannels,_=new tt(d.outShape,n.dtype),$=t.data.get(n.dataId).values,R=t.data.get(s.dataId).values,D=_.values;for(let P=0;P=d.inHeight)continue;let q=U*l[0],Y=O+j*c[1];for(let J=0;J=d.inWidth)continue;let ie=q+ee*l[1],le=Y+oe*d.inChannels,be=re,_e=ie;for(let ve=0;ve{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=t,p=e,u=p.data.get(o.dataId).values,c=o.shape.length,l=p.data.get(n.dataId).values,m=n.shape.length,{batchSize:d,inHeight:f,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:C,strideHeight:S,strideWidth:k,filterHeight:_,filterWidth:$,dilationHeight:R,dilationWidth:D,outShape:P}=w.computeDilation2DInfo(o.shape,n.shape,s,a,"NHWC",i),O=y.sizeFromShape(P),M=P.length,L=y.getArrayFromDType(o.dtype,O);for(let z=0;z=0&&oe=0&&lere&&(re=ve)}}}let ne=y.locToIndex([z,U,q,J],M,y.computeStrides(P));L[ne]=re}}}return{dataId:p.write(y.toTypedArray(L,o.dtype),P,o.dtype),shape:P,dtype:o.dtype}}};var ME={kernelName:Li,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:$,dilationWidth:R,outShape:D}=w.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===D.length,()=>`Error in ${Li}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let P=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&ee=0&&ieY&&(Y=le,J=ne,re=oe)}}}O[J][re][q]+=P[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var LE={kernelName:Mi,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:S,filterHeight:k,filterWidth:_,dilationHeight:$,dilationWidth:R,outShape:D}=w.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===D.length,()=>`Error in ${Mi}, dy must have the same rank as output ${D.length}, but got ${s.rank}`);let P=y.toNestedArray(D,u.data.get(s.dataId).values),O=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let L=0;L=0&&ee=0&&ieY&&(Y=le,J=ee,re=ie)}}}O[L][J][re][q]+=P[L][B][U][q]}}}return{dataId:u.write(y.toTypedArray(O,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function sQ(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{canvas:s,options:a}=o,{contextOptions:i,imageOptions:p}=a||{},u=(p==null?void 0:p.alpha)||1,c=(i==null?void 0:i.contextType)||"2d";if(c!=="2d")throw new Error(`Context type ${i.contextType} is not supported by the CPU backend.`);let l=s.getContext(c,(i==null?void 0:i.contextAttributes)||{});if(l==null)throw new Error(`Could not get the context with ${c} type.`);let[m,d]=n.shape.slice(0,2),f=n.shape.length===2?1:n.shape[2],h=t.data.get(n.dataId).values,g=n.dtype==="float32"?255:1,x=new Uint8ClampedArray(d*m*4);for(let C=0;C1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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FY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Q(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=c.strideDepth,m=c.strideHeight,d=c.strideWidth,f=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,C=c.dilationWidth,S=c.effectiveFilterDepth,k=c.effectiveFilterHeight,_=c.effectiveFilterWidth,$=S-1-c.padInfo.front,R=_-1-c.padInfo.left,D=k-1-c.padInfo.top,P=me(s.shape,"float32"),O=1/(f*h*g),M=t.bufferSync(n);for(let L=0;L=c.outDepth||Math.floor(ee)!==ee))for(let oe=0;oe=c.outHeight||Math.floor(ie)!==ie))for(let le=0;le<_;le+=C){let be=(J+le)/d;if(be<0||be>=c.outWidth||Math.floor(be)!==be)continue;let _e=M.get(L,ee,ie,be,B);re+=_e}}}P.set(re*O,L,z,U,j,B)}return t.makeTensorInfo(P.shape,P.dtype,P.values)}var pE={kernelName:Ri,backendName:"cpu",kernelFunc:OY};function MY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Q([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=c.strideHeight,m=c.strideWidth,d=c.filterHeight,f=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,C=b-1-c.padInfo.left,S=x-1-c.padInfo.top,k=me(a.shape,"float32"),_=1/(d*f),$=t.data.get(n.dataId).values,R=me(n.shape,"float32",$);for(let D=0;D=c.outHeight||Math.floor(j)!==j))for(let q=0;q=c.outWidth||Math.floor(Y)!==Y)continue;let J=R.get(D,j,Y,P);z+=J}}k.set(z*_,D,O,M,P)}return t.makeTensorInfo(k.shape,k.dtype,k.values)}var cE={kernelName:$i,backendName:"cpu",kernelFunc:MY};function LY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,scale:s,offset:a,mean:i,variance:p}=e;y.assert(i.shape.length===p.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient 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i=s.reduce((x,b)=>x*b),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=We({inputs:{x:n},backend:t,attrs:{shape:p}}),f=St({inputs:{x:d},backend:t,attrs:{perm:u}}),h=We({inputs:{x:f},backend:t,attrs:{shape:c}}),g=Ao({inputs:{x:h},backend:t,attrs:{begin:l,size:m}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var mE={kernelName:Js,backendName:"cpu",kernelFunc:BY};function zY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,p=t.data.get(s.dataId).values,u=Cc(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var dE={kernelName:Jo,backendName:"cpu",kernelFunc:zY};function VY(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.data.get(o.dataId).values,a=t.data.get(n.dataId).values,i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var fE={kernelName:ea,backendName:"cpu",kernelFunc:VY};var WY=Ie(bo,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r{let{x:e}=r.inputs,t=r.backend,o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=t.data.get(s.dataId).values,p=t.data.get(a.dataId).values;for(let u=0;uh.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(e.map(h=>h.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(h=>y.sizeFromShape(h.shape)>0);if(p.length===1)return lr({inputs:{x:p[0]},backend:t});if(p[0].dtype==="complex64"){let h=p.map(S=>$o({inputs:{input:S},backend:t})),g=p.map(S=>Oa({inputs:{input:S},backend:t})),x=hu({inputs:h,backend:t,attrs:{axis:s}}),b=hu({inputs:g,backend:t,attrs:{axis:s}}),C=Ht({inputs:{real:x,imag:b},backend:t});return h.forEach(S=>t.disposeIntermediateTensorInfo(S)),g.forEach(S=>t.disposeIntermediateTensorInfo(S)),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(b),C}let u=p.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return We({inputs:{x:h},backend:t,attrs:{shape:x}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));i=w.computeOutShape(u.map(h=>h.shape),1);let l=u[0].shape[0]===1,m=ap(c,i,e[0].dtype,l),d=w.computeOutShape(p.map(h=>h.shape),s),f=t.makeTensorInfo(d,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),f}var yE={kernelName:ta,backendName:"cpu",kernelFunc:hu};function CI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o;Q([n,s],"conv2d");let l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat==="channelsLast",S=new tt(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),$=k[0],R=C?k[1]:k[2],D=C?k[2]:1,P=C?1:k[1],O=S.strides[0],M=C?S.strides[1]:S.strides[2],L=C?S.strides[2]:1,B=C?1:S.strides[1],z=t.data.get(n.dataId).values,U=t.data.get(s.dataId).values,j=S.values;for(let q=0;q=m.inHeight)continue;let le=oe*_[0],be=Y+ie*R;for(let _e=0;_e=m.inWidth)continue;let ct=le+Pe*_[1],He=be+st*D,lt=ct;for(let it=0;it=u.inDepth)continue;let q=U*D[0],Y=O+j*R[1];for(let J=0;J=u.inHeight)continue;let ie=q+ee*D[1],le=Y+oe*R[2];for(let be=0;be=u.inWidth)continue;let st=ie+Fe*D[2],ct=le+Pe*u.inChannels,He=st;for(let lt=0;ltMath.cos(r)),kE={kernelName:"Cos",backendName:"cpu",kernelFunc:XY};var YY=Ie(an,r=>Math.cosh(r)),NE={kernelName:an,backendName:"cpu",kernelFunc:YY};function QY(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=me([f,h,g,d],"float32"),b=t.data.get(s.dataId).values,C=t.data.get(a.dataId).values,S=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let $=0;$=c)continue;let B=h>1?(O-D)*(l-1)/(h-1):0,z=g>1?(M-P)*(m-1)/(g-1):0;for(let U=0;U1?D*(l-1)+U*B:.5*(D+O)*(l-1);if(j<0||j>l-1){for(let q=0;q1?P*(m-1)+re*z:.5*(P+M)*(m-1);if(ne<0||ne>m-1){for(let le=0;le1?P*(m-1)+q*z:.5*(P+M)*(m-1);if(Y<0||Y>m-1){for(let ne=0;nex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=t.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. 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nA={kernelName:"Abs",backendName:"webgl",kernelFunc:C9};var w9=Wt+` if (abs(x) > 1.) { return NAN; } return acos(x); `,S9=xe({opSnippet:w9}),sA={kernelName:Vo,backendName:"webgl",kernelFunc:S9};var I9=Wt+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,v9=xe({opSnippet:I9}),aA={kernelName:Wo,backendName:"webgl",kernelFunc:v9};var iA="return a + b;",k9=nt({opSnippet:iA,packedOpSnippet:iA,supportsComplex:!0,cpuKernelImpl:LR}),uA={kernelName:uo,backendName:"webgl",kernelFunc:k9};var bh=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` +return log(x + sqrt(x * x - 1.0));`,v9=xe({opSnippet:I9}),aA={kernelName:Wo,backendName:"webgl",kernelFunc:v9};var iA="return a + b;",k9=nt({opSnippet:iA,packedOpSnippet:iA,supportsComplex:!0,cpuKernelImpl:LR}),uA={kernelName:"Add",backendName:"webgl",kernelFunc:k9};var bh=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${o.join(` `)} @@ -1539,7 +1539,7 @@ return log(x + sqrt(x * x - 1.0));`,v9=xe({opSnippet:I9}),aA={kernelName:Wo,back vec4 result = ${n}; setOutput(result); } - `}};function wh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Dt({inputs:{x:o[0]},backend:t});if(o.length>A().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let p=Math.floor(o.length/2),u=wh({inputs:o.slice(0,p),backend:t}),c=wh({inputs:o.slice(p),backend:t});return wh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=A().getBool("WEBGL_PACK")?new Ch(o[0].shape,s):new bh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var pA={kernelName:Uo,backendName:"webgl",kernelFunc:wh};function N9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims("all",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,"all",t),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var cA={kernelName:Go,backendName:"webgl",kernelFunc:N9};function T9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims("any",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,"any",t),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var lA={kernelName:Ho,backendName:"webgl",kernelFunc:T9};var Sh=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",p=o?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + `}};function wh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Dt({inputs:{x:o[0]},backend:t});if(o.length>A().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let p=Math.floor(o.length/2),u=wh({inputs:o.slice(0,p),backend:t}),c=wh({inputs:o.slice(p),backend:t});return wh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=A().getBool("WEBGL_PACK")?new Ch(o[0].shape,s):new bh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var pA={kernelName:Uo,backendName:"webgl",kernelFunc:wh};function N9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims("all",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,"all",t),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var cA={kernelName:"All",backendName:"webgl",kernelFunc:N9};function T9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims("any",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,"any",t),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var lA={kernelName:"Any",backendName:"webgl",kernelFunc:T9};var Sh=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",p=o?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1639,7 +1639,7 @@ return log(x + sqrt(x * x - 1.0));`,v9=xe({opSnippet:I9}),aA={kernelName:Wo,back return result; `,L9=nt({opSnippet:O9,packedOpSnippet:M9}),bA={kernelName:Yo,backendName:"webgl",kernelFunc:L9};var B9=Wt+` if ((x < -1.0) || (x > 1.0)) return NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelName:Xo,backendName:"webgl",kernelFunc:z9};var Us=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let R=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelName:Xo,backendName:"webgl",kernelFunc:z9};var Us=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){this.userCode=` const ivec2 strides = ivec2(${i}, ${p}); const ivec2 pads = ivec2(${d}, ${f}); @@ -1681,7 +1681,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelNam // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); - if (value ${R} currMinMaxValue) { + if (value >= currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`}; @@ -1780,7 +1780,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelNam } setOutput(${S}); } - `}},bu=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t==="avg",S="0.0";if(C||(S="-1.0 / 1e-20"),o){let P=">=";this.userCode=` + `}},bu=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t==="avg",S="0.0";if(C||(S="-1.0 / 1e-20"),o){this.userCode=` const ivec3 strides = ivec3(${i}, ${p}, ${u}); const ivec3 pads = ivec3(${g}, ${x}, ${b}); @@ -1831,7 +1831,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelNam // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); - if (value ${P} currMinMaxValue) { + if (value >= currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${f} * ${h} + @@ -2927,7 +2927,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelNam bvec4 isNaN = isnan(x); ${Xr} return result; -`,hJ=xe({opSnippet:dJ,packedOpSnippet:fJ}),XA={kernelName:sn,backendName:"webgl",kernelFunc:hJ};var gJ=` +`,hJ=xe({opSnippet:dJ,packedOpSnippet:fJ}),XA={kernelName:"Cos",backendName:"webgl",kernelFunc:hJ};var gJ=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,xJ=xe({opSnippet:gJ}),YA={kernelName:an,backendName:"webgl",kernelFunc:xJ};var qh=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n==="bilinear"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,"(y2-y1) * height_ratio",`y1*${f} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${f}`],[C,S,k]=m>1?[`${(p-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` @@ -3398,7 +3398,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,RJ=xe({opSnippet:EJ,packedOpSnippet:$J}),cF={kernelName:hn,backendName:"webgl",kernelFunc:RJ};var DJ="return (b >= 0.0) ? a : a * (b + 1.0);",AJ=` +`,RJ=xe({opSnippet:EJ,packedOpSnippet:$J}),cF={kernelName:"Elu",backendName:"webgl",kernelFunc:RJ};var DJ="return (b >= 0.0) ? a : a * (b + 1.0);",AJ=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,FJ=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=A().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(AJ,o.shape,n.shape):new Pr(DJ,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},lF={kernelName:Xa,backendName:"webgl",kernelFunc:FJ};var PJ=` @@ -3418,7 +3418,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelNam x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,BJ=xe({opSnippet:LJ}),dF={kernelName:gn,backendName:"webgl",kernelFunc:BJ};var zJ=Fo+` +`,BJ=xe({opSnippet:LJ}),dF={kernelName:"Erf",backendName:"webgl",kernelFunc:BJ};var zJ=Fo+` return exp(x); `,VJ=` vec4 result = exp(x); @@ -3429,7 +3429,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelNam result.a = isNaN.a ? x.a : result.a; return result; -`,kv=xe({opSnippet:zJ,packedOpSnippet:VJ,cpuKernelImpl:HR,dtype:"float32"}),fF={kernelName:yn,backendName:"webgl",kernelFunc:kv};function eg(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var hF={kernelName:na,backendName:"webgl",kernelFunc:eg};var gF="return exp(x) - 1.0;",WJ=xe({opSnippet:gF,packedOpSnippet:gF,cpuKernelImpl:KR}),xF={kernelName:bn,backendName:"webgl",kernelFunc:WJ};var nm=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` +`,kv=xe({opSnippet:zJ,packedOpSnippet:VJ,cpuKernelImpl:HR,dtype:"float32"}),fF={kernelName:"Exp",backendName:"webgl",kernelFunc:kv};function eg(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var hF={kernelName:na,backendName:"webgl",kernelFunc:eg};var gF="return exp(x) - 1.0;",WJ=xe({opSnippet:gF,packedOpSnippet:gF,cpuKernelImpl:KR}),xF={kernelName:bn,backendName:"webgl",kernelFunc:WJ};var nm=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` const float exponentMultiplier = ${s}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -3462,7 +3462,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelNam ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function tg(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new nm("real",p,e),c=new nm("imag",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,"float32"),d=t.runWebGLProgram(c,l,"float32"),f=Or({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function UJ(r){let{inputs:e,backend:t}=r,{input:o}=e;return tg(o,!1,t)}var yF={kernelName:zi,backendName:"webgl",kernelFunc:UJ};var rg=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function tg(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new nm("real",p,e),c=new nm("imag",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,"float32"),d=t.runWebGLProgram(c,l,"float32"),f=Or({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function UJ(r){let{inputs:e,backend:t}=r,{input:o}=e;return tg(o,!1,t)}var yF={kernelName:"FFT",backendName:"webgl",kernelFunc:UJ};var rg=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); @@ -3608,7 +3608,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelNam result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; -`,wee=xe({opSnippet:bee,packedOpSnippet:Cee,cpuKernelImpl:tD}),LF={kernelName:Fn,backendName:"webgl",kernelFunc:wee};var See=Fo+` +`,wee=xe({opSnippet:bee,packedOpSnippet:Cee,cpuKernelImpl:tD}),LF={kernelName:"Log",backendName:"webgl",kernelFunc:wee};var See=Fo+` return log(1.0 + x); `,Iee=xe({opSnippet:See}),BF={kernelName:Pn,backendName:"webgl",kernelFunc:Iee};var vee="return float(a >= 1.0 && b >= 1.0);",kee=` return vec4( @@ -3700,7 +3700,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelNam vec4 result = xAtOutputCoords * ${p}; setOutput(result); } - `}};var Dee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=A().getBool("WEBGL_PACK_NORMALIZATION")?new pg(n.shape,s,a,i,p):new ug(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},UF={kernelName:Bn,backendName:"webgl",kernelFunc:Dee};var cg=class{constructor(e,t,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=` + `}};var Dee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=A().getBool("WEBGL_PACK_NORMALIZATION")?new pg(n.shape,s,a,i,p):new 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dte(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=pD(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],"int32",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var I3={kernelName:Hp,backendName:"webgl",kernelFunc:dte};function fte(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=cD(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],"int32",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var v3={kernelName:Kp,backendName:"webgl",kernelFunc:fte};function hte(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=lD(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var k3={kernelName:qp,backendName:"webgl",kernelFunc:hte};var Av=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=mD(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},N3={kernelName:ma,backendName:"webgl",kernelFunc:Av};var gte="return 1.0 / x;",xte=xe({opSnippet:gte}),T3={kernelName:ns,backendName:"webgl",kernelFunc:xte};var yte=Wt+` return (x < 0.0) ? 0.0 : x; `,bte=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); @@ -4651,7 +4651,7 @@ return a / b;`,Yee=` bvec4 isNaN = isnan(x); ${Xr} return result; -`,Hte=xe({opSnippet:Ute,packedOpSnippet:Gte}),G3={kernelName:gs,backendName:"webgl",kernelFunc:Hte};var Kte=` 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NO={kernelName:un,backendName:"wasm",setupFunc:hoe,kernelFunc:goe};var TO;function xoe(r){TO=r.wasm.cwrap(pn,null,["number","number","number","number","number","number"])}function yoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o,p=n.shape.length;y.assert(n.dtype==="float32"||n.dtype==="int32",()=>`cumsum does not support ${n.dtype} tensors in the WASM backend`);let u=w.getAxesPermutation([s],p),c=n;u!==null&&(c=ho({inputs:{x:n},attrs:{perm:u},backend:t}));let l=w.getInnerMostAxes(1,p)[0];w.assertAxesAreInnerMostDims("cumsum",[l],p);let m=t.makeOutput(c.shape,c.dtype),d=c.shape[l],f=t.dataIdMap.get(c.dataId).id,h=t.dataIdMap.get(m.dataId).id;TO(f,a?1:0,i?1:0,d,h,we[n.dtype]);let g=m;if(u!==null){let x=w.getUndoAxesPermutation(u);g=ho({inputs:{x:m},attrs:{perm:x},backend:t}),t.disposeData(c.dataId),t.disposeData(m.dataId)}return g}var _O={kernelName:pn,backendName:"wasm",setupFunc:xoe,kernelFunc:yoe};var EO;function 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voe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:c,dimRoundingMode:l}=t,m=u==null?[1,1]:u,d=w.computeConv2DInfo(n.shape,s.shape,p,m,c,l,!0),f=d.filterHeight,h=d.filterWidth,g=d.padInfo.top,x=d.padInfo.right,b=d.padInfo.bottom,C=d.padInfo.left,S=d.dilationHeight,k=d.dilationWidth,_=d.strideHeight,$=d.strideWidth,R=d.inChannels,D=d.outChannels,P=d.padInfo.type==="SAME"?1:0;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let O=o.makeOutput(d.outShape,"float32"),M=o.dataIdMap.get(O.dataId).id;return AO(a,n.shape[0],n.shape[1],n.shape[2],i,f,h,g,x,b,C,P,S,k,_,$,R,D,M),O}var FO={kernelName:mn,backendName:"wasm",setupFunc:Ioe,kernelFunc:voe};var PO;function koe(r){PO=r.wasm.cwrap("Diag",null,["number","number","number","number"])}function Noe(r){let{inputs:e,backend:t}=r,{x:o}=e,n=y.sizeFromShape(o.shape),s=t.makeOutput([...o.shape,...o.shape],o.dtype);return PO(t.dataIdMap.get(o.dataId).id,we[o.dtype],n,t.dataIdMap.get(s.dataId).id),s}var OO={kernelName:oa,backendName:"wasm",setupFunc:koe,kernelFunc:Noe};var MO;function Toe(r){MO=r.wasm.cwrap(dn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _oe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o;if(n.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. 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Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=t.makeOutput(s.shape,s.dtype);return BO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var zO={kernelName:Li,backendName:"wasm",setupFunc:Eoe,kernelFunc:$oe};var VO;function Roe(r){VO=r.wasm.cwrap(Mi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Doe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o;if(n.dtype!==s.dtype||n.dtype!==a.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=t.makeOutput(n.shape,n.dtype);return VO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var WO={kernelName:Mi,backendName:"wasm",setupFunc:Roe,kernelFunc:Doe};var UO=he(hn);var GO;function Aoe(r){GO=r.wasm.cwrap(Xa,null,["number","number","number"])}function Foe(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=t.makeOutput(n.shape,"float32"),a=i=>t.dataIdMap.get(i.dataId).id;return GO(a(n),a(o),a(s)),s}var HO={kernelName:Xa,backendName:"wasm",setupFunc:Aoe,kernelFunc:Foe};var Poe=!1,KO=Ge(xn,Poe,"bool");var qO=he(gn);var jO=he(yn,"float32");function Lg(r){let{inputs:e,attrs:t,backend:o}=r,{input:n}=e,{dim:s}=t,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),zt({inputs:{x:n},backend:o,attrs:{shape:i}})}var XO={kernelName:na,backendName:"wasm",kernelFunc:Lg};var YO=he(bn,"float32");function Mv(r){let{attrs:{shape:e,value:t},backend:o}=r,{attrs:{dtype:n}}=r;n=n||y.inferDtype(t);let s=o.makeOutput(e,n);return o.typedArrayFromHeap(s).fill(t),s}var QO={kernelName:sa,backendName:"wasm",kernelFunc:Mv};var ZO;function Ooe(r){ZO=r.wasm.cwrap(Cn,null,["number","number","number","number","number","number"])}function Moe(r){let{inputs:e,backend:t}=r,{image:o}=e,n=t.makeOutput(o.shape,o.dtype),s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return ZO(s,i,p,u,c,a),n}var JO={kernelName:Cn,backendName:"wasm",kernelFunc:Moe,setupFunc:Ooe};var eM=he(wn);var Loe=!1,tM=Ge(Sn,Loe);var rM;function Boe(r){rM=r.wasm.cwrap(In,null,["number","number","number","number","number","number","number"])}function zoe(r){let{backend:e,inputs:t,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=t,c=e.dataIdMap.get(s.dataId).id,l=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,d=p!=null?e.dataIdMap.get(p.dataId).id:0,f=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return rM(c,l,m,d,f,n,g),h}var oM={kernelName:In,backendName:"wasm",setupFunc:Boe,kernelFunc:zoe};var nM;function Voe(r){nM=r.wasm.cwrap(Io,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Woe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=wu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,$=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type==="SAME"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. 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joe(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,indices:s}=t,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=e.readSync(s.dataId),c=n.shape[p];for(let D=0;D=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let l=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=zt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:e}),d=y.sizeFromShape(s.shape),f=zt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:e}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=e.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,C=e.dataIdMap.get(m.dataId).id,k=e.dataIdMap.get(f.dataId).id,_=e.dataIdMap.get(g.dataId).id,$=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return cM(C,we[n.dtype],$,x,k,l.batchSize,R,_),e.disposeData(m.dataId),e.disposeData(f.dataId),g.shape=l.outputShape,g}var lM={kernelName:aa,backendName:"wasm",setupFunc:qoe,kernelFunc:joe};var Xoe=!1,mM=Ge(kn,Xoe,"bool");var Yoe=!1,dM=Ge(Nn,Yoe,"bool");var fM=he(Tn,"bool");var hM=he(_n,"bool");var gM=he(En,"bool");var xM;function Qoe(r){xM=r.wasm.cwrap($n,null,["number","number","number","number"])}function Zoe(r){let{inputs:{x:e},attrs:{alpha:t},backend:o}=r,n=o.dataIdMap.get(e.dataId).id,s=o.makeOutput(e.shape,"float32");if(y.sizeFromShape(e.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;xM(n,we[e.dtype],t,a)}return s}var yM={kernelName:$n,backendName:"wasm",setupFunc:Qoe,kernelFunc:Zoe};var Joe=!1,bM=Ge(Rn,Joe,"bool");var ene=!1,CM=Ge(Dn,ene,"bool");var wM;function tne(r){wM=r.wasm.cwrap(An,null,["number","number","number","number"])}function rne(r){let{attrs:e,backend:t}=r,{start:o,stop:n,num:s}=e,a=Math.floor(s),i=t.makeOutput([a],"float32");return wM(t.dataIdMap.get(i.dataId).id,o,n,a),i}var SM={kernelName:An,backendName:"wasm",setupFunc:tne,kernelFunc:rne};var IM=he(Fn);var vM=he(Pn);var one=!1,kM=Ge(On,one,"bool");var NM=he(Mn);var nne=!1,TM=Ge(Ln,nne,"bool");var sne=!1,_M=Ge(R0,sne,"bool");var EM;function ane(r){EM=r.wasm.cwrap(Bn,null,["number","number","number","number","number","number","number"])}function ine(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o;if(n.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=t.makeOutput(n.shape,n.dtype);return EM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var $M={kernelName:Bn,backendName:"wasm",setupFunc:ane,kernelFunc:ine};var RM;function une(r){RM=r.wasm.cwrap(Ya,null,["number","number","number","number","number","number","number","number","number"])}function pne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o;if(n.dtype!=="float32"||s.dtype!=="float32"||a.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let l=t.makeOutput(n.shape,n.dtype);return RM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,a.shape[3],i,p,u,c),l}var DM={kernelName:Ya,backendName:"wasm",setupFunc:une,kernelFunc:pne};var AM;function cne(r){AM=r.wasm.cwrap(zn,null,["number","number","number","number"])}function lne(r){let{backend:e,inputs:t,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=t,p=e.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e);if(d){let C=e.dataIdMap.get(c.dataId).id;u=c,p=C}let f=u.shape.length;w.assertAxesAreInnerMostDims("max",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;AM(p,we[a.dtype],x,C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var FM={kernelName:zn,backendName:"wasm",setupFunc:cne,kernelFunc:lne};var mne=!1,PM=Ge(Vn,mne);var OM;function dne(r){OM=r.wasm.cwrap(Wn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fne(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=t,c=w.computePool2DInfo(n.shape,a,i,1,p,u),l=c.filterHeight,m=c.filterWidth,d=c.padInfo.top,f=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,C=c.strideHeight,S=c.strideWidth,k=c.inChannels,_=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let $=o.makeOutput(c.outShape,"float32"),R=o.dataIdMap.get($.dataId).id;return OM(s,n.shape[0],n.shape[1],n.shape[2],l,m,d,f,h,g,x,b,C,S,k,_,R),$}var MM={kernelName:Wn,backendName:"wasm",setupFunc:dne,kernelFunc:fne};var LM;function hne(r){LM=r.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function gne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=w.computePool3DInfo(n.shape,s,a,1,i,p,u),l=t.makeOutput(c.outShape,n.dtype);return LM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var BM={kernelName:ia,backendName:"wasm",setupFunc:hne,kernelFunc:gne};var zM;function xne(r){zM=r.wasm.cwrap("MaxPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function yne(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=t.makeOutput(s.shape,s.dtype);return zM(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var VM={kernelName:Gi,backendName:"wasm",setupFunc:xne,kernelFunc:yne};var WM;function bne(r){WM=r.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Cne(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool2DInfo(s.shape,a,i,1,p,u),l=t.makeOutput(s.shape,s.dtype);return WM(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),l}var UM={kernelName:Ui,backendName:"wasm",setupFunc:bne,kernelFunc:Cne};var GM;function wne(r){GM=r.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Sne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,includeBatchInIndex:p}=o;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,[1,1],i),l=t.makeOutput(c.outShape,n.dtype),m=t.makeOutput(c.outShape,"int32");return GM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,t.dataIdMap.get(m.dataId).id,we[n.dtype],p,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[l,m]}var HM={kernelName:ua,backendName:"wasm",setupFunc:wne,kernelFunc:Sne};var KM;function Ine(r){KM=r.wasm.cwrap(Un,null,["number, number, number"])}function vne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e),f=l;if(d){let S=e.dataIdMap.get(c.dataId).id;S!==i&&(u=c,p=S,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=Mr({backend:e,inputs:{x:u},attrs:{dtype:"float32"}}),p=e.dataIdMap.get(b.dataId).id);let C=e.makeOutput(h,"float32");if(y.sizeFromShape(u.shape)!==0){let S=e.dataIdMap.get(C.dataId).id;KM(p,x,S)}if(d&&e.disposeData(c.dataId),s){let S=w.expandShapeToKeepDim(C.shape,m);C.shape=S}return u.dtype!=="float32"&&e.disposeData(b.dataId),C}var qM={kernelName:Un,backendName:"wasm",setupFunc:Ine,kernelFunc:vne};var jM;function kne(r){jM=r.wasm.cwrap(Gn,null,["number","number","number","number"])}function Nne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e);if(d){let C=e.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C)}let f=u.shape.length;w.assertAxesAreInnerMostDims("min",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;jM(p,we[a.dtype],x,C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var XM={kernelName:Gn,backendName:"wasm",setupFunc:kne,kernelFunc:Nne};var Tne=!1,YM=Ge(Hn,Tne);var Lv;(function(r){r[r.reflect=0]="reflect",r[r.symmetric=1]="symmetric"})(Lv||(Lv={}));var QM;function _ne(r){QM=r.wasm.cwrap(Kn,null,["number","array","number","number","array","array","number","number"])}function Ene(r){let{inputs:{x:e},backend:t,attrs:{paddings:o,mode:n}}=r,s=o.map((f,h)=>f[0]+e.shape[h]+f[1]),a=t.dataIdMap.get(e.dataId).id,i=t.makeOutput(s,e.dtype),p=t.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(e.shape).buffer),c=o.map(f=>f[0]),l=o.map(f=>f[1]),m=new Uint8Array(new Int32Array(c).buffer),d=new Uint8Array(new 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tL(t.dataIdMap.get(p.dataId).id,u,c,s,a,t.dataIdMap.get(l.dataId).id),i||t.disposeData(p.dataId),l}var rL={kernelName:jn,backendName:"wasm",setupFunc:Rne,kernelFunc:Dne};var oL=Ge(qn,!0);var Ane=!0,nL=Ge(Xn,Ane);var sL=he(pa);function qc(r,e){let t=new Int32Array(r.wasm.HEAPU8.buffer,e,4),o=t[0],n=t[1],s=t[2],a=t[3];return r.wasm._free(e),{pSelectedIndices:o,selectedSize:n,pSelectedScores:s,pValidOutputs:a}}var aL;function Fne(r){aL=r.wasm.cwrap(Qn,"number",["number","number","number","number","number"])}function Pne(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a}=o,{boxes:i,scores:p}=t,u=e.dataIdMap.get(i.dataId).id,c=e.dataIdMap.get(p.dataId).id,l=aL(u,c,s,n,a),{pSelectedIndices:m,selectedSize:d,pSelectedScores:f,pValidOutputs:h}=qc(e,l);return e.wasm._free(f),e.wasm._free(h),e.makeOutput([d],"int32",m)}var iL={kernelName:Qn,backendName:"wasm",setupFunc:Fne,kernelFunc:Pne};var uL;function 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x=e.makeOutput([f],"int32",d),b=e.makeOutput([f],"float32",h);return[x,b]}var lL={kernelName:Zn,backendName:"wasm",setupFunc:Lne,kernelFunc:Bne};var zne=!1,mL=Ge(Yn,zne,"bool");var dL;function Vne(r){dL=r.wasm.cwrap(Jn,null,["number","number","number","number","number"])}function Wne(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=t.makeOutput([...n.shape,a],s),c=t.dataIdMap.get(u.dataId).id,m=t.dataIdMap.get(n.dataId).id;return dL(m,a,i,p,c),u}var fL={kernelName:Jn,backendName:"wasm",setupFunc:Vne,kernelFunc:Wne};function Une(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype);return t.typedArrayFromHeap(o).fill(1),o}var hL={kernelName:ca,backendName:"wasm",kernelFunc:Une};function Gne(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Lg({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching 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Bg={kernelName:es,backendName:"wasm",kernelFunc:Kne,setupFunc:Hne};var qne=!1,yL=Ge(ts,qne);var bL;function jne(r){bL=r.wasm.cwrap(rs,null,["number","number","number"])}function Xne(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,i=s,p=o,u=p;p.dtype!=="float32"&&(u=Mr({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),i=t.dataIdMap.get(u.dataId).id);let c=t.makeOutput(o.shape,"float32"),l=t.dataIdMap.get(c.dataId).id;return bL(i,a,l),p.dtype!=="float32"&&t.disposeData(u.dataId),c}var CL={kernelName:rs,backendName:"wasm",setupFunc:jne,kernelFunc:Xne};var wL;function Yne(r){wL=r.wasm.cwrap(os,null,["number","number","number","number"])}function Qne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e),f=l;if(d){let C=e.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;wL(p,x,we[b.dtype],C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var SL={kernelName:os,backendName:"wasm",setupFunc:Yne,kernelFunc:Qne};var Zne=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=up(o,n,s,a),p=e.makeOutput([i.length],a);return e.typedArrayFromHeap(p).set(i),p},IL={kernelName:ma,backendName:"wasm",kernelFunc:Zne};var Jne=!0,vL=Ge(fn,Jne);var kL=he(ns);var NL=he(ss);var TL=he(us);var _L;function ese(r){_L=r.wasm.cwrap(is,null,["number","number","number","number","number","number","number","number","number","number"])}function 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Uint8Array(new Int32Array(n.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),a),u!=null&&t.disposeData(u.dataId),i}var RL={kernelName:Ja,backendName:"wasm",setupFunc:rse,kernelFunc:ose};var DL;function nse(r){DL=r.wasm.cwrap(as,null,["number","number","number","number","number","number","number","number","number","number"])}function sse(r){let{backend:e,inputs:t,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,[c,l,m,d]=n.shape,f=[c,p,u,d],h=e.makeOutput(f,"float32");if(y.sizeFromShape(n.shape)===0)return h;let g=e.dataIdMap.get(n.dataId),x;g.dtype!=="float32"&&(x=Mr({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),g=e.dataIdMap.get(x.dataId));let b=g.id,C=e.dataIdMap.get(h.dataId).id;return DL(b,c,l,m,d,p,u,s?1:0,a?1:0,C),x!=null&&e.disposeData(x.dataId),h}var AL={kernelName:as,backendName:"wasm",setupFunc:nse,kernelFunc:sse};var FL;function ase(r){FL=r.wasm.cwrap(Za,null,["number","number","number","array","array","boolean"])}function ise(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=t.makeOutput(n.shape,"float32"),p=t.dataIdMap.get(n.dataId),u;return p.dtype!=="float32"&&(u=Mr({backend:t,inputs:{x:n},attrs:{dtype:"float32"}}),p=t.dataIdMap.get(u.dataId)),FL(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(i.dataId).id,new Uint8Array(new Int32Array(n.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),a),u!=null&&t.disposeData(u.dataId),i}var PL={kernelName:Za,backendName:"wasm",setupFunc:ase,kernelFunc:ise};var OL;function use(r){OL=r.wasm.cwrap(ps,null,["number","array","number","array","number","number"])}function pse(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=y.parseAxisParam(s,n.shape);if(n.shape.length===0)return Np({inputs:{x:n},backend:t});let i=t.makeOutput(n.shape,n.dtype),p=t.dataIdMap.get(n.dataId).id,u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),l=new Uint8Array(new Int32Array(n.shape).buffer);OL(p,c,a.length,l,n.shape.length,u);let m=zt({inputs:{x:i},attrs:{shape:n.shape},backend:t});return t.disposeData(i.dataId),m}var ML={kernelName:ps,backendName:"wasm",kernelFunc:pse,setupFunc:use};var LL;function cse(r){LL=r.wasm.cwrap(Ds,null,["number","number","number","number","number","number","number","number","array","number","number"])}function lse(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{radians:s,fillValue:a,center:i}=o,p=t.makeOutput(n.shape,n.dtype),u=t.dataIdMap.get(n.dataId).id,c=t.dataIdMap.get(p.dataId).id,[l,m,d,f]=n.shape,[h,g]=w.getImageCenter(i,m,d),x=a===0,b=255,C=typeof a=="number"?[a,a,a,x?0:b]:[...a,b],S=new Uint8Array(new Int32Array(C).buffer);return LL(u,l,m,d,f,s,h,g,S,C.length,c),p}var BL={kernelName:Ds,backendName:"wasm",kernelFunc:lse,setupFunc:cse};var zL=he(cs);var VL=he(ls);var WL;function mse(r){WL=r.wasm.cwrap(ms,null,["number","number","number","number","number","number","array","number","number"])}function 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NO={kernelName:un,backendName:"wasm",setupFunc:hoe,kernelFunc:goe};var TO;function xoe(r){TO=r.wasm.cwrap(pn,null,["number","number","number","number","number","number"])}function yoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o,p=n.shape.length;y.assert(n.dtype==="float32"||n.dtype==="int32",()=>`cumsum does not support ${n.dtype} tensors in the WASM backend`);let u=w.getAxesPermutation([s],p),c=n;u!==null&&(c=ho({inputs:{x:n},attrs:{perm:u},backend:t}));let l=w.getInnerMostAxes(1,p)[0];w.assertAxesAreInnerMostDims("cumsum",[l],p);let m=t.makeOutput(c.shape,c.dtype),d=c.shape[l],f=t.dataIdMap.get(c.dataId).id,h=t.dataIdMap.get(m.dataId).id;TO(f,a?1:0,i?1:0,d,h,we[n.dtype]);let g=m;if(u!==null){let x=w.getUndoAxesPermutation(u);g=ho({inputs:{x:m},attrs:{perm:x},backend:t}),t.disposeData(c.dataId),t.disposeData(m.dataId)}return g}var _O={kernelName:pn,backendName:"wasm",setupFunc:xoe,kernelFunc:yoe};var EO;function 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voe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:c,dimRoundingMode:l}=t,m=u==null?[1,1]:u,d=w.computeConv2DInfo(n.shape,s.shape,p,m,c,l,!0),f=d.filterHeight,h=d.filterWidth,g=d.padInfo.top,x=d.padInfo.right,b=d.padInfo.bottom,C=d.padInfo.left,S=d.dilationHeight,k=d.dilationWidth,_=d.strideHeight,$=d.strideWidth,R=d.inChannels,D=d.outChannels,P=d.padInfo.type==="SAME"?1:0;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let O=o.makeOutput(d.outShape,"float32"),M=o.dataIdMap.get(O.dataId).id;return AO(a,n.shape[0],n.shape[1],n.shape[2],i,f,h,g,x,b,C,P,S,k,_,$,R,D,M),O}var FO={kernelName:mn,backendName:"wasm",setupFunc:Ioe,kernelFunc:voe};var PO;function koe(r){PO=r.wasm.cwrap("Diag",null,["number","number","number","number"])}function Noe(r){let{inputs:e,backend:t}=r,{x:o}=e,n=y.sizeFromShape(o.shape),s=t.makeOutput([...o.shape,...o.shape],o.dtype);return PO(t.dataIdMap.get(o.dataId).id,we[o.dtype],n,t.dataIdMap.get(s.dataId).id),s}var OO={kernelName:oa,backendName:"wasm",setupFunc:koe,kernelFunc:Noe};var MO;function Toe(r){MO=r.wasm.cwrap(dn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _oe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o;if(n.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. 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Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=t.makeOutput(s.shape,s.dtype);return BO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var zO={kernelName:Li,backendName:"wasm",setupFunc:Eoe,kernelFunc:$oe};var VO;function Roe(r){VO=r.wasm.cwrap(Mi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Doe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o;if(n.dtype!==s.dtype||n.dtype!==a.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,"NHWC",u),l=t.makeOutput(n.shape,n.dtype);return VO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var WO={kernelName:Mi,backendName:"wasm",setupFunc:Roe,kernelFunc:Doe};var UO=he("Elu");var GO;function Aoe(r){GO=r.wasm.cwrap(Xa,null,["number","number","number"])}function Foe(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=t.makeOutput(n.shape,"float32"),a=i=>t.dataIdMap.get(i.dataId).id;return GO(a(n),a(o),a(s)),s}var HO={kernelName:Xa,backendName:"wasm",setupFunc:Aoe,kernelFunc:Foe};var Poe=!1,KO=Ge(xn,Poe,"bool");var qO=he("Erf");var jO=he("Exp","float32");function Lg(r){let{inputs:e,attrs:t,backend:o}=r,{input:n}=e,{dim:s}=t,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),zt({inputs:{x:n},backend:o,attrs:{shape:i}})}var XO={kernelName:na,backendName:"wasm",kernelFunc:Lg};var YO=he(bn,"float32");function Mv(r){let{attrs:{shape:e,value:t},backend:o}=r,{attrs:{dtype:n}}=r;n=n||y.inferDtype(t);let s=o.makeOutput(e,n);return o.typedArrayFromHeap(s).fill(t),s}var QO={kernelName:sa,backendName:"wasm",kernelFunc:Mv};var ZO;function Ooe(r){ZO=r.wasm.cwrap(Cn,null,["number","number","number","number","number","number"])}function Moe(r){let{inputs:e,backend:t}=r,{image:o}=e,n=t.makeOutput(o.shape,o.dtype),s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return ZO(s,i,p,u,c,a),n}var JO={kernelName:Cn,backendName:"wasm",kernelFunc:Moe,setupFunc:Ooe};var eM=he(wn);var Loe=!1,tM=Ge(Sn,Loe);var rM;function Boe(r){rM=r.wasm.cwrap(In,null,["number","number","number","number","number","number","number"])}function zoe(r){let{backend:e,inputs:t,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=t,c=e.dataIdMap.get(s.dataId).id,l=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,d=p!=null?e.dataIdMap.get(p.dataId).id:0,f=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return rM(c,l,m,d,f,n,g),h}var oM={kernelName:In,backendName:"wasm",setupFunc:Boe,kernelFunc:zoe};var nM;function Voe(r){nM=r.wasm.cwrap(Io,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Woe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=wu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,$=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type==="SAME"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. 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Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,"float32"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return aM(x,j,q,Y,b,k,_,S,$,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var iM={kernelName:vo,backendName:"wasm",setupFunc:Uoe,kernelFunc:Goe};var uM;function Hoe(r){uM=r.wasm.cwrap(vn,null,["number","number","number","number","number","number","array","number"])}function Koe(r){let{backend:e,inputs:t}=r,{params:o,indices:n}=t,[s,a,i,p]=af.prepareAndValidate(o,n),u=e.makeOutput(s,o.dtype);if(a===0)return u;let c=n.shape,l=c[c.length-1],d=e.dataIdMap.get(o.dataId).id,h=e.dataIdMap.get(n.dataId).id,g=new Uint8Array(new Int32Array(p).buffer),x=e.dataIdMap.get(u.dataId).id;return uM(d,we[o.dtype],h,a,l,i,g,x),u}var pM={kernelName:vn,backendName:"wasm",setupFunc:Hoe,kernelFunc:Koe};var cM;function qoe(r){cM=r.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function joe(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,indices:s}=t,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=e.readSync(s.dataId),c=n.shape[p];for(let D=0;D=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let l=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=zt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:e}),d=y.sizeFromShape(s.shape),f=zt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:e}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=e.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,C=e.dataIdMap.get(m.dataId).id,k=e.dataIdMap.get(f.dataId).id,_=e.dataIdMap.get(g.dataId).id,$=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return cM(C,we[n.dtype],$,x,k,l.batchSize,R,_),e.disposeData(m.dataId),e.disposeData(f.dataId),g.shape=l.outputShape,g}var lM={kernelName:aa,backendName:"wasm",setupFunc:qoe,kernelFunc:joe};var Xoe=!1,mM=Ge(kn,Xoe,"bool");var Yoe=!1,dM=Ge(Nn,Yoe,"bool");var fM=he(Tn,"bool");var hM=he(_n,"bool");var gM=he(En,"bool");var xM;function Qoe(r){xM=r.wasm.cwrap($n,null,["number","number","number","number"])}function Zoe(r){let{inputs:{x:e},attrs:{alpha:t},backend:o}=r,n=o.dataIdMap.get(e.dataId).id,s=o.makeOutput(e.shape,"float32");if(y.sizeFromShape(e.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;xM(n,we[e.dtype],t,a)}return s}var yM={kernelName:$n,backendName:"wasm",setupFunc:Qoe,kernelFunc:Zoe};var Joe=!1,bM=Ge(Rn,Joe,"bool");var ene=!1,CM=Ge(Dn,ene,"bool");var wM;function tne(r){wM=r.wasm.cwrap(An,null,["number","number","number","number"])}function rne(r){let{attrs:e,backend:t}=r,{start:o,stop:n,num:s}=e,a=Math.floor(s),i=t.makeOutput([a],"float32");return wM(t.dataIdMap.get(i.dataId).id,o,n,a),i}var SM={kernelName:An,backendName:"wasm",setupFunc:tne,kernelFunc:rne};var IM=he("Log");var vM=he(Pn);var one=!1,kM=Ge(On,one,"bool");var NM=he(Mn);var nne=!1,TM=Ge(Ln,nne,"bool");var sne=!1,_M=Ge(R0,sne,"bool");var EM;function ane(r){EM=r.wasm.cwrap("LRN",null,["number","number","number","number","number","number","number"])}function ine(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o;if(n.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=t.makeOutput(n.shape,n.dtype);return EM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var $M={kernelName:"LRN",backendName:"wasm",setupFunc:ane,kernelFunc:ine};var RM;function une(r){RM=r.wasm.cwrap(Ya,null,["number","number","number","number","number","number","number","number","number"])}function pne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o;if(n.dtype!=="float32"||s.dtype!=="float32"||a.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let l=t.makeOutput(n.shape,n.dtype);return RM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,a.shape[3],i,p,u,c),l}var DM={kernelName:Ya,backendName:"wasm",setupFunc:une,kernelFunc:pne};var AM;function cne(r){AM=r.wasm.cwrap("Max",null,["number","number","number","number"])}function lne(r){let{backend:e,inputs:t,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=t,p=e.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e);if(d){let C=e.dataIdMap.get(c.dataId).id;u=c,p=C}let f=u.shape.length;w.assertAxesAreInnerMostDims("max",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;AM(p,we[a.dtype],x,C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var FM={kernelName:"Max",backendName:"wasm",setupFunc:cne,kernelFunc:lne};var mne=!1,PM=Ge(Vn,mne);var OM;function dne(r){OM=r.wasm.cwrap(Wn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fne(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=t,c=w.computePool2DInfo(n.shape,a,i,1,p,u),l=c.filterHeight,m=c.filterWidth,d=c.padInfo.top,f=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,C=c.strideHeight,S=c.strideWidth,k=c.inChannels,_=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. 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Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,[1,1],i),l=t.makeOutput(c.outShape,n.dtype),m=t.makeOutput(c.outShape,"int32");return GM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,t.dataIdMap.get(m.dataId).id,we[n.dtype],p,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[l,m]}var HM={kernelName:ua,backendName:"wasm",setupFunc:wne,kernelFunc:Sne};var KM;function Ine(r){KM=r.wasm.cwrap(Un,null,["number, number, number"])}function vne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e),f=l;if(d){let S=e.dataIdMap.get(c.dataId).id;S!==i&&(u=c,p=S,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=Mr({backend:e,inputs:{x:u},attrs:{dtype:"float32"}}),p=e.dataIdMap.get(b.dataId).id);let C=e.makeOutput(h,"float32");if(y.sizeFromShape(u.shape)!==0){let S=e.dataIdMap.get(C.dataId).id;KM(p,x,S)}if(d&&e.disposeData(c.dataId),s){let S=w.expandShapeToKeepDim(C.shape,m);C.shape=S}return u.dtype!=="float32"&&e.disposeData(b.dataId),C}var qM={kernelName:Un,backendName:"wasm",setupFunc:Ine,kernelFunc:vne};var jM;function kne(r){jM=r.wasm.cwrap("Min",null,["number","number","number","number"])}function Nne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e);if(d){let C=e.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C)}let f=u.shape.length;w.assertAxesAreInnerMostDims("min",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;jM(p,we[a.dtype],x,C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var XM={kernelName:"Min",backendName:"wasm",setupFunc:kne,kernelFunc:Nne};var Tne=!1,YM=Ge(Hn,Tne);var Lv;(function(r){r[r.reflect=0]="reflect",r[r.symmetric=1]="symmetric"})(Lv||(Lv={}));var QM;function _ne(r){QM=r.wasm.cwrap(Kn,null,["number","array","number","number","array","array","number","number"])}function Ene(r){let{inputs:{x:e},backend:t,attrs:{paddings:o,mode:n}}=r,s=o.map((f,h)=>f[0]+e.shape[h]+f[1]),a=t.dataIdMap.get(e.dataId).id,i=t.makeOutput(s,e.dtype),p=t.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(e.shape).buffer),c=o.map(f=>f[0]),l=o.map(f=>f[1]),m=new Uint8Array(new Int32Array(c).buffer),d=new Uint8Array(new 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tL(t.dataIdMap.get(p.dataId).id,u,c,s,a,t.dataIdMap.get(l.dataId).id),i||t.disposeData(p.dataId),l}var rL={kernelName:jn,backendName:"wasm",setupFunc:Rne,kernelFunc:Dne};var oL=Ge("Mod",!0);var Ane=!0,nL=Ge(Xn,Ane);var sL=he("Neg");function qc(r,e){let t=new Int32Array(r.wasm.HEAPU8.buffer,e,4),o=t[0],n=t[1],s=t[2],a=t[3];return r.wasm._free(e),{pSelectedIndices:o,selectedSize:n,pSelectedScores:s,pValidOutputs:a}}var aL;function Fne(r){aL=r.wasm.cwrap(Qn,"number",["number","number","number","number","number"])}function Pne(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a}=o,{boxes:i,scores:p}=t,u=e.dataIdMap.get(i.dataId).id,c=e.dataIdMap.get(p.dataId).id,l=aL(u,c,s,n,a),{pSelectedIndices:m,selectedSize:d,pSelectedScores:f,pValidOutputs:h}=qc(e,l);return e.wasm._free(f),e.wasm._free(h),e.makeOutput([d],"int32",m)}var iL={kernelName:Qn,backendName:"wasm",setupFunc:Fne,kernelFunc:Pne};var uL;function One(r){uL=r.wasm.cwrap(Qa,"number",["number","number","number","number","number","bool"])}function Mne(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a,padToMaxOutputSize:i}=o,{boxes:p,scores:u}=t,c=e.dataIdMap.get(p.dataId).id,l=e.dataIdMap.get(u.dataId).id,m=uL(c,l,s,n,a,i),{pSelectedIndices:d,selectedSize:f,pSelectedScores:h,pValidOutputs:g}=qc(e,m);e.wasm._free(h);let x=e.makeOutput([f],"int32",d),b=e.makeOutput([],"int32",g);return[x,b]}var pL={kernelName:Qa,backendName:"wasm",setupFunc:One,kernelFunc:Mne};var cL;function Lne(r){cL=r.wasm.cwrap(Zn,"number",["number","number","number","number","number","number"])}function Bne(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a,softNmsSigma:i}=o,{boxes:p,scores:u}=t,c=e.dataIdMap.get(p.dataId).id,l=e.dataIdMap.get(u.dataId).id,m=cL(c,l,s,n,a,i),{pSelectedIndices:d,selectedSize:f,pSelectedScores:h,pValidOutputs:g}=qc(e,m);e.wasm._free(g);let x=e.makeOutput([f],"int32",d),b=e.makeOutput([f],"float32",h);return[x,b]}var lL={kernelName:Zn,backendName:"wasm",setupFunc:Lne,kernelFunc:Bne};var zne=!1,mL=Ge(Yn,zne,"bool");var dL;function Vne(r){dL=r.wasm.cwrap(Jn,null,["number","number","number","number","number"])}function Wne(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=t.makeOutput([...n.shape,a],s),c=t.dataIdMap.get(u.dataId).id,m=t.dataIdMap.get(n.dataId).id;return dL(m,a,i,p,c),u}var fL={kernelName:Jn,backendName:"wasm",setupFunc:Vne,kernelFunc:Wne};function Une(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype);return t.typedArrayFromHeap(o).fill(1),o}var hL={kernelName:ca,backendName:"wasm",kernelFunc:Une};function Gne(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Lg({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching 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Bg={kernelName:es,backendName:"wasm",kernelFunc:Kne,setupFunc:Hne};var qne=!1,yL=Ge("Pow",qne);var bL;function jne(r){bL=r.wasm.cwrap(rs,null,["number","number","number"])}function Xne(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,i=s,p=o,u=p;p.dtype!=="float32"&&(u=Mr({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),i=t.dataIdMap.get(u.dataId).id);let c=t.makeOutput(o.shape,"float32"),l=t.dataIdMap.get(c.dataId).id;return bL(i,a,l),p.dtype!=="float32"&&t.disposeData(u.dataId),c}var CL={kernelName:rs,backendName:"wasm",setupFunc:jne,kernelFunc:Xne};var wL;function Yne(r){wL=r.wasm.cwrap(os,null,["number","number","number","number"])}function Qne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e),f=l;if(d){let C=e.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;wL(p,x,we[b.dtype],C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var SL={kernelName:os,backendName:"wasm",setupFunc:Yne,kernelFunc:Qne};var Zne=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=up(o,n,s,a),p=e.makeOutput([i.length],a);return e.typedArrayFromHeap(p).set(i),p},IL={kernelName:ma,backendName:"wasm",kernelFunc:Zne};var Jne=!0,vL=Ge(fn,Jne);var kL=he(ns);var NL=he(ss);var TL=he(us);var _L;function ese(r){_L=r.wasm.cwrap(is,null,["number","number","number","number","number","number","number","number","number","number"])}function tse(r){let{backend:e,inputs:t,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,[c,l,m,d]=n.shape,f=[c,p,u,d],h=e.dataIdMap.get(n.dataId),g;h.dtype!=="float32"&&(g=Mr({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),h=e.dataIdMap.get(g.dataId));let x=h.id,b=e.makeOutput(f,"float32");if(y.sizeFromShape(n.shape)===0)return b;let C=e.dataIdMap.get(b.dataId).id;return _L(x,c,l,m,d,p,u,s?1:0,a?1:0,C),g!=null&&e.disposeData(g.dataId),b}var EL={kernelName:is,backendName:"wasm",setupFunc:ese,kernelFunc:tse};var $L;function rse(r){$L=r.wasm.cwrap(Ja,null,["number","number","number","array","array","boolean"])}function ose(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=t.makeOutput(n.shape,"float32"),p=t.dataIdMap.get(n.dataId),u;return p.dtype!=="float32"&&(u=Mr({backend:t,inputs:{x:n},attrs:{dtype:"float32"}}),p=t.dataIdMap.get(u.dataId)),$L(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(i.dataId).id,new Uint8Array(new Int32Array(n.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),a),u!=null&&t.disposeData(u.dataId),i}var RL={kernelName:Ja,backendName:"wasm",setupFunc:rse,kernelFunc:ose};var DL;function nse(r){DL=r.wasm.cwrap(as,null,["number","number","number","number","number","number","number","number","number","number"])}function sse(r){let{backend:e,inputs:t,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,[c,l,m,d]=n.shape,f=[c,p,u,d],h=e.makeOutput(f,"float32");if(y.sizeFromShape(n.shape)===0)return h;let g=e.dataIdMap.get(n.dataId),x;g.dtype!=="float32"&&(x=Mr({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),g=e.dataIdMap.get(x.dataId));let b=g.id,C=e.dataIdMap.get(h.dataId).id;return DL(b,c,l,m,d,p,u,s?1:0,a?1:0,C),x!=null&&e.disposeData(x.dataId),h}var AL={kernelName:as,backendName:"wasm",setupFunc:nse,kernelFunc:sse};var FL;function ase(r){FL=r.wasm.cwrap(Za,null,["number","number","number","array","array","boolean"])}function ise(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=t.makeOutput(n.shape,"float32"),p=t.dataIdMap.get(n.dataId),u;return p.dtype!=="float32"&&(u=Mr({backend:t,inputs:{x:n},attrs:{dtype:"float32"}}),p=t.dataIdMap.get(u.dataId)),FL(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(i.dataId).id,new Uint8Array(new Int32Array(n.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),a),u!=null&&t.disposeData(u.dataId),i}var PL={kernelName:Za,backendName:"wasm",setupFunc:ase,kernelFunc:ise};var OL;function use(r){OL=r.wasm.cwrap(ps,null,["number","array","number","array","number","number"])}function pse(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=y.parseAxisParam(s,n.shape);if(n.shape.length===0)return Np({inputs:{x:n},backend:t});let i=t.makeOutput(n.shape,n.dtype),p=t.dataIdMap.get(n.dataId).id,u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),l=new Uint8Array(new 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go=A();go.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);go.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);go.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE",()=>-1);go.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!0);go.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);go.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);go.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);go.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE",()=>!0);go.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG",()=>!1);go.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL",()=>-1);go.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);go.registerFlag("WEBGPU_PRINT_SHADER",()=>"");go.registerFlag("WEBGPU_ENGINE_COMPILE_ONLY",()=>!1);var Hg=class{constructor(e){e&&(this.vendor=e.vendor,this.architecture=e.architecture,this.intelGPUGeneration=this.getIntelGPUGeneration())}getIntelGPUGeneration(){if(this.isIntel()){if(this.architecture.startsWith("gen"))return 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s=ZB(o),a=e*t*s,i=QB(e,t,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[e,t],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(e){if(this.freeTextures.size===0)return;let t=e.width,o=e.height,n=e.format,s=e.usage,a=QB(t,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(e);if(p<0)throw new Error("Cannot release a texture that was never provided by this texture manager");i.splice(p,1);let u=ZB(n),c=t*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return 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ye({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Jr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function et({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype==="complex64"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==fe.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},S={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Ii(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,S],dt(x.dtype,b.dtype))});else{let g=new gm(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new 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h=xo({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype==="string"||i.dtype==="string"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype==="string"?w.fromUint8ToStringArray(l):l,f=a.dtype==="string"?w.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Ii(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:mz,castImpl:dz,ceilImpl:fz,concatImpl:hz,equalImpl:gz,expImpl:xz,expm1Impl:yz,floorImpl:bz,floorDivImpl:Cz,gatherNdImpl:wz,gatherV2Impl:Sz,greaterEqualImpl:Iz,greaterImpl:vz,lessEqualImpl:kz,lessImpl:Nz,logImpl:Tz,maxImpl:_z,maximumImpl:Ez,minimumImpl:$z,multiplyImpl:Rz,negImpl:Dz,notEqualImpl:Az,prodImpl:Fz,rangeImpl:Pz,rsqrtImpl:Oz,scatterImpl:Mz,simpleAbsImpl:Lz,sliceImpl:Bz,stridedSliceImpl:zz,stringNGramsImpl:Vz,subImpl:Wz,tileImpl:Uz,topKImpl:Gz,transposeImpl:Hz,uniqueImpl:rOt}=Ic;var Qie=ye({opType:Z.ABS,cpuKernelImpl:Lz}),Kz={kernelName:"Abs",backendName:"webgpu",kernelFunc:Qie};var Zie=ye({opType:Z.ACOS}),qz={kernelName:Vo,backendName:"webgpu",kernelFunc:Zie};var Jie=ye({opType:Z.ACOSH}),jz={kernelName:Wo,backendName:"webgpu",kernelFunc:Jie};var eue=et({opType:fe.ADD,cpuKernelImpl:mz,supportsComplex:!0}),Xz={kernelName:"Add",backendName:"webgpu",kernelFunc:eue};var tx=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(" + ");return` ${G("index")} { for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; @@ -6004,7 +6004,7 @@ return a / b;`,Yee=` ${n} } } - `}};var rue={mean:"float32",all:"bool",any:"bool"};function eo(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=w.getAxesPermutation(p,s),c=r;u!=null&&(c=xr({inputs:{x:r},attrs:{perm:u},backend:n}),p=w.getInnerMostAxes(p.length,s),a.push(c)),w.assertAxesAreInnerMostDims(o,p,s);let[l,m]=w.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=w.expandShapeToKeepDim(l,i));let f;if((o==="max"||o==="prod")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case"max":let g=_z(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case"prod":let{outVals:x,outShape:b,outDtype:C}=Fz(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=rue[o]||oi(r.dtype),S=[{type:"int32",data:[h]}],k=new nx(b,o,n.device.limits.maxComputeWorkgroupSizeX),_=n.runWebGPUProgram(k,[c],C,S);a.push(_),f=pe({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function oue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,"all",t)}var Zz={kernelName:Go,backendName:"webgpu",kernelFunc:oue};function nue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,"any",t)}var Jz={kernelName:Ho,backendName:"webgpu",kernelFunc:nue};var Yc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=o==="min"?"<":">";let[s,a]=w.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=X(this.outputShape),y.sizeFromShape(a)<32?(this.type="plain",this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Oo(this.inputShape.length-1)}`,o=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let s=0;sn.disposeData(h.dataId)),f}function oue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,"all",t)}var Zz={kernelName:"All",backendName:"webgpu",kernelFunc:oue};function nue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,"any",t)}var Jz={kernelName:"Any",backendName:"webgpu",kernelFunc:nue};var Yc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=o==="min"?"<":">";let[s,a]=w.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=X(this.outputShape),y.sizeFromShape(a)<32?(this.type="plain",this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Oo(this.inputShape.length-1)}`,o=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let s=0;s u32 { return ((a - 1u) / b + 1u); } @@ -6175,7 +6175,7 @@ return a / b;`,Yee=` ${this.computePositions?"setOutputAtIndexI32(index, maxPosition);":`setOutputAtIndex(index, ${t});`} } } - `}};function t0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return eo(n,s,a,"max",t)}var iV={kernelName:zn,backendName:"webgpu",kernelFunc:t0};function r0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,"mean",t)}var uV={kernelName:Un,backendName:"webgpu",kernelFunc:r0};function ax(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return At({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type==="VALID"){let a=r.shape.length,i=pe({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t==="avg"?p=r0({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t==="max",()=>`Invalid pool type ${t}`),p=t0({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=pe({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:"int32",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new sx(e):(t==="avg"?n=new Ba(e,"avg"):(y.assert(t==="max",()=>`Invalid pool type ${t}`),n=new Ba(e,"max")),s.push({type:"int32",data:[e.padInfo.top,e.padInfo.left]},{type:"int32",data:[e.dilationHeight,e.dilationWidth]},{type:"int32",data:[e.inHeight,e.inWidth]},{type:"int32",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function mue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,c=w.computePool2DInfo(n.shape,s,a,1,i,p);return ax(n,c,"avg",t)}var pV={kernelName:Qo,backendName:"webgpu",kernelFunc:mue};function due(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Iu(l,"avg"),d=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.inDepth,l.inHeight,l.inWidth]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var cV={kernelName:Zs,backendName:"webgpu",kernelFunc:due};var ix=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, + `}};function t0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return eo(n,s,a,"max",t)}var iV={kernelName:"Max",backendName:"webgpu",kernelFunc:t0};function r0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,"mean",t)}var uV={kernelName:Un,backendName:"webgpu",kernelFunc:r0};function ax(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return At({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type==="VALID"){let a=r.shape.length,i=pe({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t==="avg"?p=r0({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t==="max",()=>`Invalid pool type ${t}`),p=t0({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=pe({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:"int32",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new sx(e):(t==="avg"?n=new Ba(e,"avg"):(y.assert(t==="max",()=>`Invalid pool type ${t}`),n=new Ba(e,"max")),s.push({type:"int32",data:[e.padInfo.top,e.padInfo.left]},{type:"int32",data:[e.dilationHeight,e.dilationWidth]},{type:"int32",data:[e.inHeight,e.inWidth]},{type:"int32",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function mue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,c=w.computePool2DInfo(n.shape,s,a,1,i,p);return ax(n,c,"avg",t)}var pV={kernelName:Qo,backendName:"webgpu",kernelFunc:mue};function due(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Iu(l,"avg"),d=[{type:"int32",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:"int32",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:"int32",data:[l.inDepth,l.inHeight,l.inWidth]},{type:"int32",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var cV={kernelName:Zs,backendName:"webgpu",kernelFunc:due};var ix=class{constructor(e){this.variableNames=["dy"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="avgPool2DBackprop"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { @@ -6932,7 +6932,7 @@ return a / b;`,Yee=` } setOutputAtIndex(index, dotProd); } - }`}};function Fue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=[u.padInfo.front,u.padInfo.top,u.padInfo.left],l=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new kx(u),d=dt(n.dtype,s.dtype);return t.runWebGPUProgram(m,[n,s],d,l)}var RV={kernelName:on,backendName:"webgpu",kernelFunc:Fue};function Pue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Sx(u),l=[{type:"int32",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.batchSize]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.inDepth]},{type:"int32",data:[u.inHeight]},{type:"int32",data:[u.inWidth]}];return t.runWebGPUProgram(c,[n,s],s.dtype,l)}var DV={kernelName:ja,backendName:"webgpu",kernelFunc:Pue};function Oue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,pad:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,a,1,i),c=new Ix(u),l=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.outChannels]}];return t.runWebGPUProgram(c,[n,s],n.dtype,l)}var AV={kernelName:nn,backendName:"webgpu",kernelFunc:Oue};var Mue=ye({opType:Z.COS}),FV={kernelName:sn,backendName:"webgpu",kernelFunc:Mue};var Lue=ye({opType:Z.COSH}),PV={kernelName:an,backendName:"webgpu",kernelFunc:Lue};var Nx=class{constructor(e,t,o,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` + }`}};function Fue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=[u.padInfo.front,u.padInfo.top,u.padInfo.left],l=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new kx(u),d=dt(n.dtype,s.dtype);return t.runWebGPUProgram(m,[n,s],d,l)}var RV={kernelName:on,backendName:"webgpu",kernelFunc:Fue};function Pue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Sx(u),l=[{type:"int32",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.batchSize]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.inDepth]},{type:"int32",data:[u.inHeight]},{type:"int32",data:[u.inWidth]}];return t.runWebGPUProgram(c,[n,s],s.dtype,l)}var DV={kernelName:ja,backendName:"webgpu",kernelFunc:Pue};function Oue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,pad:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,a,1,i),c=new Ix(u),l=[{type:"int32",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:"int32",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:"int32",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:"int32",data:[u.outDepth]},{type:"int32",data:[u.outHeight]},{type:"int32",data:[u.outWidth]},{type:"int32",data:[u.outChannels]}];return t.runWebGPUProgram(c,[n,s],n.dtype,l)}var AV={kernelName:nn,backendName:"webgpu",kernelFunc:Oue};var Mue=ye({opType:Z.COS}),FV={kernelName:"Cos",backendName:"webgpu",kernelFunc:Mue};var Lue=ye({opType:Z.COSH}),PV={kernelName:an,backendName:"webgpu",kernelFunc:Lue};var Nx=class{constructor(e,t,o,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7437,7 +7437,7 @@ return a / b;`,Yee=` textureStore(outImage, vec2(coords.yx), rgba); } } - `}};function Que(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{canvas:s,options:a}=o,[i,p]=n.shape.slice(0,2),{imageOptions:u}=a||{},c=(u==null?void 0:u.alpha)||1,l=t.device.features.has("bgra8unorm-storage")?"bgra8unorm":"rgba8unorm",m=[i,p],d=new Ox(m,n.dtype,l);s.width=p,s.height=i;let f="webgpu",h=s.getContext(f),g;h||(g=new OffscreenCanvas(p,i),h=g.getContext(f));let x=n.shape.length===3?n.shape[2]:1;h.configure({device:t.device,format:l,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:"premultiplied"});let b="int32",C=t.makeTensorInfo(m,b),S=t.tensorMap.get(C.dataId);S.resource=h.getCurrentTexture(),S.external=!0;let k=[{type:"uint32",data:[x]},{type:"float32",data:[c]}];if(t.runWebGPUProgram(d,[n],b,k,C),g){let _=s.getContext("2d");if(!_)throw new Error("Please make sure this canvas has only been used for 2d or webgpu context!");_.drawImage(g,0,0)}return t.disposeData(C.dataId),n}var YV={kernelName:$u,backendName:"webgpu",kernelFunc:Que};var i0=et({opType:fe.MUL,cpuKernelImpl:Rz,supportsComplex:!0}),QV={kernelName:Xn,backendName:"webgpu",kernelFunc:i0};function u0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,"sum",t)}var ZV={kernelName:Ss,backendName:"webgpu",kernelFunc:u0};function Zue(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=u0({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var JV={kernelName:Bi,backendName:"webgpu",kernelFunc:Zue};var Jue=ye({opType:Z.ELU}),eW={kernelName:hn,backendName:"webgpu",kernelFunc:Jue};var epe=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=new Ii(fe.ELU_DER,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],o.dtype)},tW={kernelName:Xa,backendName:"webgpu",kernelFunc:epe};var tpe=et({opType:fe.EQUAL,dtype:"bool",cpuKernelImpl:gz}),rW={kernelName:xn,backendName:"webgpu",kernelFunc:tpe};var rpe=ye({opType:Z.ERF}),oW={kernelName:gn,backendName:"webgpu",kernelFunc:rpe};var ope=ye({opType:Z.EXP,cpuKernelImpl:xz,dtype:"float32"}),nW={kernelName:yn,backendName:"webgpu",kernelFunc:ope};function Mx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var sW={kernelName:na,backendName:"webgpu",kernelFunc:Mx};var npe=ye({opType:Z.EXPM1,cpuKernelImpl:yz}),aW={kernelName:bn,backendName:"webgpu",kernelFunc:npe};var ym=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return` + `}};function Que(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{canvas:s,options:a}=o,[i,p]=n.shape.slice(0,2),{imageOptions:u}=a||{},c=(u==null?void 0:u.alpha)||1,l=t.device.features.has("bgra8unorm-storage")?"bgra8unorm":"rgba8unorm",m=[i,p],d=new Ox(m,n.dtype,l);s.width=p,s.height=i;let f="webgpu",h=s.getContext(f),g;h||(g=new OffscreenCanvas(p,i),h=g.getContext(f));let x=n.shape.length===3?n.shape[2]:1;h.configure({device:t.device,format:l,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:"premultiplied"});let b="int32",C=t.makeTensorInfo(m,b),S=t.tensorMap.get(C.dataId);S.resource=h.getCurrentTexture(),S.external=!0;let k=[{type:"uint32",data:[x]},{type:"float32",data:[c]}];if(t.runWebGPUProgram(d,[n],b,k,C),g){let _=s.getContext("2d");if(!_)throw new Error("Please make sure this canvas has only been used for 2d or webgpu context!");_.drawImage(g,0,0)}return t.disposeData(C.dataId),n}var YV={kernelName:$u,backendName:"webgpu",kernelFunc:Que};var i0=et({opType:fe.MUL,cpuKernelImpl:Rz,supportsComplex:!0}),QV={kernelName:Xn,backendName:"webgpu",kernelFunc:i0};function u0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,"sum",t)}var ZV={kernelName:"Sum",backendName:"webgpu",kernelFunc:u0};function Zue(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=u0({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var JV={kernelName:Bi,backendName:"webgpu",kernelFunc:Zue};var Jue=ye({opType:Z.ELU}),eW={kernelName:"Elu",backendName:"webgpu",kernelFunc:Jue};var epe=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=new Ii(fe.ELU_DER,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],o.dtype)},tW={kernelName:Xa,backendName:"webgpu",kernelFunc:epe};var tpe=et({opType:fe.EQUAL,dtype:"bool",cpuKernelImpl:gz}),rW={kernelName:xn,backendName:"webgpu",kernelFunc:tpe};var rpe=ye({opType:Z.ERF}),oW={kernelName:"Erf",backendName:"webgpu",kernelFunc:rpe};var ope=ye({opType:Z.EXP,cpuKernelImpl:xz,dtype:"float32"}),nW={kernelName:"Exp",backendName:"webgpu",kernelFunc:ope};function Mx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var sW={kernelName:na,backendName:"webgpu",kernelFunc:Mx};var npe=ye({opType:Z.EXPM1,cpuKernelImpl:yz}),aW={kernelName:bn,backendName:"webgpu",kernelFunc:npe};var ym=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return` fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 { ${this.component==="real"?"return real * expR - imag * expI;":"return real * expI + imag * expR;"} } @@ -7470,7 +7470,7 @@ return a / b;`,Yee=` setOutputAtIndex(index, mulMatDFT(coords[0], coords[1])); } } - `}};function Lx(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=pe({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new ym("real",u),l=new ym("imag",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:"float32",data:[d]},{type:"float32",data:[f]}],g=t.runWebGPUProgram(c,m,"float32",h);i.push(g);let x=t.runWebGPUProgram(l,m,"float32",h);i.push(x);let b=xo({inputs:{real:g,imag:x},backend:t});i.push(b);let C=pe({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(S=>t.disposeData(S.dataId)),C}function spe(r){let{inputs:e,backend:t}=r,{input:o}=e;return Lx(o,!1,t)}var iW={kernelName:zi,backendName:"webgpu",kernelFunc:spe};var Bx=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` + `}};function Lx(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=pe({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new ym("real",u),l=new ym("imag",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:"float32",data:[d]},{type:"float32",data:[f]}],g=t.runWebGPUProgram(c,m,"float32",h);i.push(g);let x=t.runWebGPUProgram(l,m,"float32",h);i.push(x);let b=xo({inputs:{real:g,imag:x},backend:t});i.push(b);let C=pe({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(S=>t.disposeData(S.dataId)),C}function spe(r){let{inputs:e,backend:t}=r,{input:o}=e;return Lx(o,!1,t)}var iW={kernelName:"FFT",backendName:"webgpu",kernelFunc:spe};var Bx=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7533,7 +7533,7 @@ return a / b;`,Yee=` setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step); } } - `}};function Spe(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=(n-o)/(s-1),i=new Gx(s),p=[{type:"float32",data:[o]},{type:"float32",data:[a]}];return e.runWebGPUProgram(i,[],"float32",p)}var NW={kernelName:An,backendName:"webgpu",kernelFunc:Spe};var Ipe=ye({opType:Z.LOG,cpuKernelImpl:Tz}),TW={kernelName:Fn,backendName:"webgpu",kernelFunc:Ipe};var vpe=ye({opType:Z.LOG1P}),_W={kernelName:Pn,backendName:"webgpu",kernelFunc:vpe};var kpe=et({opType:fe.LOGICAL_AND,dtype:"bool"}),EW={kernelName:On,backendName:"webgpu",kernelFunc:kpe};var Npe=ye({opType:Z.LOGICAL_NOT}),$W={kernelName:Mn,backendName:"webgpu",kernelFunc:Npe};var Tpe=et({opType:fe.LOGICAL_OR}),RW={kernelName:Ln,backendName:"webgpu",kernelFunc:Tpe};var DW=` + `}};function Spe(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=(n-o)/(s-1),i=new Gx(s),p=[{type:"float32",data:[o]},{type:"float32",data:[a]}];return e.runWebGPUProgram(i,[],"float32",p)}var NW={kernelName:An,backendName:"webgpu",kernelFunc:Spe};var Ipe=ye({opType:Z.LOG,cpuKernelImpl:Tz}),TW={kernelName:"Log",backendName:"webgpu",kernelFunc:Ipe};var vpe=ye({opType:Z.LOG1P}),_W={kernelName:Pn,backendName:"webgpu",kernelFunc:vpe};var kpe=et({opType:fe.LOGICAL_AND,dtype:"bool"}),EW={kernelName:On,backendName:"webgpu",kernelFunc:kpe};var Npe=ye({opType:Z.LOGICAL_NOT}),$W={kernelName:Mn,backendName:"webgpu",kernelFunc:Npe};var Tpe=et({opType:fe.LOGICAL_OR}),RW={kernelName:Ln,backendName:"webgpu",kernelFunc:Tpe};var DW=` var powValue = 0.0; let basis = uniforms.bias + uniforms.alpha * sum; if (uniforms.beta == 0.5) { @@ -7598,7 +7598,7 @@ return a / b;`,Yee=` setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue); } - } `}};function _pe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new Hx(n.shape):u=new Kx(n.shape,s);let c=[{type:"int32",data:[s]},{type:"float32",data:[a]},{type:"float32",data:[i]},{type:"float32",data:[p]}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var AW={kernelName:Bn,backendName:"webgpu",kernelFunc:_pe};var qx=class{constructor(e){this.outputShape=[],this.variableNames=["inputImage","outputImage","dy"],this.uniforms="depthRadius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn_grad"}getUserCode(){return` + } `}};function _pe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new Hx(n.shape):u=new Kx(n.shape,s);let c=[{type:"int32",data:[s]},{type:"float32",data:[a]},{type:"float32",data:[i]},{type:"float32",data:[p]}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var AW={kernelName:"LRN",backendName:"webgpu",kernelFunc:_pe};var qx=class{constructor(e){this.outputShape=[],this.variableNames=["inputImage","outputImage","dy"],this.uniforms="depthRadius : i32, bias : f32, alpha : f32, beta : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="lrn_grad"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); @@ -7751,7 +7751,7 @@ return a / b;`,Yee=` setOutputAtIndex(index, dotProd); } } - `}};function Ape(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Iu(m,"max",!0),f=[{type:"int32",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:"int32",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inDepth,m.inHeight,m.inWidth]},{type:"int32",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[a],"int32",f),g=new Xx(m);f=[{type:"int32",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:"int32",data:[m.effectiveFilterDepth-1-m.padInfo.front,m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:"int32",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:"int32",data:[m.outDepth]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],a.dtype,f);return t.disposeData(h.dataId),x}var LW={kernelName:Gi,backendName:"webgpu",kernelFunc:Ape};function Fpe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;fm([s,a],"maxPoolGrad");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=new Ba(m,"max",!0),f=[{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]},{type:"int32",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[i],"int32",f),g=new jx(m);f=[{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],i.dtype,f);return t.disposeData(h.dataId),x}var BW={kernelName:Ui,backendName:"webgpu",kernelFunc:Fpe};function Ppe(r){let{inputs:e,backend:t,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=e;y.assert(p.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(p.shape,n,s,u,a),l=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]}],m=new Ba(c,"max",!1),d=t.runWebGPUProgram(m,[p],p.dtype,l);m=new Ba(c,"max",!0,!0,i);let f=t.runWebGPUProgram(m,[p],"int32",l);return[d,f]}var zW={kernelName:ua,backendName:"webgpu",kernelFunc:Ppe};function Ope(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,"min",t)}var VW={kernelName:Gn,backendName:"webgpu",kernelFunc:Ope};var Mpe=et({opType:fe.MIN,cpuKernelImpl:$z}),WW={kernelName:Hn,backendName:"webgpu",kernelFunc:Mpe};var Yx=class{constructor(e,t,o){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o==="reflect"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(","),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:""}`).join(","),n=e===1?"start":"start[i]",s=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=ft(e),p=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` + `}};function Ape(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Iu(m,"max",!0),f=[{type:"int32",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:"int32",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inDepth,m.inHeight,m.inWidth]},{type:"int32",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[a],"int32",f),g=new Xx(m);f=[{type:"int32",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:"int32",data:[m.effectiveFilterDepth-1-m.padInfo.front,m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:"int32",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:"int32",data:[m.outDepth]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],a.dtype,f);return t.disposeData(h.dataId),x}var LW={kernelName:Gi,backendName:"webgpu",kernelFunc:Ape};function Fpe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;fm([s,a],"maxPoolGrad");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=new Ba(m,"max",!0),f=[{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]},{type:"int32",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[i],"int32",f),g=new jx(m);f=[{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:"int32",data:[m.outHeight]},{type:"int32",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],i.dtype,f);return t.disposeData(h.dataId),x}var BW={kernelName:Ui,backendName:"webgpu",kernelFunc:Fpe};function Ppe(r){let{inputs:e,backend:t,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=e;y.assert(p.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(p.shape,n,s,u,a),l=[{type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]}],m=new Ba(c,"max",!1),d=t.runWebGPUProgram(m,[p],p.dtype,l);m=new Ba(c,"max",!0,!0,i);let f=t.runWebGPUProgram(m,[p],"int32",l);return[d,f]}var zW={kernelName:ua,backendName:"webgpu",kernelFunc:Ppe};function Ope(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,"min",t)}var VW={kernelName:"Min",backendName:"webgpu",kernelFunc:Ope};var Mpe=et({opType:fe.MIN,cpuKernelImpl:$z}),WW={kernelName:Hn,backendName:"webgpu",kernelFunc:Mpe};var Yx=class{constructor(e,t,o){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o==="reflect"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(","),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:""}`).join(","),n=e===1?"start":"start[i]",s=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=ft(e),p=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` ${G("index")} { if (index < uniforms.size) { let start = ${i}(${t}); @@ -7768,7 +7768,7 @@ return a / b;`,Yee=` setOutputAtIndex(index, getX(${p})); } } - `}};var UW={kernelName:Kn,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:"int32",data:[c[0],c[1]]})),p=new Yx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var Lpe=et({opType:fe.MOD}),GW={kernelName:qn,backendName:"webgpu",kernelFunc:Lpe};var Qx=class{constructor(e,t){this.variableNames=["probs"],this.outputShape=[],this.uniforms="seed : f32, numOutcomes: i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="multinomial"}getUserCode(){return` + `}};var UW={kernelName:Kn,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:"int32",data:[c[0],c[1]]})),p=new Yx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var Lpe=et({opType:fe.MOD}),GW={kernelName:"Mod",backendName:"webgpu",kernelFunc:Lpe};var Qx=class{constructor(e,t){this.variableNames=["probs"],this.outputShape=[],this.uniforms="seed : f32, numOutcomes: i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="multinomial"}getUserCode(){return` //Based on the work of Dave Hoskins //https://www.shadertoy.com/view/4djSRW fn random (seed : f32, resultUV : vec2) -> f32 { @@ -7860,7 +7860,7 @@ return a / b;`,Yee=` setOutputAtCoords(row, col, value); } } - `}};function l0(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=pe({inputs:{x:n},backend:t,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new Zx(a.shape),p=t.runWebGPUProgram(i,[a],n.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n.shape}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var HW={kernelName:Is,backendName:"webgpu",kernelFunc:l0};function Bpe(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:l0({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new Qx(u,s),m=[{type:"float32",data:[a]},{type:"int32",data:[c]}],d=t.runWebGPUProgram(l,[p],"int32",m);return i||t.disposeData(p.dataId),d}var KW={kernelName:jn,backendName:"webgpu",kernelFunc:Bpe};function zpe(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=Dz(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Jr(o.shape,Z.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var qW={kernelName:pa,backendName:"webgpu",kernelFunc:zpe};function Vpe(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Vt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],"int32",new Int32Array(l))}var jW={kernelName:Qn,backendName:"webgpu",kernelFunc:Vpe};function Wpe(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Vt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var XW={kernelName:Zn,backendName:"webgpu",kernelFunc:Wpe};var Jx=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` + `}};function l0(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=pe({inputs:{x:n},backend:t,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new Zx(a.shape),p=t.runWebGPUProgram(i,[a],n.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n.shape}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var HW={kernelName:Is,backendName:"webgpu",kernelFunc:l0};function Bpe(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:l0({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new Qx(u,s),m=[{type:"float32",data:[a]},{type:"int32",data:[c]}],d=t.runWebGPUProgram(l,[p],"int32",m);return i||t.disposeData(p.dataId),d}var KW={kernelName:jn,backendName:"webgpu",kernelFunc:Bpe};function zpe(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=Dz(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Jr(o.shape,Z.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var qW={kernelName:"Neg",backendName:"webgpu",kernelFunc:zpe};function Vpe(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Vt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],"int32",new Int32Array(l))}var jW={kernelName:Qn,backendName:"webgpu",kernelFunc:Vpe};function Wpe(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Vt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var XW={kernelName:Zn,backendName:"webgpu",kernelFunc:Wpe};var Jx=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` ${G("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -7884,7 +7884,7 @@ return a / b;`,Yee=` ${m0(this.xShape)} } } - `}};var Hpe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return At({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return vt({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:"float32",data:[a]}];s.map(u=>i.push({type:"int32",data:[u[0],u[1]]}));let p=new ey(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},tU={kernelName:es,backendName:"webgpu",kernelFunc:Hpe};var Kpe=et({opType:fe.POW}),rU={kernelName:ts,backendName:"webgpu",kernelFunc:Kpe};function qpe(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Ii(fe.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],"float32")}var oU={kernelName:rs,backendName:"webgpu",kernelFunc:qpe};function jpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,"prod",t)}var nU={kernelName:os,backendName:"webgpu",kernelFunc:jpe};var Xpe=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=Pz(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},sU={kernelName:ma,backendName:"webgpu",kernelFunc:Xpe};var Ype=et({opType:fe.DIV}),aU={kernelName:fn,backendName:"webgpu",kernelFunc:Ype};var Qpe=ye({opType:Z.RECIPROCAL}),iU={kernelName:ns,backendName:"webgpu",kernelFunc:Qpe};var Zpe=ye({opType:Z.RELU}),uU={kernelName:ss,backendName:"webgpu",kernelFunc:Zpe};var Jpe=ye({opType:Z.RELU6}),pU={kernelName:us,backendName:"webgpu",kernelFunc:Jpe};var ty=class{constructor(e,t,o){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` + `}};var Hpe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return At({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return vt({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:"float32",data:[a]}];s.map(u=>i.push({type:"int32",data:[u[0],u[1]]}));let p=new ey(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},tU={kernelName:es,backendName:"webgpu",kernelFunc:Hpe};var Kpe=et({opType:fe.POW}),rU={kernelName:"Pow",backendName:"webgpu",kernelFunc:Kpe};function qpe(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Ii(fe.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],"float32")}var oU={kernelName:rs,backendName:"webgpu",kernelFunc:qpe};function jpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,"prod",t)}var nU={kernelName:os,backendName:"webgpu",kernelFunc:jpe};var Xpe=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=Pz(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},sU={kernelName:ma,backendName:"webgpu",kernelFunc:Xpe};var Ype=et({opType:fe.DIV}),aU={kernelName:fn,backendName:"webgpu",kernelFunc:Ype};var Qpe=ye({opType:Z.RECIPROCAL}),iU={kernelName:ns,backendName:"webgpu",kernelFunc:Qpe};var Zpe=ye({opType:Z.RELU}),uU={kernelName:ss,backendName:"webgpu",kernelFunc:Zpe};var Jpe=ye({opType:Z.RELU6}),pU={kernelName:us,backendName:"webgpu",kernelFunc:Jpe};var ty=class{constructor(e,t,o){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); @@ -8209,7 +8209,7 @@ return a / b;`,Yee=` } } } - `}};function pce(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new uy(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var CU={kernelName:fa,backendName:"webgpu",kernelFunc:pce};var cce=ye({opType:Z.SELU}),wU={kernelName:hs,backendName:"webgpu",kernelFunc:cce};var lce=ye({opType:Z.SIGMOID}),SU={kernelName:bs,backendName:"webgpu",kernelFunc:lce};var mce=ye({opType:Z.SIGN}),IU={kernelName:ys,backendName:"webgpu",kernelFunc:mce};var dce=ye({opType:Z.SIN}),vU={kernelName:gs,backendName:"webgpu",kernelFunc:dce};var fce=ye({opType:Z.SINH}),kU={kernelName:xs,backendName:"webgpu",kernelFunc:fce};var hce=ye({opType:Z.SOFTPLUS}),NU={kernelName:Cs,backendName:"webgpu",kernelFunc:hce};var py=class{constructor(e,t,o,n,s,a){this.variableNames=["x"],this.outputShape=[],this.uniforms="",this.workgroupSize=[64,1,1],this.size=!0;let i=new Array(n.length);for(let p=0;p{this.uniforms+=` pad${u} : vec2,`}),this.shaderKey=`spaceToBatchND_${s}`}getUserCode(){let e=ft(this.outputShape.length),t=e0(this.newDim);return` + `}};function pce(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new uy(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var CU={kernelName:fa,backendName:"webgpu",kernelFunc:pce};var cce=ye({opType:Z.SELU}),wU={kernelName:hs,backendName:"webgpu",kernelFunc:cce};var lce=ye({opType:Z.SIGMOID}),SU={kernelName:bs,backendName:"webgpu",kernelFunc:lce};var mce=ye({opType:Z.SIGN}),IU={kernelName:ys,backendName:"webgpu",kernelFunc:mce};var dce=ye({opType:Z.SIN}),vU={kernelName:"Sin",backendName:"webgpu",kernelFunc:dce};var fce=ye({opType:Z.SINH}),kU={kernelName:xs,backendName:"webgpu",kernelFunc:fce};var hce=ye({opType:Z.SOFTPLUS}),NU={kernelName:Cs,backendName:"webgpu",kernelFunc:hce};var py=class{constructor(e,t,o,n,s,a){this.variableNames=["x"],this.outputShape=[],this.uniforms="",this.workgroupSize=[64,1,1],this.size=!0;let i=new Array(n.length);for(let p=0;p{this.uniforms+=` pad${u} : vec2,`}),this.shaderKey=`spaceToBatchND_${s}`}getUserCode(){let e=ft(this.outputShape.length),t=e0(this.newDim);return` ${cm(this.paddedXShape,"PaddedX")} ${G("index")} { if(index < uniforms.size) { @@ -8263,7 +8263,7 @@ return a / b;`,Yee=` setOutputAtIndex(index, getX(${t})); } } - `}};function kce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=pt.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=pe({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let 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kce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=pt.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=pe({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=pt.computeOutShape(b,C,S),$=Hs({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=pe({inputs:{x:$},backend:t,attrs:{shape:f}}),t.disposeData($.dataId)}else if(t.shouldExecuteOnCPU([n])){let $=t.readSync(n.dataId),R=me(n.shape,n.dtype,$),D=zz(d,R,S,b);k=t.makeTensorInfo(f,n.dtype,D.values)}else{let $=new hy(d),R=[{type:"int32",data:b},{type:"int32",data:S}],D=t.runWebGPUProgram($,[n],n.dtype,R);k=pe({inputs:{x:D},backend:t,attrs:{shape:f}}),t.disposeData(D.dataId)}return k}var MU={kernelName:Ns,backendName:"webgpu",kernelFunc:kce};function Nce(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=Vz(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],"string",f),t.makeTensorInfo(l.shape,"int32",h)]}var LU={kernelName:Ca,backendName:"webgpu",kernelFunc:Nce};var Tce=et({opType:fe.SUB,cpuKernelImpl:Wz,supportsComplex:!0}),BU={kernelName:"Sub",backendName:"webgpu",kernelFunc:Tce};var _ce=ye({opType:Z.TAN}),zU={kernelName:"Tan",backendName:"webgpu",kernelFunc:_ce};var Ece=ye({opType:Z.TANH}),VU={kernelName:Es,backendName:"webgpu",kernelFunc:Ece};function $ce(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return t.makeTensorInfo(n.shape,s.dtype);let d=[],f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,i]}});d.push(f);let h=pe({inputs:{x:a},backend:t,attrs:{shape:[p,u]}});d.push(h);let g=pe({inputs:{x:n},backend:t,attrs:{shape:m}});d.push(g);let x=Cm({inputs:{x:g},backend:t,attrs:{reps:Array(m.length).fill(1)}}),b=new za([p,u],i,f.shape.length,h.shape.length,c,m,n.dtype,!1),C=y.sizeFromShape([p,u]),S=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[C]}],k=t.runWebGPUProgram(b,[h,f],g.dtype,S,x);d.push(k);let _=pe({inputs:{x:k},backend:t,attrs:{shape:n.shape}});return d.forEach($=>t.disposeData($.dataId)),_}var WU={kernelName:ds,backendName:"webgpu",kernelFunc:$ce};var gy=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return` ${G("index")} { if (index < uniforms.size) { diff --git a/src/face/face.ts b/src/face/face.ts index 00c3aec4..1acfedb0 100644 --- a/src/face/face.ts +++ b/src/face/face.ts @@ -113,7 +113,7 @@ export const detectFace = async (instance: Human /* instance of human */, input: } instance.analyze('End GEAR:'); - // run gear, inherits face from blazeface + // run ssrnet, inherits face from blazeface instance.analyze('Start SSRNet:'); if (instance.config.async) { ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null; diff --git a/src/image/imagefx.ts b/src/image/imagefx.ts index e4430330..8c8ac8f2 100644 --- a/src/image/imagefx.ts +++ b/src/image/imagefx.ts @@ -27,7 +27,7 @@ class GLProgram { this.gl = gl; const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER); const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER); - this.id = this.gl.createProgram(); + this.id = this.gl.createProgram() as WebGLProgram; if (!vertexShader || !fragmentShader) return; if (!this.id) { log('filter: could not create webgl program'); diff --git a/test/build.log b/test/build.log index b7032068..c3e5effd 100644 --- a/test/build.log +++ b/test/build.log @@ -1,51 +1,51 @@ -2025-02-05 09:39:04 DATA:  Build {"name":"@vladmandic/human","version":"3.3.5"} -2025-02-05 09:39:04 INFO:  Application: {"name":"@vladmandic/human","version":"3.3.5"} -2025-02-05 09:39:04 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} -2025-02-05 09:39:04 INFO:  Toolchain: {"build":"0.10.2","esbuild":"0.24.2","typescript":"5.7.3","typedoc":"0.27.6","eslint":"8.57.0"} -2025-02-05 09:39:04 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} -2025-02-05 09:39:04 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} -2025-02-05 09:39:04 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":358} -2025-02-05 09:39:04 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":566,"outputBytes":957} -2025-02-05 09:39:04 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678664,"outputBytes":321804} -2025-02-05 09:39:04 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":574,"outputBytes":965} -2025-02-05 09:39:04 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":678672,"outputBytes":321808} -2025-02-05 09:39:04 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":662,"outputBytes":2003} -2025-02-05 09:39:04 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":679710,"outputBytes":321919} -2025-02-05 09:39:04 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1403,"outputBytes":690} -2025-02-05 09:39:04 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":678397,"outputBytes":320365} -2025-02-05 09:39:04 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1403,"outputBytes":1267320} -2025-02-05 09:39:04 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1945027,"outputBytes":1583413} -2025-02-05 09:39:04 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1945027,"outputBytes":2067530} -2025-02-05 09:39:06 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":78} -2025-02-05 09:39:08 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":81,"generated":true} -2025-02-05 09:39:08 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":2970} -2025-02-05 09:39:08 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17498,"outputBytes":9397} -2025-02-05 09:39:08 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":22791} -2025-02-05 09:39:16 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":171,"errors":0,"warnings":0} -2025-02-05 09:39:16 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} -2025-02-05 09:39:16 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} -2025-02-05 09:39:16 INFO:  Done... -2025-02-05 09:39:16 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} -2025-02-05 09:39:16 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} -2025-02-05 09:39:16 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} -2025-02-05 09:39:16 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} -2025-02-05 09:39:16 STATE: Filter: {"input":"types/tfjs-core.d.ts"} -2025-02-05 09:39:17 STATE: API-Extractor: {"succeeeded":true,"errors":0,"warnings":0} -2025-02-05 09:39:17 STATE: Filter: {"input":"types/human.d.ts"} -2025-02-05 09:39:17 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} -2025-02-05 09:39:17 STATE: Write: {"output":"dist/human.esm.d.ts"} -2025-02-05 09:39:17 STATE: Write: {"output":"dist/human.d.ts"} -2025-02-05 09:39:17 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} -2025-02-05 09:39:17 STATE: Write: {"output":"dist/human.node.d.ts"} -2025-02-05 09:39:17 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} -2025-02-05 09:39:17 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} -2025-02-05 09:39:17 STATE: Models {"folder":"./models","models":12} -2025-02-05 09:39:17 STATE: Models {"folder":"../human-models/models","models":44} -2025-02-05 09:39:17 STATE: Models {"folder":"../blazepose/model/","models":4} -2025-02-05 09:39:17 STATE: Models {"folder":"../anti-spoofing/model","models":1} -2025-02-05 09:39:17 STATE: Models {"folder":"../efficientpose/models","models":3} -2025-02-05 09:39:17 STATE: Models {"folder":"../insightface/models","models":5} -2025-02-05 09:39:17 STATE: Models {"folder":"../movenet/models","models":3} -2025-02-05 09:39:17 STATE: Models {"folder":"../nanodet/models","models":4} -2025-02-05 09:39:17 STATE: Models: {"count":58,"totalSize":380063249} -2025-02-05 09:39:17 INFO:  Human Build complete... {"logFile":"test/build.log"} +2025-08-26 11:39:08 DATA:  Build {"name":"@vladmandic/human","version":"3.3.6"} +2025-08-26 11:39:08 INFO:  Application: {"name":"@vladmandic/human","version":"3.3.6"} +2025-08-26 11:39:08 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} +2025-08-26 11:39:08 INFO:  Toolchain: {"build":"0.10.3","esbuild":"0.25.9","typescript":"5.9.2","typedoc":"0.28.11","eslint":"8.57.0"} +2025-08-26 11:39:08 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} +2025-08-26 11:39:08 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} +2025-08-26 11:39:08 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":358} +2025-08-26 11:39:08 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":566,"outputBytes":957} +2025-08-26 11:39:09 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678685,"outputBytes":321804} +2025-08-26 11:39:09 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":574,"outputBytes":965} +2025-08-26 11:39:09 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":678693,"outputBytes":321808} +2025-08-26 11:39:09 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":662,"outputBytes":2003} +2025-08-26 11:39:09 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":679731,"outputBytes":321919} +2025-08-26 11:39:09 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1403,"outputBytes":690} +2025-08-26 11:39:09 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":678418,"outputBytes":320365} +2025-08-26 11:39:09 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1403,"outputBytes":1267682} +2025-08-26 11:39:09 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1945410,"outputBytes":1583774} +2025-08-26 11:39:09 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1945410,"outputBytes":2067875} +2025-08-26 11:39:10 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":78} +2025-08-26 11:39:12 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":81,"generated":true} +2025-08-26 11:39:12 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":2970} +2025-08-26 11:39:12 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17498,"outputBytes":9397} +2025-08-26 11:39:12 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":22797} +2025-08-26 11:39:20 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":172,"errors":0,"warnings":0} +2025-08-26 11:39:20 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} +2025-08-26 11:39:20 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} +2025-08-26 11:39:20 INFO:  Done... +2025-08-26 11:39:20 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} +2025-08-26 11:39:20 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} +2025-08-26 11:39:20 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} +2025-08-26 11:39:20 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} +2025-08-26 11:39:20 STATE: Filter: {"input":"types/tfjs-core.d.ts"} +2025-08-26 11:39:21 STATE: API-Extractor: {"succeeeded":true,"errors":0,"warnings":0} +2025-08-26 11:39:21 STATE: Filter: {"input":"types/human.d.ts"} +2025-08-26 11:39:21 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} +2025-08-26 11:39:21 STATE: Write: {"output":"dist/human.esm.d.ts"} +2025-08-26 11:39:21 STATE: Write: {"output":"dist/human.d.ts"} +2025-08-26 11:39:21 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} +2025-08-26 11:39:21 STATE: Write: {"output":"dist/human.node.d.ts"} +2025-08-26 11:39:21 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} +2025-08-26 11:39:21 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} +2025-08-26 11:39:21 STATE: Models {"folder":"./models","models":12} +2025-08-26 11:39:21 STATE: Models {"folder":"../human-models/models","models":44} +2025-08-26 11:39:21 STATE: Models {"folder":"../blazepose/model/","models":4} +2025-08-26 11:39:21 STATE: Models {"folder":"../anti-spoofing/model","models":1} +2025-08-26 11:39:21 STATE: Models {"folder":"../efficientpose/models","models":3} +2025-08-26 11:39:21 STATE: Models {"folder":"../insightface/models","models":5} +2025-08-26 11:39:21 STATE: Models {"folder":"../movenet/models","models":3} +2025-08-26 11:39:21 STATE: Models {"folder":"../nanodet/models","models":4} +2025-08-26 11:39:22 STATE: Models: {"count":58,"totalSize":380063249} +2025-08-26 11:39:22 INFO:  Human Build complete... {"logFile":"test/build.log"} diff --git a/test/test.log b/test/test.log index 810ab335..13277367 100644 --- a/test/test.log +++ b/test/test.log @@ -1,905 +1,277 @@ -2025-02-05 09:39:19 INFO:  @vladmandic/human version 3.3.5 -2025-02-05 09:39:19 INFO:  User: vlado Platform: linux Arch: x64 Node: v22.13.1 -2025-02-05 09:39:19 INFO:  demos: [{"cmd":"../demo/nodejs/node.js","args":[]},{"cmd":"../demo/nodejs/node-simple.js","args":[]},{"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]},{"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]},{"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]},{"cmd":"../demo/nodejs/process-folder.js","args":["samples"]},{"cmd":"../demo/multithread/node-multiprocess.js","args":[]},{"cmd":"../demo/facematch/node-match.js","args":[]},{"cmd":"../demo/nodejs/node-bench.js","args":[]},{"cmd":"../test/test-node-emotion.js","args":[]}] -2025-02-05 09:39:19 INFO:  {"cmd":"../demo/nodejs/node.js","args":[]} start -2025-02-05 09:39:21 INFO:  {"cmd":"../demo/nodejs/node-simple.js","args":[]} start -2025-02-05 09:39:21 INFO:  {"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]} start -2025-02-05 09:39:22 INFO:  {"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]} start -2025-02-05 09:39:23 INFO:  {"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]} start -2025-02-05 09:39:24 INFO:  {"cmd":"../demo/nodejs/process-folder.js","args":["samples"]} start -2025-02-05 09:39:26 INFO:  {"cmd":"../demo/multithread/node-multiprocess.js","args":[]} start -2025-02-05 09:39:40 INFO:  {"cmd":"../demo/facematch/node-match.js","args":[]} start -2025-02-05 09:39:41 INFO:  {"cmd":"../demo/nodejs/node-bench.js","args":[]} start -2025-02-05 09:39:52 INFO:  {"cmd":"../test/test-node-emotion.js","args":[]} start -2025-02-05 09:39:58 INFO:  tests: ["test-node-load.js","test-node-gear.js","test-backend-node.js","test-backend-node-gpu.js","test-backend-node-wasm.js"] -2025-02-05 09:39:58 INFO:  -2025-02-05 09:39:58 INFO:  test-node-load.js start -2025-02-05 09:39:58 INFO:  test-node-load.js load start {"human":"3.3.5","tf":"4.22.0","progress":0} -2025-02-05 09:39:58 DATA:  test-node-load.js load interval {"elapsed":0,"progress":0} -2025-02-05 09:39:58 DATA:  test-node-load.js load interval {"elapsed":11,"progress":0} -2025-02-05 09:39:58 DATA:  test-node-load.js load interval {"elapsed":26,"progress":0.12865823126207815} -2025-02-05 09:39:58 DATA:  test-node-load.js load interval {"elapsed":36,"progress":0.2204080722078057} -2025-02-05 09:39:58 DATA:  test-node-load.js load interval {"elapsed":59,"progress":0.4726970548784964} -2025-02-05 09:39:58 DATA:  test-node-load.js load interval {"elapsed":71,"progress":0.68355493315821} -2025-02-05 09:39:58 STATE: test-node-load.js passed {"progress":1} -2025-02-05 09:39:58 INFO:  test-node-load.js load final {"progress":1} -2025-02-05 09:39:59 DATA:  test-node-load.js load interval {"elapsed":461,"progress":1} -2025-02-05 09:39:59 INFO:  -2025-02-05 09:39:59 INFO:  test-node-gear.js start -2025-02-05 09:39:59 DATA:  test-node-gear.js input: ["samples/in/ai-face.jpg"] -2025-02-05 09:40:00 STATE: test-node-gear.js passed: gear faceres samples/in/ai-face.jpg -2025-02-05 09:40:00 DATA:  test-node-gear.js results {"face":0,"model":"faceres","image":"samples/in/ai-face.jpg","age":23.6,"gender":"female","genderScore":0.95,"emotion":[]} -2025-02-05 09:40:00 STATE: test-node-gear.js passed: gear gear samples/in/ai-face.jpg -2025-02-05 09:40:00 DATA:  test-node-gear.js results {"face":0,"model":"gear","image":"samples/in/ai-face.jpg","age":25.2,"gender":"female","genderScore":0.7,"race":[{"score":0.96,"race":"white"}],"emotion":[]} -2025-02-05 09:40:00 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg -2025-02-05 09:40:00 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":22.6,"gender":"female","genderScore":0.98,"emotion":[]} -2025-02-05 09:40:01 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg -2025-02-05 09:40:01 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":22.6,"gender":"male","emotion":[]} -2025-02-05 09:40:01 INFO:  -2025-02-05 09:40:01 INFO:  test-backend-node.js start -2025-02-05 09:40:01 INFO:  test-backend-node.js test: configuration validation -2025-02-05 09:40:01 STATE: test-backend-node.js passed: configuration default validation [] -2025-02-05 09:40:01 STATE: test-backend-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] -2025-02-05 09:40:01 INFO:  test-backend-node.js test: model load -2025-02-05 09:40:01 STATE: test-backend-node.js passed: models loaded 11 11 [{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] -2025-02-05 09:40:01 INFO:  test-backend-node.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} -2025-02-05 09:40:01 INFO:  test-backend-node.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} -2025-02-05 09:40:01 INFO:  test-backend-node.js test: warmup -2025-02-05 09:40:01 STATE: test-backend-node.js passed: create human -2025-02-05 09:40:01 INFO:  test-backend-node.js human version: 3.3.5 -2025-02-05 09:40:01 INFO:  test-backend-node.js platform: agent: -2025-02-05 09:40:01 INFO:  test-backend-node.js tfjs version: 4.22.0 -2025-02-05 09:40:01 INFO:  test-backend-node.js env: {"browser":false,"node":true,"platform":"","agent":"","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.22.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":172} -2025-02-05 09:40:01 STATE: test-backend-node.js passed: set backend: tensorflow -2025-02-05 09:40:01 STATE: test-backend-node.js tensors 1785 -2025-02-05 09:40:01 STATE: test-backend-node.js result: defined models: 11 loaded models: 11 -2025-02-05 09:40:01 STATE: test-backend-node.js passed: load models 11 -2025-02-05 09:40:01 STATE: test-backend-node.js passed: warmup: none default -2025-02-05 09:40:01 DATA:  test-backend-node.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} -2025-02-05 09:40:01 DATA:  test-backend-node.js result: performance: load: null total: null -2025-02-05 09:40:01 STATE: test-backend-node.js passed: warmup none result match -2025-02-05 09:40:01 STATE: test-backend-node.js event: image -2025-02-05 09:40:01 STATE: test-backend-node.js event: detect -2025-02-05 09:40:01 STATE: test-backend-node.js event: warmup -2025-02-05 09:40:01 STATE: test-backend-node.js passed: warmup: face default -2025-02-05 09:40:01 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} -2025-02-05 09:40:01 DATA:  test-backend-node.js result: performance: load: null total: 460 -2025-02-05 09:40:01 STATE: test-backend-node.js passed: warmup face result match -2025-02-05 09:40:01 STATE: test-backend-node.js event: image -2025-02-05 09:40:02 STATE: test-backend-node.js event: detect -2025-02-05 09:40:02 STATE: test-backend-node.js event: warmup -2025-02-05 09:40:02 STATE: test-backend-node.js passed: warmup: body default -2025-02-05 09:40:02 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:02 DATA:  test-backend-node.js result: performance: load: null total: 348 -2025-02-05 09:40:02 STATE: test-backend-node.js passed: warmup body result match -2025-02-05 09:40:02 STATE: test-backend-node.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.6,"gender":"female","genderScore":0.97},"emotion":[{"score":0.47,"emotion":"angry"},{"score":0.32,"emotion":"fear"},{"score":0.1,"emotion":"neutral"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 16% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} -2025-02-05 09:40:02 INFO:  test-backend-node.js test: details verification -2025-02-05 09:40:02 STATE: test-backend-node.js start default -2025-02-05 09:40:02 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:02 STATE: test-backend-node.js event: image -2025-02-05 09:40:02 STATE: test-backend-node.js event: detect -2025-02-05 09:40:02 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg default -2025-02-05 09:40:02 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:02 DATA:  test-backend-node.js result: performance: load: null total: 340 -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details face length 1 -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details face score 1 0.93 1 -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details face age/gender 23.6 female 0.97 2.34 -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details face arrays 4 478 1024 -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details face emotion 3 {"score":0.48,"emotion":"angry"} -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details face anti-spoofing 0.8 -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details face liveness 0.93 -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details body length 1 -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details body 0.92 17 6 -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details hand length 1 -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details hand 0.51 0.73 point -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details hand arrays 21 5 7 -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details gesture length 7 -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details gesture first {"face":0,"gesture":"facing right"} -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details object length 1 -2025-02-05 09:40:02 STATE: test-backend-node.js passed: details object 0.72 person -2025-02-05 09:40:03 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} -2025-02-05 09:40:03 STATE: test-backend-node.js event: image -2025-02-05 09:40:03 STATE: test-backend-node.js event: detect -2025-02-05 09:40:03 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 -2025-02-05 09:40:03 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} -2025-02-05 09:40:03 STATE: test-backend-node.js event: image -2025-02-05 09:40:04 STATE: test-backend-node.js event: detect -2025-02-05 09:40:04 STATE: test-backend-node.js passed: tensor shape: [1200,1200,4] dtype: float32 -2025-02-05 09:40:04 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:04 STATE: test-backend-node.js event: image -2025-02-05 09:40:04 STATE: test-backend-node.js event: detect -2025-02-05 09:40:04 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 -2025-02-05 09:40:04 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:04 STATE: test-backend-node.js event: image -2025-02-05 09:40:05 STATE: test-backend-node.js event: detect -2025-02-05 09:40:05 STATE: test-backend-node.js passed: tensor shape: [1200,1200,3] dtype: float32 -2025-02-05 09:40:05 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} -2025-02-05 09:40:05 STATE: test-backend-node.js event: image -2025-02-05 09:40:05 STATE: test-backend-node.js event: detect -2025-02-05 09:40:05 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 -2025-02-05 09:40:05 INFO:  test-backend-node.js test default -2025-02-05 09:40:05 STATE: test-backend-node.js start async -2025-02-05 09:40:06 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:06 STATE: test-backend-node.js event: image -2025-02-05 09:40:06 STATE: test-backend-node.js event: detect -2025-02-05 09:40:06 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg async -2025-02-05 09:40:06 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:06 DATA:  test-backend-node.js result: performance: load: null total: 346 -2025-02-05 09:40:06 STATE: test-backend-node.js passed: default result face match 1 female 0.97 -2025-02-05 09:40:06 INFO:  test-backend-node.js test sync -2025-02-05 09:40:06 STATE: test-backend-node.js start sync -2025-02-05 09:40:06 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:06 STATE: test-backend-node.js event: image -2025-02-05 09:40:07 STATE: test-backend-node.js event: detect -2025-02-05 09:40:07 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sync -2025-02-05 09:40:07 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:07 DATA:  test-backend-node.js result: performance: load: null total: 343 -2025-02-05 09:40:07 STATE: test-backend-node.js passed: default sync 1 female 0.97 -2025-02-05 09:40:07 INFO:  test-backend-node.js test: image process -2025-02-05 09:40:07 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:07 STATE: test-backend-node.js passed: image input null [1,256,256,3] -2025-02-05 09:40:07 INFO:  test-backend-node.js test: image null -2025-02-05 09:40:07 STATE: test-backend-node.js passed: invalid input could not convert input to tensor -2025-02-05 09:40:07 INFO:  test-backend-node.js test face similarity -2025-02-05 09:40:07 STATE: test-backend-node.js start face similarity -2025-02-05 09:40:07 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:07 STATE: test-backend-node.js event: image -2025-02-05 09:40:07 STATE: test-backend-node.js event: detect -2025-02-05 09:40:07 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face similarity -2025-02-05 09:40:07 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2025-02-05 09:40:07 DATA:  test-backend-node.js result: performance: load: null total: 340 -2025-02-05 09:40:07 STATE: test-backend-node.js start face similarity -2025-02-05 09:40:07 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:07 STATE: test-backend-node.js event: image -2025-02-05 09:40:08 STATE: test-backend-node.js event: detect -2025-02-05 09:40:08 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg face similarity -2025-02-05 09:40:08 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:08 DATA:  test-backend-node.js result: performance: load: null total: 324 -2025-02-05 09:40:08 STATE: test-backend-node.js start face similarity -2025-02-05 09:40:08 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2025-02-05 09:40:08 STATE: test-backend-node.js event: image -2025-02-05 09:40:08 STATE: test-backend-node.js event: detect -2025-02-05 09:40:08 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg face similarity -2025-02-05 09:40:08 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} -2025-02-05 09:40:08 DATA:  test-backend-node.js result: performance: load: null total: 304 -2025-02-05 09:40:08 STATE: test-backend-node.js passed: face descriptor -2025-02-05 09:40:08 STATE: test-backend-node.js passed: face similarity {"similarity":[1,0.43,0.57],"descriptors":[1024,1024,1024]} -2025-02-05 09:40:08 INFO:  test-backend-node.js test object -2025-02-05 09:40:08 STATE: test-backend-node.js start object -2025-02-05 09:40:08 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:08 STATE: test-backend-node.js event: image -2025-02-05 09:40:09 STATE: test-backend-node.js event: detect -2025-02-05 09:40:09 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object -2025-02-05 09:40:09 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:09 DATA:  test-backend-node.js result: performance: load: null total: 337 -2025-02-05 09:40:09 STATE: test-backend-node.js passed: centernet -2025-02-05 09:40:09 STATE: test-backend-node.js start object -2025-02-05 09:40:10 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:10 STATE: test-backend-node.js event: image -2025-02-05 09:40:10 STATE: test-backend-node.js event: detect -2025-02-05 09:40:10 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object -2025-02-05 09:40:10 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:10 DATA:  test-backend-node.js result: performance: load: null total: 343 -2025-02-05 09:40:10 STATE: test-backend-node.js passed: nanodet -2025-02-05 09:40:10 INFO:  test-backend-node.js test sensitive -2025-02-05 09:40:10 STATE: test-backend-node.js start sensitive -2025-02-05 09:40:11 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:11 STATE: test-backend-node.js event: image -2025-02-05 09:40:11 STATE: test-backend-node.js event: detect -2025-02-05 09:40:11 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sensitive -2025-02-05 09:40:11 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.92,"keypoints":17} -2025-02-05 09:40:11 DATA:  test-backend-node.js result: performance: load: null total: 308 -2025-02-05 09:40:11 STATE: test-backend-node.js passed: sensitive result match -2025-02-05 09:40:11 STATE: test-backend-node.js passed: sensitive face result match -2025-02-05 09:40:11 STATE: test-backend-node.js passed: sensitive face emotion result [{"score":0.48,"emotion":"angry"},{"score":0.31,"emotion":"fear"},{"score":0.11,"emotion":"neutral"}] -2025-02-05 09:40:11 STATE: test-backend-node.js passed: sensitive body result match -2025-02-05 09:40:11 STATE: test-backend-node.js passed: sensitive hand result match -2025-02-05 09:40:11 INFO:  test-backend-node.js test body -2025-02-05 09:40:11 STATE: test-backend-node.js start blazepose -2025-02-05 09:40:13 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:13 STATE: test-backend-node.js event: image -2025-02-05 09:40:13 STATE: test-backend-node.js event: detect -2025-02-05 09:40:13 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg blazepose -2025-02-05 09:40:13 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.99,"keypoints":39} -2025-02-05 09:40:13 DATA:  test-backend-node.js result: performance: load: null total: 419 -2025-02-05 09:40:13 STATE: test-backend-node.js passed: blazepose -2025-02-05 09:40:13 STATE: test-backend-node.js start efficientpose -2025-02-05 09:40:14 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:14 STATE: test-backend-node.js event: image -2025-02-05 09:40:15 STATE: test-backend-node.js event: detect -2025-02-05 09:40:15 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg efficientpose -2025-02-05 09:40:15 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.75,"keypoints":13} -2025-02-05 09:40:15 DATA:  test-backend-node.js result: performance: load: null total: 384 -2025-02-05 09:40:15 STATE: test-backend-node.js passed: efficientpose -2025-02-05 09:40:15 STATE: test-backend-node.js start posenet -2025-02-05 09:40:16 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:16 STATE: test-backend-node.js event: image -2025-02-05 09:40:16 STATE: test-backend-node.js event: detect -2025-02-05 09:40:16 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg posenet -2025-02-05 09:40:16 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.96,"keypoints":16} -2025-02-05 09:40:16 DATA:  test-backend-node.js result: performance: load: null total: 308 -2025-02-05 09:40:16 STATE: test-backend-node.js passed: posenet -2025-02-05 09:40:16 STATE: test-backend-node.js start movenet -2025-02-05 09:40:16 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:16 STATE: test-backend-node.js event: image -2025-02-05 09:40:16 STATE: test-backend-node.js event: detect -2025-02-05 09:40:16 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg movenet -2025-02-05 09:40:16 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.92,"keypoints":17} -2025-02-05 09:40:16 DATA:  test-backend-node.js result: performance: load: null total: 290 -2025-02-05 09:40:16 STATE: test-backend-node.js passed: movenet -2025-02-05 09:40:16 INFO:  test-backend-node.js test face matching -2025-02-05 09:40:16 STATE: test-backend-node.js passed: face database 40 -2025-02-05 09:40:16 STATE: test-backend-node.js passed: face match {"first":{"index":4,"similarity":0.78}} {"second":{"index":4,"similarity":0.5}} {"third":{"index":4,"similarity":0.54}} -2025-02-05 09:40:16 INFO:  test-backend-node.js test face similarity alternative -2025-02-05 09:40:16 STATE: test-backend-node.js start face embeddings -2025-02-05 09:40:17 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:17 STATE: test-backend-node.js event: image -2025-02-05 09:40:17 STATE: test-backend-node.js event: detect -2025-02-05 09:40:17 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings -2025-02-05 09:40:17 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.47,"keypoints":3} -2025-02-05 09:40:17 DATA:  test-backend-node.js result: performance: load: null total: 347 -2025-02-05 09:40:17 STATE: test-backend-node.js passed: mobilefacenet {"embedding":192} -2025-02-05 09:40:17 STATE: test-backend-node.js start face embeddings -2025-02-05 09:40:18 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:18 STATE: test-backend-node.js event: image -2025-02-05 09:40:19 STATE: test-backend-node.js event: detect -2025-02-05 09:40:19 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings -2025-02-05 09:40:19 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.47,"keypoints":3} -2025-02-05 09:40:19 DATA:  test-backend-node.js result: performance: load: null total: 360 -2025-02-05 09:40:19 STATE: test-backend-node.js passed: insightface {"embedding":512} -2025-02-05 09:40:19 INFO:  test-backend-node.js test face attention -2025-02-05 09:40:19 STATE: test-backend-node.js start face attention -2025-02-05 09:40:19 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:19 STATE: test-backend-node.js event: image -2025-02-05 09:40:20 STATE: test-backend-node.js event: detect -2025-02-05 09:40:20 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face attention -2025-02-05 09:40:20 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 7 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.47,"keypoints":3} -2025-02-05 09:40:20 DATA:  test-backend-node.js result: performance: load: null total: 347 -2025-02-05 09:40:20 STATE: test-backend-node.js passed: face attention -2025-02-05 09:40:20 INFO:  test-backend-node.js test detectors -2025-02-05 09:40:20 STATE: test-backend-node.js start detectors -2025-02-05 09:40:20 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:20 STATE: test-backend-node.js event: image -2025-02-05 09:40:20 STATE: test-backend-node.js event: detect -2025-02-05 09:40:20 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg detectors -2025-02-05 09:40:20 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2025-02-05 09:40:20 DATA:  test-backend-node.js result: performance: load: null total: 152 -2025-02-05 09:40:20 STATE: test-backend-node.js passed: detector result face match -2025-02-05 09:40:20 STATE: test-backend-node.js passed: detector result hand match -2025-02-05 09:40:20 INFO:  test-backend-node.js test: multi-instance -2025-02-05 09:40:20 STATE: test-backend-node.js start multi instance -2025-02-05 09:40:20 STATE: test-backend-node.js event: image -2025-02-05 09:40:20 STATE: test-backend-node.js event: detect -2025-02-05 09:40:20 STATE: test-backend-node.js passed: detect: random multi instance -2025-02-05 09:40:20 DATA:  test-backend-node.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} -2025-02-05 09:40:20 DATA:  test-backend-node.js result: performance: load: null total: 146 -2025-02-05 09:40:20 INFO:  test-backend-node.js test: first instance -2025-02-05 09:40:20 STATE: test-backend-node.js start multi instance -2025-02-05 09:40:21 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2025-02-05 09:40:21 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance -2025-02-05 09:40:21 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.97,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2025-02-05 09:40:21 DATA:  test-backend-node.js result: performance: load: null total: 149 -2025-02-05 09:40:21 INFO:  test-backend-node.js test: second instance -2025-02-05 09:40:21 STATE: test-backend-node.js start multi instance -2025-02-05 09:40:21 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2025-02-05 09:40:21 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance -2025-02-05 09:40:21 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.97,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2025-02-05 09:40:21 DATA:  test-backend-node.js result: performance: load: null total: 151 -2025-02-05 09:40:21 INFO:  test-backend-node.js test: concurrent -2025-02-05 09:40:21 STATE: test-backend-node.js start concurrent -2025-02-05 09:40:21 STATE: test-backend-node.js start concurrent -2025-02-05 09:40:21 STATE: test-backend-node.js start concurrent -2025-02-05 09:40:21 STATE: test-backend-node.js start concurrent -2025-02-05 09:40:21 STATE: test-backend-node.js start concurrent -2025-02-05 09:40:21 STATE: test-backend-node.js start concurrent -2025-02-05 09:40:21 STATE: test-backend-node.js start concurrent -2025-02-05 09:40:21 STATE: test-backend-node.js start concurrent -2025-02-05 09:40:21 STATE: test-backend-node.js start concurrent -2025-02-05 09:40:21 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:21 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:21 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:21 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:22 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:22 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:22 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2025-02-05 09:40:22 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2025-02-05 09:40:22 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2025-02-05 09:40:22 STATE: test-backend-node.js event: image -2025-02-05 09:40:22 STATE: test-backend-node.js event: image -2025-02-05 09:40:22 STATE: test-backend-node.js event: image -2025-02-05 09:40:24 STATE: test-backend-node.js event: detect -2025-02-05 09:40:24 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent -2025-02-05 09:40:24 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.97,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2025-02-05 09:40:24 DATA:  test-backend-node.js result: performance: load: null total: 1286 -2025-02-05 09:40:24 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent -2025-02-05 09:40:24 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.97,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2025-02-05 09:40:24 DATA:  test-backend-node.js result: performance: load: null total: 1286 -2025-02-05 09:40:24 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent -2025-02-05 09:40:24 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.97,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2025-02-05 09:40:24 DATA:  test-backend-node.js result: performance: load: null total: 1286 -2025-02-05 09:40:24 STATE: test-backend-node.js event: detect -2025-02-05 09:40:24 STATE: test-backend-node.js event: detect -2025-02-05 09:40:24 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent -2025-02-05 09:40:24 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.9,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2025-02-05 09:40:24 DATA:  test-backend-node.js result: performance: load: null total: 1287 -2025-02-05 09:40:24 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent -2025-02-05 09:40:24 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.9,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2025-02-05 09:40:24 DATA:  test-backend-node.js result: performance: load: null total: 1287 -2025-02-05 09:40:24 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent -2025-02-05 09:40:24 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.9,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2025-02-05 09:40:24 DATA:  test-backend-node.js result: performance: load: null total: 1287 -2025-02-05 09:40:24 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent -2025-02-05 09:40:24 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2025-02-05 09:40:24 DATA:  test-backend-node.js result: performance: load: null total: 1287 -2025-02-05 09:40:24 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent -2025-02-05 09:40:24 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2025-02-05 09:40:24 DATA:  test-backend-node.js result: performance: load: null total: 1287 -2025-02-05 09:40:24 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent -2025-02-05 09:40:24 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2025-02-05 09:40:24 DATA:  test-backend-node.js result: performance: load: null total: 1287 -2025-02-05 09:40:24 INFO:  test-backend-node.js test: monkey-patch -2025-02-05 09:40:24 STATE: test-backend-node.js event: image -2025-02-05 09:40:24 STATE: test-backend-node.js event: detect -2025-02-05 09:40:24 STATE: test-backend-node.js passed: monkey patch -2025-02-05 09:40:24 STATE: test-backend-node.js passed: segmentation [262144] -2025-02-05 09:40:24 STATE: test-backend-node.js passeed: equal usage -2025-02-05 09:40:24 INFO:  test-backend-node.js test: input compare -2025-02-05 09:40:24 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:25 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:25 STATE: test-backend-node.js passed: image compare 0 23.275441687091504 -2025-02-05 09:40:25 INFO:  test-backend-node.js events: {"image":29,"detect":29,"warmup":2} -2025-02-05 09:40:25 INFO:  test-backend-node.js tensors 14855 -2025-02-05 09:40:25 INFO:  test-backend-node.js test complete: 23780 ms -2025-02-05 09:40:25 INFO:  -2025-02-05 09:40:25 INFO:  test-backend-node-gpu.js start -2025-02-05 09:40:25 INFO:  test-backend-node-gpu.js test: configuration validation -2025-02-05 09:40:25 STATE: test-backend-node-gpu.js passed: configuration default validation [] -2025-02-05 09:40:25 STATE: test-backend-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] -2025-02-05 09:40:25 INFO:  test-backend-node-gpu.js test: model load -2025-02-05 09:40:25 STATE: test-backend-node-gpu.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] -2025-02-05 09:40:25 INFO:  test-backend-node-gpu.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} -2025-02-05 09:40:25 INFO:  test-backend-node-gpu.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} -2025-02-05 09:40:25 INFO:  test-backend-node-gpu.js test: warmup -2025-02-05 09:40:25 STATE: test-backend-node-gpu.js passed: create human -2025-02-05 09:40:25 INFO:  test-backend-node-gpu.js human version: 3.3.5 -2025-02-05 09:40:25 INFO:  test-backend-node-gpu.js platform: agent: -2025-02-05 09:40:25 INFO:  test-backend-node-gpu.js tfjs version: 4.22.0 -2025-02-05 09:40:25 INFO:  test-backend-node-gpu.js env: {"browser":false,"node":true,"platform":"","agent":"","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.22.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":172} -2025-02-05 09:40:25 STATE: test-backend-node-gpu.js passed: set backend: tensorflow -2025-02-05 09:40:25 STATE: test-backend-node-gpu.js tensors 1785 -2025-02-05 09:40:25 STATE: test-backend-node-gpu.js result: defined models: 11 loaded models: 11 -2025-02-05 09:40:25 STATE: test-backend-node-gpu.js passed: load models 11 -2025-02-05 09:40:25 STATE: test-backend-node-gpu.js passed: warmup: none default -2025-02-05 09:40:25 DATA:  test-backend-node-gpu.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} -2025-02-05 09:40:25 DATA:  test-backend-node-gpu.js result: performance: load: null total: null -2025-02-05 09:40:25 STATE: test-backend-node-gpu.js passed: warmup none result match -2025-02-05 09:40:25 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:26 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:26 STATE: test-backend-node-gpu.js event: warmup -2025-02-05 09:40:26 STATE: test-backend-node-gpu.js passed: warmup: face default -2025-02-05 09:40:26 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} -2025-02-05 09:40:26 DATA:  test-backend-node-gpu.js result: performance: load: null total: 502 -2025-02-05 09:40:26 STATE: test-backend-node-gpu.js passed: warmup face result match -2025-02-05 09:40:26 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:26 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:26 STATE: test-backend-node-gpu.js event: warmup -2025-02-05 09:40:26 STATE: test-backend-node-gpu.js passed: warmup: body default -2025-02-05 09:40:26 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:26 DATA:  test-backend-node-gpu.js result: performance: load: null total: 354 -2025-02-05 09:40:26 STATE: test-backend-node-gpu.js passed: warmup body result match -2025-02-05 09:40:26 STATE: test-backend-node-gpu.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.6,"gender":"female","genderScore":0.97},"emotion":[{"score":0.47,"emotion":"angry"},{"score":0.32,"emotion":"fear"},{"score":0.1,"emotion":"neutral"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 16% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} -2025-02-05 09:40:26 INFO:  test-backend-node-gpu.js test: details verification -2025-02-05 09:40:26 STATE: test-backend-node-gpu.js start default -2025-02-05 09:40:26 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:26 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg default -2025-02-05 09:40:27 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:27 DATA:  test-backend-node-gpu.js result: performance: load: null total: 361 -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details face length 1 -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details face score 1 0.93 1 -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details face age/gender 23.6 female 0.97 2.34 -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details face arrays 4 478 1024 -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details face emotion 3 {"score":0.48,"emotion":"angry"} -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details face anti-spoofing 0.8 -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details face liveness 0.93 -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details body length 1 -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details body 0.92 17 6 -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details hand length 1 -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details hand 0.51 0.73 point -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details hand arrays 21 5 7 -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details gesture length 7 -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details object length 1 -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: details object 0.72 person -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:27 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 -2025-02-05 09:40:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} -2025-02-05 09:40:28 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:28 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:28 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 -2025-02-05 09:40:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:28 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:29 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:29 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 -2025-02-05 09:40:29 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:29 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:29 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:29 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 -2025-02-05 09:40:29 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} -2025-02-05 09:40:29 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:30 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:30 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 -2025-02-05 09:40:30 INFO:  test-backend-node-gpu.js test default -2025-02-05 09:40:30 STATE: test-backend-node-gpu.js start async -2025-02-05 09:40:30 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:30 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:30 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:30 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg async -2025-02-05 09:40:30 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:30 DATA:  test-backend-node-gpu.js result: performance: load: null total: 367 -2025-02-05 09:40:30 STATE: test-backend-node-gpu.js passed: default result face match 1 female 0.97 -2025-02-05 09:40:30 INFO:  test-backend-node-gpu.js test sync -2025-02-05 09:40:30 STATE: test-backend-node-gpu.js start sync -2025-02-05 09:40:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:31 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:31 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:31 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sync -2025-02-05 09:40:31 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:31 DATA:  test-backend-node-gpu.js result: performance: load: null total: 372 -2025-02-05 09:40:31 STATE: test-backend-node-gpu.js passed: default sync 1 female 0.97 -2025-02-05 09:40:31 INFO:  test-backend-node-gpu.js test: image process -2025-02-05 09:40:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:31 STATE: test-backend-node-gpu.js passed: image input null [1,256,256,3] -2025-02-05 09:40:31 INFO:  test-backend-node-gpu.js test: image null -2025-02-05 09:40:31 STATE: test-backend-node-gpu.js passed: invalid input could not convert input to tensor -2025-02-05 09:40:31 INFO:  test-backend-node-gpu.js test face similarity -2025-02-05 09:40:31 STATE: test-backend-node-gpu.js start face similarity -2025-02-05 09:40:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:31 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:32 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:32 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face similarity -2025-02-05 09:40:32 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2025-02-05 09:40:32 DATA:  test-backend-node-gpu.js result: performance: load: null total: 360 -2025-02-05 09:40:32 STATE: test-backend-node-gpu.js start face similarity -2025-02-05 09:40:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:32 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:32 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:32 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg face similarity -2025-02-05 09:40:32 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:32 DATA:  test-backend-node-gpu.js result: performance: load: null total: 344 -2025-02-05 09:40:32 STATE: test-backend-node-gpu.js start face similarity -2025-02-05 09:40:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2025-02-05 09:40:32 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:33 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:33 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg face similarity -2025-02-05 09:40:33 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} -2025-02-05 09:40:33 DATA:  test-backend-node-gpu.js result: performance: load: null total: 331 -2025-02-05 09:40:33 STATE: test-backend-node-gpu.js passed: face descriptor -2025-02-05 09:40:33 STATE: test-backend-node-gpu.js passed: face similarity {"similarity":[1,0.43,0.57],"descriptors":[1024,1024,1024]} -2025-02-05 09:40:33 INFO:  test-backend-node-gpu.js test object -2025-02-05 09:40:33 STATE: test-backend-node-gpu.js start object -2025-02-05 09:40:33 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:33 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:33 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:33 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object -2025-02-05 09:40:33 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:33 DATA:  test-backend-node-gpu.js result: performance: load: null total: 335 -2025-02-05 09:40:33 STATE: test-backend-node-gpu.js passed: centernet -2025-02-05 09:40:33 STATE: test-backend-node-gpu.js start object -2025-02-05 09:40:34 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:34 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:34 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:34 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object -2025-02-05 09:40:34 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:34 DATA:  test-backend-node-gpu.js result: performance: load: null total: 333 -2025-02-05 09:40:34 STATE: test-backend-node-gpu.js passed: nanodet -2025-02-05 09:40:34 INFO:  test-backend-node-gpu.js test sensitive -2025-02-05 09:40:34 STATE: test-backend-node-gpu.js start sensitive -2025-02-05 09:40:35 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:35 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:35 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:35 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sensitive -2025-02-05 09:40:35 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.92,"keypoints":17} -2025-02-05 09:40:35 DATA:  test-backend-node-gpu.js result: performance: load: null total: 306 -2025-02-05 09:40:35 STATE: test-backend-node-gpu.js passed: sensitive result match -2025-02-05 09:40:35 STATE: test-backend-node-gpu.js passed: sensitive face result match -2025-02-05 09:40:35 STATE: test-backend-node-gpu.js passed: sensitive face emotion result [{"score":0.48,"emotion":"angry"},{"score":0.31,"emotion":"fear"},{"score":0.11,"emotion":"neutral"}] -2025-02-05 09:40:35 STATE: test-backend-node-gpu.js passed: sensitive body result match -2025-02-05 09:40:35 STATE: test-backend-node-gpu.js passed: sensitive hand result match -2025-02-05 09:40:35 INFO:  test-backend-node-gpu.js test body -2025-02-05 09:40:35 STATE: test-backend-node-gpu.js start blazepose -2025-02-05 09:40:37 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:37 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:37 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:37 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg blazepose -2025-02-05 09:40:37 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.99,"keypoints":39} -2025-02-05 09:40:37 DATA:  test-backend-node-gpu.js result: performance: load: null total: 425 -2025-02-05 09:40:37 STATE: test-backend-node-gpu.js passed: blazepose -2025-02-05 09:40:37 STATE: test-backend-node-gpu.js start efficientpose -2025-02-05 09:40:38 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:38 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:38 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:38 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg efficientpose -2025-02-05 09:40:38 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.75,"keypoints":13} -2025-02-05 09:40:38 DATA:  test-backend-node-gpu.js result: performance: load: null total: 362 -2025-02-05 09:40:38 STATE: test-backend-node-gpu.js passed: efficientpose -2025-02-05 09:40:38 STATE: test-backend-node-gpu.js start posenet -2025-02-05 09:40:39 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:39 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:39 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:39 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg posenet -2025-02-05 09:40:39 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.96,"keypoints":16} -2025-02-05 09:40:39 DATA:  test-backend-node-gpu.js result: performance: load: null total: 291 -2025-02-05 09:40:39 STATE: test-backend-node-gpu.js passed: posenet -2025-02-05 09:40:39 STATE: test-backend-node-gpu.js start movenet -2025-02-05 09:40:39 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:39 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:40 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:40 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg movenet -2025-02-05 09:40:40 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.92,"keypoints":17} -2025-02-05 09:40:40 DATA:  test-backend-node-gpu.js result: performance: load: null total: 291 -2025-02-05 09:40:40 STATE: test-backend-node-gpu.js passed: movenet -2025-02-05 09:40:40 INFO:  test-backend-node-gpu.js test face matching -2025-02-05 09:40:40 STATE: test-backend-node-gpu.js passed: face database 40 -2025-02-05 09:40:40 STATE: test-backend-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.78}} {"second":{"index":4,"similarity":0.5}} {"third":{"index":4,"similarity":0.54}} -2025-02-05 09:40:40 INFO:  test-backend-node-gpu.js test face similarity alternative -2025-02-05 09:40:40 STATE: test-backend-node-gpu.js start face embeddings -2025-02-05 09:40:40 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:40 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:41 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:41 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings -2025-02-05 09:40:41 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.47,"keypoints":3} -2025-02-05 09:40:41 DATA:  test-backend-node-gpu.js result: performance: load: null total: 365 -2025-02-05 09:40:41 STATE: test-backend-node-gpu.js passed: mobilefacenet {"embedding":192} -2025-02-05 09:40:41 STATE: test-backend-node-gpu.js start face embeddings -2025-02-05 09:40:41 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:41 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:42 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:42 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings -2025-02-05 09:40:42 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.47,"keypoints":3} -2025-02-05 09:40:42 DATA:  test-backend-node-gpu.js result: performance: load: null total: 353 -2025-02-05 09:40:42 STATE: test-backend-node-gpu.js passed: insightface {"embedding":512} -2025-02-05 09:40:42 INFO:  test-backend-node-gpu.js test face attention -2025-02-05 09:40:42 STATE: test-backend-node-gpu.js start face attention -2025-02-05 09:40:42 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:42 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:42 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:42 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face attention -2025-02-05 09:40:42 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 7 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.47,"keypoints":3} -2025-02-05 09:40:42 DATA:  test-backend-node-gpu.js result: performance: load: null total: 349 -2025-02-05 09:40:42 STATE: test-backend-node-gpu.js passed: face attention -2025-02-05 09:40:42 INFO:  test-backend-node-gpu.js test detectors -2025-02-05 09:40:42 STATE: test-backend-node-gpu.js start detectors -2025-02-05 09:40:43 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:43 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:43 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:43 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg detectors -2025-02-05 09:40:43 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2025-02-05 09:40:43 DATA:  test-backend-node-gpu.js result: performance: load: null total: 152 -2025-02-05 09:40:43 STATE: test-backend-node-gpu.js passed: detector result face match -2025-02-05 09:40:43 STATE: test-backend-node-gpu.js passed: detector result hand match -2025-02-05 09:40:43 INFO:  test-backend-node-gpu.js test: multi-instance -2025-02-05 09:40:43 STATE: test-backend-node-gpu.js start multi instance -2025-02-05 09:40:43 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:43 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:43 STATE: test-backend-node-gpu.js passed: detect: random multi instance -2025-02-05 09:40:43 DATA:  test-backend-node-gpu.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} -2025-02-05 09:40:43 DATA:  test-backend-node-gpu.js result: performance: load: null total: 144 -2025-02-05 09:40:43 INFO:  test-backend-node-gpu.js test: first instance -2025-02-05 09:40:43 STATE: test-backend-node-gpu.js start multi instance -2025-02-05 09:40:43 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2025-02-05 09:40:43 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance -2025-02-05 09:40:43 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.97,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2025-02-05 09:40:43 DATA:  test-backend-node-gpu.js result: performance: load: null total: 144 -2025-02-05 09:40:43 INFO:  test-backend-node-gpu.js test: second instance -2025-02-05 09:40:43 STATE: test-backend-node-gpu.js start multi instance -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance -2025-02-05 09:40:44 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.97,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2025-02-05 09:40:44 DATA:  test-backend-node-gpu.js result: performance: load: null total: 148 -2025-02-05 09:40:44 INFO:  test-backend-node-gpu.js test: concurrent -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js start concurrent -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js start concurrent -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js start concurrent -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js start concurrent -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js start concurrent -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js start concurrent -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js start concurrent -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js start concurrent -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js start concurrent -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:44 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:45 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:45 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2025-02-05 09:40:45 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2025-02-05 09:40:45 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2025-02-05 09:40:45 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:45 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:45 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:46 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:46 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.97,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1257 -2025-02-05 09:40:46 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.97,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1257 -2025-02-05 09:40:46 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.97,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1257 -2025-02-05 09:40:46 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:46 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:46 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.9,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1257 -2025-02-05 09:40:46 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.9,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1257 -2025-02-05 09:40:46 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.9,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1257 -2025-02-05 09:40:46 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1257 -2025-02-05 09:40:46 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1257 -2025-02-05 09:40:46 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2025-02-05 09:40:46 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1257 -2025-02-05 09:40:46 INFO:  test-backend-node-gpu.js test: monkey-patch -2025-02-05 09:40:46 STATE: test-backend-node-gpu.js event: image -2025-02-05 09:40:46 STATE: test-backend-node-gpu.js event: detect -2025-02-05 09:40:46 STATE: test-backend-node-gpu.js passed: monkey patch -2025-02-05 09:40:47 STATE: test-backend-node-gpu.js passed: segmentation [262144] -2025-02-05 09:40:47 STATE: test-backend-node-gpu.js passeed: equal usage -2025-02-05 09:40:47 INFO:  test-backend-node-gpu.js test: input compare -2025-02-05 09:40:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2025-02-05 09:40:47 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2025-02-05 09:40:47 STATE: test-backend-node-gpu.js passed: image compare 0 23.275441687091504 -2025-02-05 09:40:47 INFO:  test-backend-node-gpu.js events: {"image":29,"detect":29,"warmup":2} -2025-02-05 09:40:47 INFO:  test-backend-node-gpu.js tensors 14855 -2025-02-05 09:40:47 INFO:  test-backend-node-gpu.js test complete: 21990 ms -2025-02-05 09:40:47 INFO:  -2025-02-05 09:40:47 INFO:  test-backend-node-wasm.js start -2025-02-05 09:40:48 DATA:  test-backend-node-wasm.js stdout: 2025-02-05 09:40:48 INFO:  { supported: true, backend: true, simd: undefined, multithread: undefined } https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@4.22.0/dist/ -2025-02-05 09:40:48 STATE: test-backend-node-wasm.js passed: model server: https://vladmandic.github.io/human-models/models/ -2025-02-05 09:40:48 INFO:  test-backend-node-wasm.js test: configuration validation -2025-02-05 09:40:48 STATE: test-backend-node-wasm.js passed: configuration default validation [] -2025-02-05 09:40:48 STATE: test-backend-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] -2025-02-05 09:40:48 INFO:  test-backend-node-wasm.js test: model load -2025-02-05 09:40:49 STATE: test-backend-node-wasm.js passed: models loaded 11 11 [{"name":"antispoof","loaded":true,"size":853098,"url":"https://vladmandic.github.io/human-models/models/antispoof.json"},{"name":"liveness","loaded":true,"size":592976,"url":"https://vladmandic.github.io/human-models/models/liveness.json"},{"name":"blazeface","loaded":true,"size":538928,"url":"https://vladmandic.github.io/human-models/models/blazeface.json"},{"name":"emotion","loaded":true,"size":820516,"url":"https://vladmandic.github.io/human-models/models/emotion.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"https://vladmandic.github.io/human-models/models/movenet-lightning.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"https://vladmandic.github.io/human-models/models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"https://vladmandic.github.io/human-models/models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"https://vladmandic.github.io/human-models/models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"https://vladmandic.github.io/human-models/models/centernet.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"https://vladmandic.github.io/human-models/models/faceres.json"}] -2025-02-05 09:40:49 INFO:  test-backend-node-wasm.js memory: {"memory":{"unreliable":false,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} -2025-02-05 09:40:49 INFO:  test-backend-node-wasm.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} -2025-02-05 09:40:49 INFO:  test-backend-node-wasm.js test: warmup -2025-02-05 09:40:49 STATE: test-backend-node-wasm.js passed: create human -2025-02-05 09:40:49 INFO:  test-backend-node-wasm.js human version: 3.3.5 -2025-02-05 09:40:49 INFO:  test-backend-node-wasm.js platform: agent: -2025-02-05 09:40:49 INFO:  test-backend-node-wasm.js tfjs version: 4.22.0 -2025-02-05 09:40:49 INFO:  test-backend-node-wasm.js env: {"browser":false,"node":true,"platform":"","agent":"","backends":["wasm"],"initial":false,"tfjs":{"version":"4.22.0"},"offscreen":false,"perfadd":false,"tensorflow":{},"wasm":{"supported":true,"backend":true,"simd":true,"multithread":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":174} -2025-02-05 09:40:49 STATE: test-backend-node-wasm.js passed: set backend: wasm -2025-02-05 09:40:49 STATE: test-backend-node-wasm.js tensors 1785 -2025-02-05 09:40:49 STATE: test-backend-node-wasm.js result: defined models: 11 loaded models: 11 -2025-02-05 09:40:49 STATE: test-backend-node-wasm.js passed: load models 11 -2025-02-05 09:40:49 STATE: test-backend-node-wasm.js passed: warmup: none default -2025-02-05 09:40:49 DATA:  test-backend-node-wasm.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} -2025-02-05 09:40:49 DATA:  test-backend-node-wasm.js result: performance: load: null total: null -2025-02-05 09:40:49 STATE: test-backend-node-wasm.js passed: warmup none result match -2025-02-05 09:40:49 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:40:50 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:40:50 STATE: test-backend-node-wasm.js event: warmup -2025-02-05 09:40:50 STATE: test-backend-node-wasm.js passed: warmup: face default -2025-02-05 09:40:50 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2025-02-05 09:40:50 DATA:  test-backend-node-wasm.js result: performance: load: null total: 593 -2025-02-05 09:40:50 STATE: test-backend-node-wasm.js passed: warmup face result match -2025-02-05 09:40:50 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js event: warmup -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: warmup: body default -2025-02-05 09:40:51 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:51 DATA:  test-backend-node-wasm.js result: performance: load: null total: 352 -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: warmup body result match -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.6,"gender":"female","genderScore":0.97},"emotion":[{"score":0.48,"emotion":"angry"},{"score":0.31,"emotion":"fear"},{"score":0.11,"emotion":"neutral"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 16% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} -2025-02-05 09:40:51 INFO:  test-backend-node-wasm.js test: details verification -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js start default -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg default -2025-02-05 09:40:51 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:51 DATA:  test-backend-node-wasm.js result: performance: load: null total: 334 -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details face length 1 -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details face score 1 0.93 1 -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details face age/gender 23.6 female 0.97 2.34 -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details face arrays 4 478 1024 -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details face emotion 3 {"score":0.48,"emotion":"angry"} -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details face anti-spoofing 0.8 -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details face liveness 0.93 -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details body length 1 -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details body 0.92 17 6 -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details hand length 1 -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details hand 0.51 0.73 point -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details hand arrays 21 5 7 -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details gesture length 7 -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details object length 1 -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: details object 0.72 person -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} -2025-02-05 09:40:51 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:40:52 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:40:52 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 -2025-02-05 09:40:52 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} -2025-02-05 09:40:52 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:40:52 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:40:52 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 -2025-02-05 09:40:53 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2025-02-05 09:40:53 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:40:53 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:40:53 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 -2025-02-05 09:40:53 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} -2025-02-05 09:40:53 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:40:54 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:40:54 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 -2025-02-05 09:40:54 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} -2025-02-05 09:40:54 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:40:54 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:40:54 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 -2025-02-05 09:40:54 INFO:  test-backend-node-wasm.js test default -2025-02-05 09:40:54 STATE: test-backend-node-wasm.js start async -2025-02-05 09:40:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2025-02-05 09:40:56 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:40:57 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:40:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg async -2025-02-05 09:40:57 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:57 DATA:  test-backend-node-wasm.js result: performance: load: null total: 411 -2025-02-05 09:40:57 STATE: test-backend-node-wasm.js passed: default result face match 1 female 0.97 -2025-02-05 09:40:57 INFO:  test-backend-node-wasm.js test sync -2025-02-05 09:40:57 STATE: test-backend-node-wasm.js start sync -2025-02-05 09:40:59 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2025-02-05 09:40:59 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:40:59 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:40:59 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sync -2025-02-05 09:40:59 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:40:59 DATA:  test-backend-node-wasm.js result: performance: load: null total: 422 -2025-02-05 09:40:59 STATE: test-backend-node-wasm.js passed: default sync 1 female 0.97 -2025-02-05 09:40:59 INFO:  test-backend-node-wasm.js test: image process -2025-02-05 09:40:59 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2025-02-05 09:40:59 STATE: test-backend-node-wasm.js passed: image input null [1,256,256,3] -2025-02-05 09:40:59 INFO:  test-backend-node-wasm.js test: image null -2025-02-05 09:40:59 STATE: test-backend-node-wasm.js passed: invalid input could not convert input to tensor -2025-02-05 09:40:59 INFO:  test-backend-node-wasm.js test face similarity -2025-02-05 09:40:59 STATE: test-backend-node-wasm.js start face similarity -2025-02-05 09:41:00 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2025-02-05 09:41:00 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:41:01 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:41:01 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face similarity -2025-02-05 09:41:01 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2025-02-05 09:41:01 DATA:  test-backend-node-wasm.js result: performance: load: null total: 423 -2025-02-05 09:41:01 STATE: test-backend-node-wasm.js start face similarity -2025-02-05 09:41:01 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2025-02-05 09:41:01 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:41:02 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:41:02 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg face similarity -2025-02-05 09:41:02 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:41:02 DATA:  test-backend-node-wasm.js result: performance: load: null total: 343 -2025-02-05 09:41:02 STATE: test-backend-node-wasm.js start face similarity -2025-02-05 09:41:02 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} -2025-02-05 09:41:02 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:41:02 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:41:02 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg face similarity -2025-02-05 09:41:02 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} -2025-02-05 09:41:02 DATA:  test-backend-node-wasm.js result: performance: load: null total: 280 -2025-02-05 09:41:02 STATE: test-backend-node-wasm.js passed: face descriptor -2025-02-05 09:41:02 STATE: test-backend-node-wasm.js passed: face similarity {"similarity":[1,0.43,0.57],"descriptors":[1024,1024,1024]} -2025-02-05 09:41:02 INFO:  test-backend-node-wasm.js test object -2025-02-05 09:41:02 STATE: test-backend-node-wasm.js start object -2025-02-05 09:41:02 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2025-02-05 09:41:02 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:41:03 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:41:03 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object -2025-02-05 09:41:03 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:41:03 DATA:  test-backend-node-wasm.js result: performance: load: null total: 323 -2025-02-05 09:41:03 STATE: test-backend-node-wasm.js passed: centernet -2025-02-05 09:41:03 STATE: test-backend-node-wasm.js start object -2025-02-05 09:41:04 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2025-02-05 09:41:04 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:41:04 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:41:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object -2025-02-05 09:41:04 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} -2025-02-05 09:41:04 DATA:  test-backend-node-wasm.js result: performance: load: null total: 349 -2025-02-05 09:41:04 STATE: test-backend-node-wasm.js passed: nanodet -2025-02-05 09:41:04 INFO:  test-backend-node-wasm.js test sensitive -2025-02-05 09:41:04 STATE: test-backend-node-wasm.js start sensitive -2025-02-05 09:41:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2025-02-05 09:41:05 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:41:06 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:41:06 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sensitive -2025-02-05 09:41:06 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.92,"keypoints":17} -2025-02-05 09:41:06 DATA:  test-backend-node-wasm.js result: performance: load: null total: 292 -2025-02-05 09:41:06 STATE: test-backend-node-wasm.js passed: sensitive result match -2025-02-05 09:41:06 STATE: test-backend-node-wasm.js passed: sensitive face result match -2025-02-05 09:41:06 STATE: test-backend-node-wasm.js passed: sensitive face emotion result [{"score":0.48,"emotion":"angry"},{"score":0.31,"emotion":"fear"},{"score":0.11,"emotion":"neutral"}] -2025-02-05 09:41:06 STATE: test-backend-node-wasm.js passed: sensitive body result match -2025-02-05 09:41:06 STATE: test-backend-node-wasm.js passed: sensitive hand result match -2025-02-05 09:41:06 INFO:  test-backend-node-wasm.js test body -2025-02-05 09:41:06 STATE: test-backend-node-wasm.js start blazepose -2025-02-05 09:41:09 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2025-02-05 09:41:09 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:41:09 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:41:09 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg blazepose -2025-02-05 09:41:09 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.99,"keypoints":39} -2025-02-05 09:41:09 DATA:  test-backend-node-wasm.js result: performance: load: null total: 468 -2025-02-05 09:41:09 STATE: test-backend-node-wasm.js passed: blazepose -2025-02-05 09:41:09 STATE: test-backend-node-wasm.js start efficientpose -2025-02-05 09:41:10 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2025-02-05 09:41:10 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:41:11 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:41:11 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg efficientpose -2025-02-05 09:41:11 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.75,"keypoints":13} -2025-02-05 09:41:11 DATA:  test-backend-node-wasm.js result: performance: load: null total: 637 -2025-02-05 09:41:11 STATE: test-backend-node-wasm.js passed: efficientpose -2025-02-05 09:41:11 STATE: test-backend-node-wasm.js start posenet -2025-02-05 09:41:12 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2025-02-05 09:41:12 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:41:12 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:41:12 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg posenet -2025-02-05 09:41:12 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.96,"keypoints":16} -2025-02-05 09:41:12 DATA:  test-backend-node-wasm.js result: performance: load: null total: 264 -2025-02-05 09:41:12 STATE: test-backend-node-wasm.js passed: posenet -2025-02-05 09:41:12 STATE: test-backend-node-wasm.js start movenet -2025-02-05 09:41:13 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2025-02-05 09:41:13 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:41:13 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:41:13 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg movenet -2025-02-05 09:41:13 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.92,"keypoints":17} -2025-02-05 09:41:13 DATA:  test-backend-node-wasm.js result: performance: load: null total: 234 -2025-02-05 09:41:13 STATE: test-backend-node-wasm.js passed: movenet -2025-02-05 09:41:13 INFO:  test-backend-node-wasm.js test face matching -2025-02-05 09:41:13 STATE: test-backend-node-wasm.js passed: face database 40 -2025-02-05 09:41:13 STATE: test-backend-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.78}} {"second":{"index":4,"similarity":0.5}} {"third":{"index":4,"similarity":0.54}} -2025-02-05 09:41:13 INFO:  test-backend-node-wasm.js test face similarity alternative -2025-02-05 09:41:13 STATE: test-backend-node-wasm.js start face embeddings -2025-02-05 09:41:14 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2025-02-05 09:41:14 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:41:15 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:41:15 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings -2025-02-05 09:41:15 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.47,"keypoints":3} -2025-02-05 09:41:15 DATA:  test-backend-node-wasm.js result: performance: load: null total: 340 -2025-02-05 09:41:15 STATE: test-backend-node-wasm.js passed: mobilefacenet {"embedding":192} -2025-02-05 09:41:15 STATE: test-backend-node-wasm.js start face embeddings -2025-02-05 09:41:16 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2025-02-05 09:41:16 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:41:17 STATE: test-backend-node-wasm.js event: detect -2025-02-05 09:41:17 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings -2025-02-05 09:41:17 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.6,"gender":"female"} {} {"score":0.47,"keypoints":3} -2025-02-05 09:41:17 DATA:  test-backend-node-wasm.js result: performance: load: null total: 309 -2025-02-05 09:41:17 STATE: test-backend-node-wasm.js passed: insightface {"embedding":512} -2025-02-05 09:41:17 INFO:  test-backend-node-wasm.js test face attention -2025-02-05 09:41:17 STATE: test-backend-node-wasm.js start face attention -2025-02-05 09:41:17 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2025-02-05 09:41:17 STATE: test-backend-node-wasm.js event: image -2025-02-05 09:41:17 ERROR: test-backend-node-wasm.js failed: testDetect face attention -2025-02-05 09:41:17 ERROR: test-backend-node-wasm.js uncaughtException {"name":"Error","message":"BatchMatMul for non non-float32 tensors not yet supported.","stack":["Error: BatchMatMul for non non-float32 tensors not yet supported."," at Object.batchMatMul [as kernelFunc] (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.22.0_@tensorflow+tfjs-core@4.22.0/node_modules/@tensorflow/tfjs-backend-wasm/dist/tf-backend-wasm.node.js:1280:15)"," at kernelFunc (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-core@4.22.0/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4707:32)"," at /home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-core@4.22.0/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4767:27"," at Engine.scopedRun (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-core@4.22.0/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4572:23)"," at Engine.runKernelFunc (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-core@4.22.0/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4763:14)"," at Engine.runKernel (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-core@4.22.0/node_modules/@tensorflow/tfjs-core/dist/tf-core.node.js:4636:21)"," at matMul_ (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-converter@4.22.0_@tensorflow+tfjs-core@4.22.0/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:13414:19)"," at Object.matMul__op [as matMul] (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-converter@4.22.0_@tensorflow+tfjs-core@4.22.0/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:12223:29)"," at executeOp$9 (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-converter@4.22.0_@tensorflow+tfjs-core@4.22.0/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:29619:25)"," at /home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-converter@4.22.0_@tensorflow+tfjs-core@4.22.0/node_modules/@tensorflow/tfjs-converter/dist/tf-converter.node.js:30199:50"]} -2025-02-05 09:41:17 STATE: all tests complete -2025-02-05 09:41:17 INFO:  status {"test":"../demo/nodejs/node.js","passed":1,"failed":0} -2025-02-05 09:41:17 INFO:  status {"test":"../demo/nodejs/node-simple.js","passed":1,"failed":0} -2025-02-05 09:41:17 INFO:  status {"test":"../demo/nodejs/node-event.js","passed":1,"failed":0} -2025-02-05 09:41:17 INFO:  status {"test":"../demo/nodejs/node-similarity.js","passed":1,"failed":0} -2025-02-05 09:41:17 INFO:  status {"test":"../demo/nodejs/node-canvas.js","passed":1,"failed":0} -2025-02-05 09:41:17 INFO:  status {"test":"../demo/nodejs/process-folder.js","passed":1,"failed":0} -2025-02-05 09:41:17 INFO:  status {"test":"../demo/multithread/node-multiprocess.js","passed":1,"failed":0} -2025-02-05 09:41:17 INFO:  status {"test":"../demo/facematch/node-match.js","passed":1,"failed":0} -2025-02-05 09:41:17 INFO:  status {"test":"../demo/nodejs/node-bench.js","passed":1,"failed":0} -2025-02-05 09:41:17 INFO:  status {"test":"../test/test-node-emotion.js","passed":1,"failed":0} -2025-02-05 09:41:17 INFO:  status {"test":"test-node-load.js","passed":1,"failed":0} -2025-02-05 09:41:17 INFO:  status {"test":"test-node-gear.js","passed":4,"failed":0} -2025-02-05 09:41:17 INFO:  status {"test":"test-backend-node.js","passed":125,"failed":0} -2025-02-05 09:41:17 INFO:  status {"test":"test-backend-node-gpu.js","passed":125,"failed":0} -2025-02-05 09:41:17 INFO:  status {"test":"test-backend-node-wasm.js","passed":92,"failed":1} -2025-02-05 09:41:17 INFO:  failures {"count":1} -2025-02-05 09:41:17 WARN:  failed {"test":"test-backend-node-wasm.js","message":["error",["failed:","testDetect face attention"]]} +2025-08-26 11:39:38 INFO:  @vladmandic/human version 3.3.6 +2025-08-26 11:39:38 INFO:  User: vlado Platform: linux Arch: x64 Node: v22.13.1 +2025-08-26 11:39:38 INFO:  demos: [{"cmd":"../demo/nodejs/node.js","args":[]},{"cmd":"../demo/nodejs/node-simple.js","args":[]},{"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]},{"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]},{"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]},{"cmd":"../demo/nodejs/process-folder.js","args":["samples"]},{"cmd":"../demo/multithread/node-multiprocess.js","args":[]},{"cmd":"../demo/facematch/node-match.js","args":[]},{"cmd":"../demo/nodejs/node-bench.js","args":[]},{"cmd":"../test/test-node-emotion.js","args":[]}] +2025-08-26 11:39:38 INFO:  {"cmd":"../demo/nodejs/node.js","args":[]} start +2025-08-26 11:39:39 INFO:  {"cmd":"../demo/nodejs/node-simple.js","args":[]} start +2025-08-26 11:39:40 INFO:  {"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]} start +2025-08-26 11:39:40 INFO:  {"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]} start +2025-08-26 11:39:41 INFO:  {"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]} start +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: node:internal/modules/cjs/loader:1247 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: throw err; +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: ^ +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: Error: Cannot find module '../build/Release/canvas.node' +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: Require stack: +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: - /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: - /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/canvas.js +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: - /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/index.js +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: - /home/vlado/dev/human/demo/nodejs/node-canvas.js +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at Function._resolveFilename (node:internal/modules/cjs/loader:1244:15) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at Function._load (node:internal/modules/cjs/loader:1070:27) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at TracingChannel.traceSync (node:diagnostics_channel:322:14) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at wrapModuleLoad (node:internal/modules/cjs/loader:217:24) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at Module.require (node:internal/modules/cjs/loader:1335:12) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at require (node:internal/modules/helpers:136:16) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at Object. (/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js:3:18) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at Module._compile (node:internal/modules/cjs/loader:1562:14) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at Object..js (node:internal/modules/cjs/loader:1699:10) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at Module.load (node:internal/modules/cjs/loader:1313:32) { +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: code: 'MODULE_NOT_FOUND', +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: requireStack: [ +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: '/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js', +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: '/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/canvas.js', +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: '/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/index.js', +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: '/home/vlado/dev/human/demo/nodejs/node-canvas.js' +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: ] +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: Thrown at: +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at node:internal/modules/cjs/loader:1244:15 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at node:internal/modules/cjs/loader:1070:27 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at traceSync (node:diagnostics_channel:322:14) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at wrapModuleLoad (node:internal/modules/cjs/loader:217:24) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at node:internal/modules/cjs/loader:1335:12 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at require (node:internal/modules/helpers:136:16) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js:3:18 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at node:internal/modules/cjs/loader:1562:14 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at node:internal/modules/cjs/loader:1699:10 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: at node:internal/modules/cjs/loader:1313:32 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/node-canvas.js stderr: Node.js v22.13.1 +2025-08-26 11:39:41 INFO:  {"cmd":"../demo/nodejs/process-folder.js","args":["samples"]} start +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: node:internal/modules/cjs/loader:1247 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: throw err; +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: ^ +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: Error: Cannot find module '../build/Release/canvas.node' +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: Require stack: +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: - /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: - /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/canvas.js +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: - /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/index.js +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: - /home/vlado/dev/human/demo/nodejs/process-folder.js +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at Function._resolveFilename (node:internal/modules/cjs/loader:1244:15) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at Function._load (node:internal/modules/cjs/loader:1070:27) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at TracingChannel.traceSync (node:diagnostics_channel:322:14) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at wrapModuleLoad (node:internal/modules/cjs/loader:217:24) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at Module.require (node:internal/modules/cjs/loader:1335:12) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at require (node:internal/modules/helpers:136:16) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at Object. (/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js:3:18) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at Module._compile (node:internal/modules/cjs/loader:1562:14) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at Object..js (node:internal/modules/cjs/loader:1699:10) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at Module.load (node:internal/modules/cjs/loader:1313:32) { +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: code: 'MODULE_NOT_FOUND', +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: requireStack: [ +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: '/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js', +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: '/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/canvas.js', +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: '/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/index.js', +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: '/home/vlado/dev/human/demo/nodejs/process-folder.js' +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: ] +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: Thrown at: +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at node:internal/modules/cjs/loader:1244:15 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at node:internal/modules/cjs/loader:1070:27 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at traceSync (node:diagnostics_channel:322:14) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at wrapModuleLoad (node:internal/modules/cjs/loader:217:24) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at node:internal/modules/cjs/loader:1335:12 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at require (node:internal/modules/helpers:136:16) +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js:3:18 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at node:internal/modules/cjs/loader:1562:14 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at node:internal/modules/cjs/loader:1699:10 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: at node:internal/modules/cjs/loader:1313:32 +2025-08-26 11:39:41 WARN:  ../demo/nodejs/process-folder.js stderr: Node.js v22.13.1 +2025-08-26 11:39:41 INFO:  {"cmd":"../demo/multithread/node-multiprocess.js","args":[]} start +2025-08-26 11:39:54 INFO:  {"cmd":"../demo/facematch/node-match.js","args":[]} start +2025-08-26 11:39:55 INFO:  {"cmd":"../demo/nodejs/node-bench.js","args":[]} start +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: node:internal/modules/cjs/loader:1247 +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: throw err; +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: ^ +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: Error: Cannot find module '../build/Release/canvas.node' +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: Require stack: +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: - /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: - /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/canvas.js +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: - /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/index.js +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: - /home/vlado/dev/human/demo/nodejs/node-bench.js +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at Function._resolveFilename (node:internal/modules/cjs/loader:1244:15) +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at Function._load (node:internal/modules/cjs/loader:1070:27) +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at TracingChannel.traceSync (node:diagnostics_channel:322:14) +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at wrapModuleLoad (node:internal/modules/cjs/loader:217:24) +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at Module.require (node:internal/modules/cjs/loader:1335:12) +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at require (node:internal/modules/helpers:136:16) +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at Object. (/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js:3:18) +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at Module._compile (node:internal/modules/cjs/loader:1562:14) +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at Object..js (node:internal/modules/cjs/loader:1699:10) +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at Module.load (node:internal/modules/cjs/loader:1313:32) { +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: code: 'MODULE_NOT_FOUND', +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: requireStack: [ +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: '/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js', +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: '/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/canvas.js', +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: '/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/index.js', +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: '/home/vlado/dev/human/demo/nodejs/node-bench.js' +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: ] +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: Thrown at: +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at node:internal/modules/cjs/loader:1244:15 +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at node:internal/modules/cjs/loader:1070:27 +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at traceSync (node:diagnostics_channel:322:14) +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at wrapModuleLoad (node:internal/modules/cjs/loader:217:24) +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at node:internal/modules/cjs/loader:1335:12 +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at require (node:internal/modules/helpers:136:16) +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js:3:18 +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at node:internal/modules/cjs/loader:1562:14 +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at node:internal/modules/cjs/loader:1699:10 +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: at node:internal/modules/cjs/loader:1313:32 +2025-08-26 11:39:55 WARN:  ../demo/nodejs/node-bench.js stderr: Node.js v22.13.1 +2025-08-26 11:39:55 INFO:  {"cmd":"../test/test-node-emotion.js","args":[]} start +2025-08-26 11:40:01 INFO:  tests: ["test-node-load.js","test-node-gear.js","test-backend-node.js","test-backend-node-gpu.js","test-backend-node-wasm.js"] +2025-08-26 11:40:01 INFO:  +2025-08-26 11:40:01 INFO:  test-node-load.js start +2025-08-26 11:40:01 INFO:  test-node-load.js load start {"human":"3.3.6","tf":"4.22.0","progress":0} +2025-08-26 11:40:01 DATA:  test-node-load.js load interval {"elapsed":0,"progress":0} +2025-08-26 11:40:01 DATA:  test-node-load.js load interval {"elapsed":10,"progress":0} +2025-08-26 11:40:01 DATA:  test-node-load.js load interval {"elapsed":23,"progress":0.12865823126207815} +2025-08-26 11:40:01 DATA:  test-node-load.js load interval {"elapsed":33,"progress":0.2204080722078057} +2025-08-26 11:40:01 DATA:  test-node-load.js load interval {"elapsed":56,"progress":0.4726970548784964} +2025-08-26 11:40:01 DATA:  test-node-load.js load interval {"elapsed":68,"progress":0.68355493315821} +2025-08-26 11:40:01 STATE: test-node-load.js passed {"progress":1} +2025-08-26 11:40:01 INFO:  test-node-load.js load final {"progress":1} +2025-08-26 11:40:01 DATA:  test-node-load.js load interval {"elapsed":426,"progress":1} +2025-08-26 11:40:01 INFO:  +2025-08-26 11:40:01 INFO:  test-node-gear.js start +2025-08-26 11:40:02 DATA:  test-node-gear.js input: ["samples/in/ai-face.jpg"] +2025-08-26 11:40:02 STATE: test-node-gear.js passed: gear faceres samples/in/ai-face.jpg +2025-08-26 11:40:02 DATA:  test-node-gear.js results {"face":0,"model":"faceres","image":"samples/in/ai-face.jpg","age":23.6,"gender":"female","genderScore":0.95,"emotion":[]} +2025-08-26 11:40:03 STATE: test-node-gear.js passed: gear gear samples/in/ai-face.jpg +2025-08-26 11:40:03 DATA:  test-node-gear.js results {"face":0,"model":"gear","image":"samples/in/ai-face.jpg","age":25.2,"gender":"female","genderScore":0.7,"race":[{"score":0.96,"race":"white"}],"emotion":[]} +2025-08-26 11:40:03 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg +2025-08-26 11:40:03 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":22.6,"gender":"female","genderScore":0.98,"emotion":[]} +2025-08-26 11:40:03 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg +2025-08-26 11:40:03 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":22.6,"gender":"male","emotion":[]} +2025-08-26 11:40:03 INFO:  +2025-08-26 11:40:03 INFO:  test-backend-node.js start +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: node:internal/modules/cjs/loader:1247 +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: throw err; +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: ^ +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: Error: Cannot find module '../build/Release/canvas.node' +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: Require stack: +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: - /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: - /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/canvas.js +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: - /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/index.js +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: - /home/vlado/dev/human/test/test-node-main.js +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: - /home/vlado/dev/human/test/test-backend-node.js +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at Function._resolveFilename (node:internal/modules/cjs/loader:1244:15) +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at Function._load (node:internal/modules/cjs/loader:1070:27) +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at TracingChannel.traceSync (node:diagnostics_channel:322:14) +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at wrapModuleLoad (node:internal/modules/cjs/loader:217:24) +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at Module.require (node:internal/modules/cjs/loader:1335:12) +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at require (node:internal/modules/helpers:136:16) +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at Object. (/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js:3:18) +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at Module._compile (node:internal/modules/cjs/loader:1562:14) +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at Object..js (node:internal/modules/cjs/loader:1699:10) +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at Module.load (node:internal/modules/cjs/loader:1313:32) { +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: code: 'MODULE_NOT_FOUND', +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: requireStack: [ +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: '/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js', +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: '/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/canvas.js', +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: '/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/index.js', +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: '/home/vlado/dev/human/test/test-node-main.js', +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: '/home/vlado/dev/human/test/test-backend-node.js' +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: ] +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: Thrown at: +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at node:internal/modules/cjs/loader:1244:15 +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at node:internal/modules/cjs/loader:1070:27 +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at traceSync (node:diagnostics_channel:322:14) +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at wrapModuleLoad (node:internal/modules/cjs/loader:217:24) +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at node:internal/modules/cjs/loader:1335:12 +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at require (node:internal/modules/helpers:136:16) +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js:3:18 +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at node:internal/modules/cjs/loader:1562:14 +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at node:internal/modules/cjs/loader:1699:10 +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: at node:internal/modules/cjs/loader:1313:32 +2025-08-26 11:40:03 WARN:  test-backend-node.js stderr: Node.js v22.13.1 +2025-08-26 11:40:03 INFO:  +2025-08-26 11:40:03 INFO:  test-backend-node-gpu.js start +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: /home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-node-gpu@4.22.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-node-gpu/dist/index.js:61 +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: throw new Error("The Node.js native addon module (tfjs_binding.node) can not " + +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: ^ +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: Error: The Node.js native addon module (tfjs_binding.node) can not be found at path: /home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-node-gpu@4.22.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-node-gpu/lib/napi-v8/tfjs_binding.node. +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: Please run command 'npm rebuild @tensorflow/tfjs-node-gpu --build-addon-from-source' to rebuild the native addon module. +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: If you have problem with building the addon module, please check https://github.com/tensorflow/tfjs/blob/master/tfjs-node/WINDOWS_TROUBLESHOOTING.md or file an issue. +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at Object. (/home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-node-gpu@4.22.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-node-gpu/dist/index.js:61:11) +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at Module._compile (node:internal/modules/cjs/loader:1562:14) +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at Object..js (node:internal/modules/cjs/loader:1699:10) +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at Module.load (node:internal/modules/cjs/loader:1313:32) +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at Function._load (node:internal/modules/cjs/loader:1123:12) +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at TracingChannel.traceSync (node:diagnostics_channel:322:14) +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at wrapModuleLoad (node:internal/modules/cjs/loader:217:24) +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at Module.require (node:internal/modules/cjs/loader:1335:12) +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at require (node:internal/modules/helpers:136:16) +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at /home/vlado/dev/human/dist/human.node-gpu.js:7:1670 +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: Thrown at: +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at /home/vlado/dev/human/node_modules/.pnpm/@tensorflow+tfjs-node-gpu@4.22.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs-node-gpu/dist/index.js:61:11 +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at node:internal/modules/cjs/loader:1562:14 +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at node:internal/modules/cjs/loader:1699:10 +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at node:internal/modules/cjs/loader:1313:32 +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at node:internal/modules/cjs/loader:1123:12 +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at traceSync (node:diagnostics_channel:322:14) +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at wrapModuleLoad (node:internal/modules/cjs/loader:217:24) +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at node:internal/modules/cjs/loader:1335:12 +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at require (node:internal/modules/helpers:136:16) +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: at /home/vlado/dev/human/dist/human.node-gpu.js:7:1670 +2025-08-26 11:40:04 WARN:  test-backend-node-gpu.js stderr: Node.js v22.13.1 +2025-08-26 11:40:04 INFO:  +2025-08-26 11:40:04 INFO:  test-backend-node-wasm.js start +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: node:internal/modules/cjs/loader:1247 +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: throw err; +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: ^ +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: Error: Cannot find module '../build/Release/canvas.node' +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: Require stack: +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: - /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: - /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/canvas.js +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: - /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/index.js +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: - /home/vlado/dev/human/test/test-backend-node-wasm.js +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at Function._resolveFilename (node:internal/modules/cjs/loader:1244:15) +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at Function._load (node:internal/modules/cjs/loader:1070:27) +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at TracingChannel.traceSync (node:diagnostics_channel:322:14) +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at wrapModuleLoad (node:internal/modules/cjs/loader:217:24) +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at Module.require (node:internal/modules/cjs/loader:1335:12) +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at require (node:internal/modules/helpers:136:16) +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at Object. (/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js:3:18) +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at Module._compile (node:internal/modules/cjs/loader:1562:14) +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at Object..js (node:internal/modules/cjs/loader:1699:10) +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at Module.load (node:internal/modules/cjs/loader:1313:32) { +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: code: 'MODULE_NOT_FOUND', +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: requireStack: [ +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: '/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js', +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: '/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/canvas.js', +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: '/home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/index.js', +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: '/home/vlado/dev/human/test/test-backend-node-wasm.js' +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: ] +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: Thrown at: +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at node:internal/modules/cjs/loader:1244:15 +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at node:internal/modules/cjs/loader:1070:27 +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at traceSync (node:diagnostics_channel:322:14) +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at wrapModuleLoad (node:internal/modules/cjs/loader:217:24) +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at node:internal/modules/cjs/loader:1335:12 +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at require (node:internal/modules/helpers:136:16) +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at /home/vlado/dev/human/node_modules/.pnpm/canvas@3.2.0/node_modules/canvas/lib/bindings.js:3:18 +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at node:internal/modules/cjs/loader:1562:14 +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at node:internal/modules/cjs/loader:1699:10 +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: at node:internal/modules/cjs/loader:1313:32 +2025-08-26 11:40:04 WARN:  test-backend-node-wasm.js stderr: Node.js v22.13.1 +2025-08-26 11:40:04 STATE: all tests complete +2025-08-26 11:40:04 INFO:  status {"test":"../demo/nodejs/node.js","passed":1,"failed":0} +2025-08-26 11:40:04 INFO:  status {"test":"../demo/nodejs/node-simple.js","passed":1,"failed":0} +2025-08-26 11:40:04 INFO:  status {"test":"../demo/nodejs/node-event.js","passed":1,"failed":0} +2025-08-26 11:40:04 INFO:  status {"test":"../demo/nodejs/node-similarity.js","passed":1,"failed":0} 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"; \ No newline at end of file diff --git a/typedoc/assets/style.css b/typedoc/assets/style.css index 7f80f3dc..5ba5a2a9 100644 --- a/typedoc/assets/style.css +++ b/typedoc/assets/style.css @@ -1,14 +1,33 @@ @layer typedoc { + :root { + --dim-toolbar-contents-height: 2.5rem; + --dim-toolbar-border-bottom-width: 1px; + --dim-header-height: calc( + var(--dim-toolbar-border-bottom-width) + + var(--dim-toolbar-contents-height) + ); + + /* 0rem For mobile; unit is required for calculation in `calc` */ + --dim-container-main-margin-y: 0rem; + + --dim-footer-height: 3.5rem; + + --modal-animation-duration: 0.2s; + } + :root { /* Light */ --light-color-background: #f2f4f8; --light-color-background-secondary: #eff0f1; - --light-color-warning-text: #222; + /* Not to be confused with [:active](https://developer.mozilla.org/en-US/docs/Web/CSS/:active) */ + --light-color-background-active: #d6d8da; --light-color-background-warning: #e6e600; + --light-color-warning-text: #222; --light-color-accent: #c5c7c9; - --light-color-active-menu-item: var(--light-color-accent); + --light-color-active-menu-item: var(--light-color-background-active); --light-color-text: #222; - --light-color-text-aside: #6e6e6e; + --light-color-contrast-text: #000; + --light-color-text-aside: #5e5e5e; --light-color-icon-background: var(--light-color-background); --light-color-icon-text: var(--light-color-text); @@ -56,15 +75,20 @@ --light-external-icon: url("data:image/svg+xml;utf8,"); --light-color-scheme: light; + } + :root { /* Dark */ --dark-color-background: #2b2e33; --dark-color-background-secondary: #1e2024; + /* Not to be confused with [:active](https://developer.mozilla.org/en-US/docs/Web/CSS/:active) */ + --dark-color-background-active: #5d5d6a; --dark-color-background-warning: #bebe00; --dark-color-warning-text: #222; --dark-color-accent: #9096a2; - --dark-color-active-menu-item: #5d5d6a; + --dark-color-active-menu-item: var(--dark-color-background-active); --dark-color-text: #f5f5f5; + --dark-color-contrast-text: #ffffff; --dark-color-text-aside: #dddddd; --dark-color-icon-background: var(--dark-color-background-secondary); @@ -119,11 +143,13 @@ --color-background-secondary: var( --light-color-background-secondary ); + --color-background-active: var(--light-color-background-active); --color-background-warning: var(--light-color-background-warning); --color-warning-text: var(--light-color-warning-text); --color-accent: var(--light-color-accent); --color-active-menu-item: var(--light-color-active-menu-item); --color-text: var(--light-color-text); + --color-contrast-text: var(--light-color-contrast-text); --color-text-aside: var(--light-color-text-aside); --color-icon-background: var(--light-color-icon-background); @@ -179,11 +205,13 @@ --color-background-secondary: var( --dark-color-background-secondary ); + --color-background-active: var(--dark-color-background-active); --color-background-warning: var(--dark-color-background-warning); --color-warning-text: var(--dark-color-warning-text); --color-accent: var(--dark-color-accent); --color-active-menu-item: var(--dark-color-active-menu-item); --color-text: var(--dark-color-text); + --color-contrast-text: var(--dark-color-contrast-text); --color-text-aside: var(--dark-color-text-aside); --color-icon-background: var(--dark-color-icon-background); @@ -233,23 +261,17 @@ } } - html { - color-scheme: var(--color-scheme); - } - - body { - margin: 0; - } - :root[data-theme="light"] { --color-background: var(--light-color-background); --color-background-secondary: var(--light-color-background-secondary); + --color-background-active: var(--light-color-background-active); --color-background-warning: var(--light-color-background-warning); --color-warning-text: var(--light-color-warning-text); --color-icon-background: var(--light-color-icon-background); --color-accent: var(--light-color-accent); --color-active-menu-item: var(--light-color-active-menu-item); --color-text: var(--light-color-text); + --color-contrast-text: var(--light-color-contrast-text); --color-text-aside: var(--light-color-text-aside); --color-icon-text: var(--light-color-icon-text); @@ -299,12 +321,14 @@ :root[data-theme="dark"] { --color-background: var(--dark-color-background); --color-background-secondary: var(--dark-color-background-secondary); + --color-background-active: var(--dark-color-background-active); --color-background-warning: var(--dark-color-background-warning); --color-warning-text: var(--dark-color-warning-text); --color-icon-background: var(--dark-color-icon-background); --color-accent: var(--dark-color-accent); --color-active-menu-item: var(--dark-color-active-menu-item); --color-text: var(--dark-color-text); + --color-contrast-text: var(--dark-color-contrast-text); --color-text-aside: var(--dark-color-text-aside); --color-icon-text: var(--dark-color-icon-text); @@ -351,6 +375,13 @@ --color-scheme: var(--dark-color-scheme); } + html { + color-scheme: var(--color-scheme); + @media (prefers-reduced-motion: no-preference) { + scroll-behavior: smooth; + } + } + *:focus-visible, .tsd-accordion-summary:focus-visible svg { outline: 2px solid var(--color-focus-outline); @@ -421,16 +452,19 @@ border-top: 1px solid var(--color-accent); padding-top: 1rem; padding-bottom: 1rem; - max-height: 3.5rem; + max-height: var(--dim-footer-height); } footer > p { margin: 0 1em; } .container-main { - margin: 0 auto; + margin: var(--dim-container-main-margin-y) auto; /* toolbar, footer, margin */ - min-height: calc(100vh - 41px - 56px - 4rem); + min-height: calc( + 100svh - var(--dim-header-height) - var(--dim-footer-height) - + 2 * var(--dim-container-main-margin-y) + ); } @keyframes fade-in { @@ -450,29 +484,6 @@ opacity: 0; } } - @keyframes fade-in-delayed { - 0% { - opacity: 0; - } - 33% { - opacity: 0; - } - 100% { - opacity: 1; - } - } - @keyframes fade-out-delayed { - 0% { - opacity: 1; - visibility: visible; - } - 66% { - opacity: 0; - } - 100% { - opacity: 0; - } - } @keyframes pop-in-from-right { from { transform: translate(100%, 0); @@ -492,10 +503,12 @@ } body { background: var(--color-background); - font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Noto Sans", + font-family: + -apple-system, BlinkMacSystemFont, "Segoe UI", "Noto Sans", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji"; font-size: 16px; color: var(--color-text); + margin: 0; } a { @@ -514,6 +527,9 @@ a.tsd-anchor-link { color: var(--color-text); } + :target { + scroll-margin-block: calc(var(--dim-header-height) + 0.5rem); + } code, pre { @@ -545,7 +561,8 @@ box-sizing: border-box; } pre:hover > button, - pre > button.visible { + pre > button.visible, + pre > button:focus-visible { opacity: 1; } @@ -555,6 +572,52 @@ border-left: 4px solid gray; } + img { + max-width: 100%; + } + + * { + scrollbar-width: thin; + scrollbar-color: var(--color-accent) var(--color-icon-background); + } + + *::-webkit-scrollbar { + width: 0.75rem; + } + + *::-webkit-scrollbar-track { + background: var(--color-icon-background); + } + + *::-webkit-scrollbar-thumb { + background-color: var(--color-accent); + border-radius: 999rem; + border: 0.25rem solid var(--color-icon-background); + } + + dialog { + border: none; + outline: none; + padding: 0; + background-color: var(--color-background); + } + dialog::backdrop { + display: none; + } + #tsd-overlay { + background-color: rgba(0, 0, 0, 0.5); + position: fixed; + z-index: 9999; + top: 0; + left: 0; + right: 0; + bottom: 0; + animation: fade-in var(--modal-animation-duration) forwards; + } + #tsd-overlay.closing { + animation-name: fade-out; + } + .tsd-typography { line-height: 1.333em; } @@ -629,6 +692,7 @@ .tsd-breadcrumb { margin: 0; + margin-top: 1rem; padding: 0; color: var(--color-text-aside); } @@ -733,7 +797,7 @@ margin-right: 0.5em; border-radius: 0.33em; /* Leaving this at full opacity breaks event listeners on Firefox. - Don't remove unless you know what you're doing. */ + Don't remove unless you know what you're doing. */ opacity: 0.99; } .tsd-filter-input input[type="checkbox"]:focus-visible + svg { @@ -876,7 +940,8 @@ } .tsd-navigation.settings { - margin: 1rem 0; + margin: 0; + margin-bottom: 1rem; } .tsd-navigation > a, .tsd-navigation .tsd-accordion-summary { @@ -898,6 +963,7 @@ .tsd-navigation a.current, .tsd-page-navigation a.current { background: var(--color-active-menu-item); + color: var(--color-contrast-text); } .tsd-navigation a:hover, .tsd-page-navigation a:hover { @@ -931,14 +997,14 @@ margin-left: -1.5rem; } - .tsd-page-navigation-section { - margin-left: 10px; - } .tsd-page-navigation-section > summary { padding: 0.25rem; } + .tsd-page-navigation-section > summary > svg { + margin-right: 0.25rem; + } .tsd-page-navigation-section > div { - margin-left: 20px; + margin-left: 30px; } .tsd-page-navigation ul { padding-left: 1.75rem; @@ -964,6 +1030,10 @@ .tsd-accordion-summary { list-style-type: none; /* hide marker on non-safari */ outline: none; /* broken on safari, so just hide it */ + display: flex; + align-items: center; + gap: 0.25rem; + box-sizing: border-box; } .tsd-accordion-summary::-webkit-details-marker { display: none; /* hide marker on safari */ @@ -986,27 +1056,21 @@ padding-top: 0; padding-bottom: 0; } - .tsd-accordion .tsd-accordion-summary > svg { - margin-left: 0.25rem; - vertical-align: text-top; - } /* - We need to be careful to target the arrow indicating whether the accordion - is open, but not any other SVGs included in the details element. -*/ - .tsd-accordion:not([open]) > .tsd-accordion-summary > svg:first-child, - .tsd-accordion:not([open]) > .tsd-accordion-summary > h1 > svg:first-child, - .tsd-accordion:not([open]) > .tsd-accordion-summary > h2 > svg:first-child, - .tsd-accordion:not([open]) > .tsd-accordion-summary > h3 > svg:first-child, - .tsd-accordion:not([open]) > .tsd-accordion-summary > h4 > svg:first-child { + * We need to be careful to target the arrow indicating whether the accordion + * is open, but not any other SVGs included in the details element. + */ + .tsd-accordion:not([open]) > .tsd-accordion-summary > svg:first-child { transform: rotate(-90deg); } .tsd-index-content > :not(:first-child) { margin-top: 0.75rem; } - .tsd-index-heading { + .tsd-index-summary { margin-top: 1.5rem; margin-bottom: 0.75rem; + display: flex; + align-content: center; } .tsd-no-select { @@ -1061,117 +1125,104 @@ margin-bottom: 1rem; } - #tsd-search { - transition: background-color 0.2s; + #tsd-search[open] { + animation: fade-in var(--modal-animation-duration) ease-out forwards; } - #tsd-search .title { - position: relative; - z-index: 2; + #tsd-search[open].closing { + animation-name: fade-out; } - #tsd-search .field { - position: absolute; - left: 0; - top: 0; - right: 2.5rem; - height: 100%; + + /* Avoid setting `display` on closed dialog */ + #tsd-search[open] { + display: flex; + flex-direction: column; + padding: 1rem; + width: 32rem; + max-width: 90vw; + max-height: calc(100vh - env(keyboard-inset-height, 0px) - 25vh); + /* Anchor dialog to top */ + margin-top: 10vh; + border-radius: 6px; + will-change: max-height; } - #tsd-search .field input { + #tsd-search-input { box-sizing: border-box; - position: relative; - top: -50px; - z-index: 1; width: 100%; - padding: 0 10px; - opacity: 0; + padding: 0 0.625rem; /* 10px */ outline: 0; - border: 0; - background: transparent; + border: 2px solid var(--color-accent); + background-color: transparent; color: var(--color-text); + border-radius: 4px; + height: 2.5rem; + flex: 0 0 auto; + font-size: 0.875rem; + transition: border-color 0.2s, background-color 0.2s; } - #tsd-search .field label { - position: absolute; - overflow: hidden; - right: -40px; + #tsd-search-input:focus-visible { + background-color: var(--color-background-active); + border-color: transparent; + color: var(--color-contrast-text); } - #tsd-search .field input, - #tsd-search .title, - #tsd-toolbar-links a { - transition: opacity 0.2s; + #tsd-search-input::placeholder { + color: inherit; + opacity: 0.8; } - #tsd-search .results { - position: absolute; - visibility: hidden; - top: 40px; - width: 100%; + #tsd-search-results { margin: 0; padding: 0; list-style: none; - box-shadow: 0 0 4px rgba(0, 0, 0, 0.25); + flex: 1 1 auto; + display: flex; + flex-direction: column; + overflow-y: auto; } - #tsd-search .results li { + #tsd-search-results:not(:empty) { + margin-top: 0.5rem; + } + #tsd-search-results > li { background-color: var(--color-background); - line-height: initial; - padding: 4px; + line-height: 1.5; + box-sizing: border-box; + border-radius: 4px; } - #tsd-search .results li:nth-child(even) { + #tsd-search-results > li:nth-child(even) { background-color: var(--color-background-secondary); } - #tsd-search .results li.state { - display: none; + #tsd-search-results > li:is(:hover, [aria-selected="true"]) { + background-color: var(--color-background-active); + color: var(--color-contrast-text); } - #tsd-search .results li.current:not(.no-results), - #tsd-search .results li:hover:not(.no-results) { - background-color: var(--color-accent); - } - #tsd-search .results a { + /* It's important that this takes full size of parent `li`, to capture a click on `li` */ + #tsd-search-results > li > a { display: flex; align-items: center; - padding: 0.25rem; + padding: 0.5rem 0.25rem; box-sizing: border-box; + width: 100%; } - #tsd-search .results a:before { - top: 10px; + #tsd-search-results > li > a > .text { + flex: 1 1 auto; + min-width: 0; + overflow-wrap: anywhere; } - #tsd-search .results span.parent { + #tsd-search-results > li > a .parent { color: var(--color-text-aside); - font-weight: normal; } - #tsd-search.has-focus { - background-color: var(--color-accent); + #tsd-search-results > li > a mark { + color: inherit; + background-color: inherit; + font-weight: bold; } - #tsd-search.has-focus .field input { - top: 0; - opacity: 1; + #tsd-search-status { + flex: 1; + display: grid; + place-content: center; + text-align: center; + overflow-wrap: anywhere; } - #tsd-search.has-focus .title, - #tsd-search.has-focus #tsd-toolbar-links a { - z-index: 0; - opacity: 0; - } - #tsd-search.has-focus .results { - visibility: visible; - } - #tsd-search.loading .results li.state.loading { - display: block; - } - #tsd-search.failure .results li.state.failure { - display: block; - } - - #tsd-toolbar-links { - position: absolute; - top: 0; - right: 2rem; - height: 100%; - display: flex; - align-items: center; - justify-content: flex-end; - } - #tsd-toolbar-links a { - margin-left: 1.5rem; - } - #tsd-toolbar-links a:hover { - text-decoration: underline; + #tsd-search-status:not(:empty) { + min-height: 6rem; } .tsd-signature { @@ -1256,78 +1307,52 @@ width: 100%; color: var(--color-text); background: var(--color-background-secondary); - border-bottom: 1px var(--color-accent) solid; + border-bottom: var(--dim-toolbar-border-bottom-width) + var(--color-accent) solid; transition: transform 0.3s ease-in-out; } .tsd-page-toolbar a { color: var(--color-text); - text-decoration: none; } - .tsd-page-toolbar a.title { - font-weight: bold; - } - .tsd-page-toolbar a.title:hover { - text-decoration: underline; - } - .tsd-page-toolbar .tsd-toolbar-contents { + .tsd-toolbar-contents { display: flex; - justify-content: space-between; - height: 2.5rem; + align-items: center; + height: var(--dim-toolbar-contents-height); margin: 0 auto; } - .tsd-page-toolbar .table-cell { - position: relative; - white-space: nowrap; - line-height: 40px; + .tsd-toolbar-contents > .title { + font-weight: bold; + margin-right: auto; } - .tsd-page-toolbar .table-cell:first-child { - width: 100%; - } - .tsd-page-toolbar .tsd-toolbar-icon { - box-sizing: border-box; - line-height: 0; - padding: 12px 0; + #tsd-toolbar-links { + display: flex; + align-items: center; + gap: 1.5rem; + margin-right: 1rem; } .tsd-widget { + box-sizing: border-box; display: inline-block; - overflow: hidden; opacity: 0.8; - height: 40px; - transition: - opacity 0.1s, - background-color 0.2s; - vertical-align: bottom; + height: 2.5rem; + width: 2.5rem; + transition: opacity 0.1s, background-color 0.1s; + text-align: center; cursor: pointer; + border: none; + background-color: transparent; } .tsd-widget:hover { opacity: 0.9; } - .tsd-widget.active { + .tsd-widget:active { opacity: 1; background-color: var(--color-accent); } - .tsd-widget.no-caption { - width: 40px; - } - .tsd-widget.no-caption:before { - margin: 0; - } - - .tsd-widget.options, - .tsd-widget.menu { + #tsd-toolbar-menu-trigger { display: none; } - input[type="checkbox"] + .tsd-widget:before { - background-position: -120px 0; - } - input[type="checkbox"]:checked + .tsd-widget:before { - background-position: -160px 0; - } - - img { - max-width: 100%; - } .tsd-member-summary-name { display: inline-flex; @@ -1341,6 +1366,7 @@ align-items: center; margin-left: 0.5rem; color: var(--color-text); + vertical-align: middle; } .tsd-anchor-icon svg { @@ -1350,7 +1376,8 @@ } .tsd-member-summary-name:hover > .tsd-anchor-icon svg, - .tsd-anchor-link:hover > .tsd-anchor-icon svg { + .tsd-anchor-link:hover > .tsd-anchor-icon svg, + .tsd-anchor-icon:focus-visible svg { visibility: visible; } @@ -1436,41 +1463,26 @@ color: var(--color-text); } - * { - scrollbar-width: thin; - scrollbar-color: var(--color-accent) var(--color-icon-background); - } - - *::-webkit-scrollbar { - width: 0.75rem; - } - - *::-webkit-scrollbar-track { - background: var(--color-icon-background); - } - - *::-webkit-scrollbar-thumb { - background-color: var(--color-accent); - border-radius: 999rem; - border: 0.25rem solid var(--color-icon-background); - } - /* mobile */ @media (max-width: 769px) { - .tsd-widget.options, - .tsd-widget.menu { + #tsd-toolbar-menu-trigger { display: inline-block; + /* temporary fix to vertically align, for compatibility */ + line-height: 2.5; + } + #tsd-toolbar-links { + display: none; } .container-main { display: flex; } - html .col-content { + .col-content { float: none; max-width: 100%; width: 100%; } - html .col-sidebar { + .col-sidebar { position: fixed !important; overflow-y: auto; -webkit-overflow-scrolling: touch; @@ -1485,10 +1497,10 @@ background-color: var(--color-background); transform: translate(100%, 0); } - html .col-sidebar > *:last-child { + .col-sidebar > *:last-child { padding-bottom: 20px; } - html .overlay { + .overlay { content: ""; display: block; position: fixed; @@ -1535,9 +1547,6 @@ .has-menu .tsd-navigation { max-height: 100%; } - #tsd-toolbar-links { - display: none; - } .tsd-navigation .tsd-nav-link { display: flex; } @@ -1549,7 +1558,11 @@ display: grid; grid-template-columns: minmax(0, 1fr) minmax(0, 2fr); grid-template-areas: "sidebar content"; - margin: 2rem auto; + --dim-container-main-margin-y: 2rem; + } + + .tsd-breadcrumb { + margin-top: 0; } .col-sidebar { @@ -1562,11 +1575,15 @@ } @media (min-width: 770px) and (max-width: 1399px) { .col-sidebar { - max-height: calc(100vh - 2rem - 42px); + max-height: calc( + 100vh - var(--dim-header-height) - var(--dim-footer-height) - + 2 * var(--dim-container-main-margin-y) + ); overflow: auto; position: sticky; - top: 42px; - padding-top: 1rem; + top: calc( + var(--dim-header-height) + var(--dim-container-main-margin-y) + ); } .site-menu { margin-top: 1rem; @@ -1576,10 +1593,11 @@ /* two sidebars */ @media (min-width: 1200px) { .container-main { - grid-template-columns: minmax(0, 1fr) minmax(0, 2.5fr) minmax( - 0, - 20rem - ); + grid-template-columns: + minmax(0, 1fr) minmax(0, 2.5fr) minmax( + 0, + 20rem + ); grid-template-areas: "sidebar content toc"; } @@ -1596,15 +1614,20 @@ } .site-menu { - margin-top: 1rem; + margin-top: 0rem; } .page-menu, .site-menu { - max-height: calc(100vh - 2rem - 42px); + max-height: calc( + 100vh - var(--dim-header-height) - var(--dim-footer-height) - + 2 * var(--dim-container-main-margin-y) + ); overflow: auto; position: sticky; - top: 42px; + top: calc( + var(--dim-header-height) + var(--dim-container-main-margin-y) + ); } } } diff --git a/typedoc/classes/Env.html b/typedoc/classes/Env.html index 9cb60137..6dd4dd1f 100644 --- a/typedoc/classes/Env.html +++ b/typedoc/classes/Env.html @@ -1,44 +1,44 @@ -Env | @vladmandic/human - v3.3.5

Class Env

Env class that holds detected capabilities

-

Constructors

constructor -

Properties

agent -backends -browser -cpu -filter -initial -kernels -node -offscreen -perfadd -platform -tensorflow -tfjs -wasm -webgl -webgpu -worker -

Accessors

Canvas -Image -ImageData -

Methods

updateBackend -updateCPU -

Constructors

constructor

Properties

agent

agent: string = ''

Detected agent

-

backends

backends: string[] = []

List of supported backends

-

browser

browser: boolean

Running in Browser

-

cpu

cpu: { flags: string[]; model: undefined | string } = ...

CPU info

-

filter

filter: undefined | boolean

Are image filters supported?

-

initial

initial: boolean

Has any work been performed so far

-

kernels

kernels: string[] = []

List of supported kernels for current backend

-

node

node: boolean

Running in NodeJS

-

offscreen

offscreen: undefined | boolean

Is offscreenCanvas supported?

-

perfadd

perfadd: boolean = false

Are performance counter instant values or additive

-

platform

platform: string = ''

Detected platform

-

tensorflow

tensorflow: { gpu: undefined | boolean; version: undefined | string } = ...

If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled

-

tfjs

tfjs: { version: undefined | string }

TFJS instance details

-

wasm

wasm: {
    backend: undefined | boolean;
    multithread: undefined | boolean;
    simd: undefined | boolean;
    supported: undefined | boolean;
} = ...

WASM detected capabilities

-

webgl

webgl: {
    backend: undefined | boolean;
    renderer: undefined | string;
    shader: undefined | string;
    supported: undefined | boolean;
    vendor: undefined | string;
    version: undefined | string;
} = ...

WebGL detected capabilities

-

webgpu

webgpu: {
    adapter: undefined | GPUAdapterInfo;
    backend: undefined | boolean;
    supported: undefined | boolean;
} = ...

WebGPU detected capabilities

-

worker

worker: boolean

Running in WebWorker thread

-

Accessors

Canvas

Image

ImageData

Methods

updateBackend

  • updateBackend(): Promise<void>

  • update backend information

    -

    Returns Promise<void>

updateCPU

Settings

Member Visibility

On This Page

Constructors
Properties
Accessors
Methods
+Env | @vladmandic/human - v3.3.6
@vladmandic/human - v3.3.6
    Preparing search index...

    Class Env

    Env class that holds detected capabilities

    +
    Index

    Constructors

    constructor +

    Properties

    agent +backends +browser +cpu +filter +initial +kernels +node +offscreen +perfadd +platform +tensorflow +tfjs +wasm +webgl +webgpu +worker +

    Accessors

    Canvas +Image +ImageData +

    Methods

    updateBackend +updateCPU +

    Constructors

    Properties

    agent: string = ''

    Detected agent

    +
    backends: string[] = []

    List of supported backends

    +
    browser: boolean

    Running in Browser

    +
    cpu: { flags: string[]; model: undefined | string } = ...

    CPU info

    +
    filter: undefined | boolean

    Are image filters supported?

    +
    initial: boolean

    Has any work been performed so far

    +
    kernels: string[] = []

    List of supported kernels for current backend

    +
    node: boolean

    Running in NodeJS

    +
    offscreen: undefined | boolean

    Is offscreenCanvas supported?

    +
    perfadd: boolean = false

    Are performance counter instant values or additive

    +
    platform: string = ''

    Detected platform

    +
    tensorflow: { gpu: undefined | boolean; version: undefined | string } = ...

    If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled

    +
    tfjs: { version: undefined | string }

    TFJS instance details

    +
    wasm: {
        backend: undefined | boolean;
        multithread: undefined | boolean;
        simd: undefined | boolean;
        supported: undefined | boolean;
    } = ...

    WASM detected capabilities

    +
    webgl: {
        backend: undefined | boolean;
        renderer: undefined | string;
        shader: undefined | string;
        supported: undefined | boolean;
        vendor: undefined | string;
        version: undefined | string;
    } = ...

    WebGL detected capabilities

    +
    webgpu: {
        adapter: undefined | GPUAdapterInfo;
        backend: undefined | boolean;
        supported: undefined | boolean;
    } = ...

    WebGPU detected capabilities

    +
    worker: boolean

    Running in WebWorker thread

    +

    Accessors

    Methods

    • update backend information

      +

      Returns Promise<void>

    Settings

    Member Visibility

    On This Page

    Constructors
    Properties
    Accessors
    Methods
    diff --git a/typedoc/classes/GraphModel.html b/typedoc/classes/GraphModel.html deleted file mode 100644 index a71a6a8d..00000000 --- a/typedoc/classes/GraphModel.html +++ /dev/null @@ -1,128 +0,0 @@ -GraphModel | @vladmandic/human - v3.3.5

    Class GraphModel<ModelURL>

    A tf.GraphModel is a directed, acyclic graph built from a -SavedModel GraphDef and allows inference execution.

    -

    A tf.GraphModel can only be created by loading from a model converted from -a TensorFlow SavedModel using -the command line converter tool and loaded via tf.loadGraphModel.

    -

    Doc

    Type Parameters

    • ModelURL extends Url = string | io.IOHandler

    Implements

    • InferenceModel

    Constructors

    constructor -

    Accessors

    inputNodes -inputs -metadata -modelSignature -modelStructuredOutputKeys -modelVersion -outputNodes -outputs -weights -

    Methods

    dispose -disposeIntermediateTensors -execute -executeAsync -getIntermediateTensors -load -loadSync -predict -predictAsync -save -

    Constructors

    constructor

    • new GraphModel<ModelURL extends Url = string | IOHandler>(
          modelUrl: ModelURL,
          loadOptions?: LoadOptions,
          tfio?: __module,
      ): GraphModel<ModelURL>

    • Type Parameters

      • ModelURL extends Url = string | IOHandler

      Parameters

      • modelUrl: ModelURL

        url for the model, or an io.IOHandler.

        -
      • OptionalloadOptions: LoadOptions
      • Optionaltfio: __module

      Returns GraphModel<ModelURL>

    Accessors

    inputNodes

    • get inputNodes(): string[]

    • Returns string[]

    inputs

    • get inputs(): TensorInfo[]

    • Return the array of input tensor info.

      -

      Returns TensorInfo[]

    metadata

    • get metadata(): {}

    • Returns {}

    modelSignature

    • get modelSignature(): {}

    • Returns {}

    modelStructuredOutputKeys

    • get modelStructuredOutputKeys(): {}

    • Returns {}

    modelVersion

    • get modelVersion(): string

    • Returns string

    outputNodes

    • get outputNodes(): string[]

    • Returns string[]

    outputs

    • get outputs(): TensorInfo[]

    • Return the array of output tensor info.

      -

      Returns TensorInfo[]

    weights

    • get weights(): NamedTensorsMap

    • Returns NamedTensorsMap

    Methods

    dispose

    • dispose(): void

    • Releases the memory used by the weight tensors and resourceManager.

      -

      Returns void

      Doc

    disposeIntermediateTensors

    • disposeIntermediateTensors(): void

    • Dispose intermediate tensors for model debugging mode (flag -KEEP_INTERMEDIATE_TENSORS is true).

      -

      Returns void

      Doc

    execute

    • execute(
          inputs: Tensor | Tensor[] | NamedTensorMap,
          outputs?: string | string[],
      ): Tensor | Tensor[]

    • Executes inference for the model for given input tensors.

      -

      Parameters

      • inputs: Tensor | Tensor[] | NamedTensorMap

        tensor, tensor array or tensor map of the inputs for the -model, keyed by the input node names.

        -
      • Optionaloutputs: string | string[]

        output node name from the TensorFlow model, if no -outputs are specified, the default outputs of the model would be used. -You can inspect intermediate nodes of the model by adding them to the -outputs array.

        -

      Returns Tensor | Tensor[]

      A single tensor if provided with a single output or no outputs -are provided and there is only one default output, otherwise return a -tensor array. The order of the tensor array is the same as the outputs -if provided, otherwise the order of outputNodes attribute of the model.

      -

      Doc

    executeAsync

    • executeAsync(
          inputs: Tensor | Tensor[] | NamedTensorMap,
          outputs?: string | string[],
      ): Promise<Tensor | Tensor[]>

    • Executes inference for the model for given input tensors in async -fashion, use this method when your model contains control flow ops.

      -

      Parameters

      • inputs: Tensor | Tensor[] | NamedTensorMap

        tensor, tensor array or tensor map of the inputs for the -model, keyed by the input node names.

        -
      • Optionaloutputs: string | string[]

        output node name from the TensorFlow model, if no outputs -are specified, the default outputs of the model would be used. You can -inspect intermediate nodes of the model by adding them to the outputs -array.

        -

      Returns Promise<Tensor | Tensor[]>

      A Promise of single tensor if provided with a single output or -no outputs are provided and there is only one default output, otherwise -return a tensor map.

      -

      Doc

    getIntermediateTensors

    • getIntermediateTensors(): NamedTensorsMap

    • Get intermediate tensors for model debugging mode (flag -KEEP_INTERMEDIATE_TENSORS is true).

      -

      Returns NamedTensorsMap

      Doc

    load

    • load(): UrlIOHandler<ModelURL> extends IOHandlerSync
          ? boolean
          : Promise<boolean>

    • Loads the model and weight files, construct the in memory weight map and -compile the inference graph.

      -

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

    loadSync

    • loadSync(artifacts: ModelArtifacts): boolean

    • Synchronously construct the in memory weight map and -compile the inference graph.

      -

      Parameters

      • artifacts: ModelArtifacts

      Returns boolean

      Doc

    predict

    • predict(
          inputs: Tensor | Tensor[] | NamedTensorMap,
          config?: ModelPredictConfig,
      ): Tensor | Tensor[] | NamedTensorMap

    • Execute the inference for the input tensors.

      -

      Parameters

      • inputs: Tensor | Tensor[] | NamedTensorMap
      • Optionalconfig: ModelPredictConfig

        Prediction configuration for specifying the batch size. -Currently the batch size option is ignored for graph model.

        -

      Returns Tensor | Tensor[] | NamedTensorMap

      Inference result tensors. If the model is converted and it -originally had structured_outputs in tensorflow, then a NamedTensorMap -will be returned matching the structured_outputs. If no structured_outputs -are present, the output will be single tf.Tensor if the model has single -output node, otherwise Tensor[].

      -

      See

      GraphModel.inputNodes

      -

      You can also feed any intermediate nodes using the NamedTensorMap as the -input type. For example, given the graph -InputNode => Intermediate => OutputNode, -you can execute the subgraph Intermediate => OutputNode by calling -model.execute('IntermediateNode' : tf.tensor(...));

      -

      This is useful for models that uses tf.dynamic_rnn, where the intermediate -state needs to be fed manually.

      -

      For batch inference execution, the tensors for each input need to be -concatenated together. For example with mobilenet, the required input shape -is [1, 244, 244, 3], which represents the [batch, height, width, channel]. -If we are provide a batched data of 100 images, the input tensor should be -in the shape of [100, 244, 244, 3].

      -

      Doc

    predictAsync

    • predictAsync(
          inputs: Tensor | Tensor[] | NamedTensorMap,
          config?: ModelPredictConfig,
      ): Promise<Tensor | Tensor[] | NamedTensorMap>

    • Execute the inference for the input tensors in async fashion, use this -method when your model contains control flow ops.

      -

      Parameters

      • inputs: Tensor | Tensor[] | NamedTensorMap
      • Optionalconfig: ModelPredictConfig

        Prediction configuration for specifying the batch size. -Currently the batch size option is ignored for graph model.

        -

      Returns Promise<Tensor | Tensor[] | NamedTensorMap>

      A Promise of inference result tensors. If the model is converted -and it originally had structured_outputs in tensorflow, then a -NamedTensorMap will be returned matching the structured_outputs. If no -structured_outputs are present, the output will be single tf.Tensor if -the model has single output node, otherwise Tensor[].

      -

      See

      GraphModel.inputNodes

      -

      You can also feed any intermediate nodes using the NamedTensorMap as the -input type. For example, given the graph -InputNode => Intermediate => OutputNode, -you can execute the subgraph Intermediate => OutputNode by calling -model.execute('IntermediateNode' : tf.tensor(...));

      -

      This is useful for models that uses tf.dynamic_rnn, where the intermediate -state needs to be fed manually.

      -

      For batch inference execution, the tensors for each input need to be -concatenated together. For example with mobilenet, the required input shape -is [1, 244, 244, 3], which represents the [batch, height, width, channel]. -If we are provide a batched data of 100 images, the input tensor should be -in the shape of [100, 244, 244, 3].

      -

      Doc

    save

    • save(handlerOrURL: string | IOHandler, config?: SaveConfig): Promise<SaveResult>

    • Save the configuration and/or weights of the GraphModel.

      -

      An IOHandler is an object that has a save method of the proper -signature defined. The save method manages the storing or -transmission of serialized data ("artifacts") that represent the -model's topology and weights onto or via a specific medium, such as -file downloads, local storage, IndexedDB in the web browser and HTTP -requests to a server. TensorFlow.js provides IOHandler -implementations for a number of frequently used saving mediums, such as -tf.io.browserDownloads and tf.io.browserLocalStorage. See tf.io -for more details.

      -

      This method also allows you to refer to certain types of IOHandlers -as URL-like string shortcuts, such as 'localstorage://' and -'indexeddb://'.

      -

      Example 1: Save model's topology and weights to browser local -storage; -then load it back.

      -
      const modelUrl =
         'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
      const model = await tf.loadGraphModel(modelUrl);
      const zeros = tf.zeros([1, 224, 224, 3]);
      model.predict(zeros).print();

      const saveResults = await model.save('localstorage://my-model-1');

      const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');
      console.log('Prediction from loaded model:');
      model.predict(zeros).print();
-
      - -

      Parameters

      • handlerOrURL: string | IOHandler

        An instance of IOHandler or a URL-like, -scheme-based string shortcut for IOHandler.

        -
      • Optionalconfig: SaveConfig

        Options for saving the model.

        -

      Returns Promise<SaveResult>

      A Promise of SaveResult, which summarizes the result of -the saving, such as byte sizes of the saved artifacts for the model's -topology and weight values.

      -

      Doc

    Settings

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    diff --git a/typedoc/classes/Human.html b/typedoc/classes/Human.html index bc55744f..df8016dd 100644 --- a/typedoc/classes/Human.html +++ b/typedoc/classes/Human.html @@ -1,57 +1,57 @@ -Human | @vladmandic/human - v3.3.5

    Class Human

    Human* library main class

    +Human | @vladmandic/human - v3.3.6
    @vladmandic/human - v3.3.6
      Preparing search index...

      Class Human

      Human* library main class

      All methods and properties are available only as members of Human class

      • Configuration object definition: Config
      • Results object definition: Result
      • Possible inputs: Input
      -

      Param: userConfig

      Config

      -

      Returns

      instance of Human

      -

      Constructors

      constructor -

      Properties

      config -draw -env -events -faceTriangulation -faceUVMap -match -performance -process -result -state -tf -version -webcam -

      Methods

      analyze -compare -detect -emit -image -init -load -next -now -profile -reset -segmentation -sleep -validate -video -warmup -

      Constructors

      constructor

      • new Human(userConfig?: Partial<Config>): Human

      • Constructor for Human library that is futher used for all operations

        +

        Config

        +

        instance of Human

        +
      Index

      Constructors

      constructor +

      Properties

      config +draw +env +events +faceTriangulation +faceUVMap +match +performance +process +result +state +tf +version +webcam +

      Methods

      analyze +compare +detect +emit +image +init +load +next +now +profile +reset +segmentation +sleep +validate +video +warmup +

      Constructors

      • Constructor for Human library that is futher used for all operations

        Parameters

        • OptionaluserConfig: Partial<Config>

          user configuration object Config

          -

        Returns Human

      Properties

      config

      config: Config

      Current configuration

      +

      Returns Human

      Properties

      config: Config

      Current configuration

      -

      draw

      draw: draw = draw

      Draw helper classes that can draw detected objects on canvas using specified draw

      +
      draw: draw = draw

      Draw helper classes that can draw detected objects on canvas using specified draw

      • canvas: draws input to canvas
      • options: are global settings for all draw operations, can be overriden for each draw method DrawOptions
      • face, body, hand, gesture, object, person: draws detected results as overlays on canvas
      -

      env

      env: Env = env

      Object containing environment information used for diagnostics

      -

      events

      events: undefined | EventTarget

      Container for events dispatched by Human +

      env: Env = env

      Object containing environment information used for diagnostics

      +
      events: undefined | EventTarget

      Container for events dispatched by Human Possible events:

      • create: triggered when Human object is instantiated
        • @@ -61,34 +61,34 @@ Possible events:

      • warmup: triggered when warmup is complete
      • error: triggered on some errors
      -

      faceTriangulation

      faceTriangulation: number[]

      Reference face triangualtion array of 468 points, used for triangle references between points

      -

      faceUVMap

      faceUVMap: [number, number][]

      Refernce UV map of 468 values, used for 3D mapping of the face mesh

      -

      match

      match: match = match

      Face Matching

      +
      faceTriangulation: number[]

      Reference face triangualtion array of 468 points, used for triangle references between points

      +
      faceUVMap: [number, number][]

      Refernce UV map of 468 values, used for 3D mapping of the face mesh

      +
      match: match = match

      Face Matching

      • similarity: compare two face descriptors and return similarity index
      • distance: compare two face descriptors and return raw calculated differences
      • find: compare face descriptor to array of face descriptors and return best match
      -

      performance

      performance: Record<string, number>

      Performance object that contains values for all recently performed operations

      -

      process

      process: { canvas: null | AnyCanvas; tensor: null | Tensor }

      currenty processed image tensor and canvas

      -

      result

      result: Result

      Last known result of detect run

      +
      performance: Record<string, number>

      Performance object that contains values for all recently performed operations

      +
      process: { canvas: null | AnyCanvas; tensor: null | Tensor<Rank> }

      currenty processed image tensor and canvas

      +
      result: Result

      Last known result of detect run

      • Can be accessed anytime after initial detection
      -

      state

      state: string

      Current state of Human library

      +
      state: string

      Current state of Human library

      • Can be polled to determine operations that are currently executed
      • Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'
      -

      tf

      tf: any

      Instance of TensorFlow/JS used by Human

      +
      tf: any

      Instance of TensorFlow/JS used by Human

      • Can be embedded or externally provided TFJS API
      -

      version

      version: string

      Current version of Human library in semver format

      -

      webcam

      webcam: WebCam = ...

      WebCam helper methods

      -

      Methods

      analyze

      • analyze(...msg: string[]): void

      • internal function to measure tensor leaks

        -

        Parameters

        • ...msg: string[]

        Returns void

      compare

      • compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number>

      • Compare two input tensors for pixel similarity

        +
      version: string

      Current version of Human library in semver format

      +
      webcam: WebCam = ...

      WebCam helper methods

      +

      Methods

      • internal function to measure tensor leaks

        +

        Parameters

        • ...msg: string[]

        Returns void

      • Compare two input tensors for pixel similarity

        • use human.image to process any valid input and get a tensor that can be used for compare
        • when passing manually generated tensors:
          • @@ -96,7 +96,7 @@ Possible events:

        • if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor
        • return value is pixel similarity score normalized by input resolution and rgb channels
        -

        Parameters

        Returns Promise<number>

      detect

      emit

      • emit(event: string): void

      • emit event

        -

        Parameters

        • event: string

        Returns void

      image

      • image(
            input: Input,
            getTensor?: boolean,
        ): Promise<{ canvas: null | AnyCanvas; tensor: null | Tensor4D }>

      • Process input as return canvas and tensor

        +

      • emit event

        +

        Parameters

        • event: string

        Returns void

      • Process input as return canvas and tensor

        Parameters

        • input: Input

          any input Input

        • getTensor: boolean = false

          should image processing also return tensor or just canvas Returns object with tensor and canvas

          -

        Returns Promise<{ canvas: null | AnyCanvas; tensor: null | Tensor4D }>

      init

      • init(): Promise<void>

      • Explicit backend initialization

        +

      Returns Promise<{ canvas: null | AnyCanvas; tensor: null | Tensor4D }>

      • Explicit backend initialization

        • Normally done implicitly during initial load phase
        • Call to explictly register and initialize TFJS backend without any other operations
        • Use when changing backend during runtime
        -

        Returns Promise<void>

      load

      • load(userConfig?: Partial<Config>): Promise<void>

      • Load method preloads all configured models on-demand

        +

        Returns Promise<void>

      • Load method preloads all configured models on-demand

        • Not explicitly required as any required model is load implicitly on it's first run

        Parameters

        Returns Promise<void>

      next

      • next(result?: Result): Result

      • Runs interpolation using last known result and returns smoothened result +

      Returns Promise<void>

      • Runs interpolation using last known result and returns smoothened result Interpolation is based on time since last known result so can be called independently

        Parameters

        • result: Result = ...

          Result optional use specific result set to run interpolation on

        Returns Result

        result - Result

        -

      now

      • now(): number

      • Utility wrapper for performance.now()

        -

        Returns number

      profile

      • profile(
            input: Input,
            userConfig?: Partial<Config>,
        ): Promise<{ kernel: string; perc: number; time: number }[]>

      • Run detect with tensorflow profiling

        +

      • Utility wrapper for performance.now()

        +

        Returns number

      • Run detect with tensorflow profiling

        • result object will contain total exeuction time information for top-20 kernels
        • actual detection object can be accessed via human.result
        -

        Parameters

        Returns Promise<{ kernel: string; perc: number; time: number }[]>

      reset

      • reset(): void

      • Reset configuration to default values

        -

        Returns void

      segmentation

      • segmentation(input: Input, userConfig?: Partial<Config>): Promise<null | Tensor>

      • Segmentation method takes any input and returns RGBA tensor +

        Parameters

        Returns Promise<{ kernel: string; perc: number; time: number }[]>

      • Reset configuration to default values

        +

        Returns void

      • Segmentation method takes any input and returns RGBA tensor Note: Segmentation is not triggered as part of detect process

        Parameters

        • input: Input

          Input Returns tensor which contains image data in RGBA format

          -
        • OptionaluserConfig: Partial<Config>

        Returns Promise<null | Tensor>

      sleep

      • sleep(ms: number): Promise<void>

      • Helper function

        +
      • OptionaluserConfig: Partial<Config>

      Returns Promise<null | Tensor<Rank>>

      • Helper function

        Parameters

        • ms: number

          sleep time in miliseconds

          -

        Returns Promise<void>

      validate

      • validate(
            userConfig?: Partial<Config>,
        ): { expected?: string; reason: string; where: string }[]

      • Validate current configuration schema

        -

        Parameters

        • OptionaluserConfig: Partial<Config>

        Returns { expected?: string; reason: string; where: string }[]

      video

      • video(element: HTMLVideoElement, run?: boolean, delay?: number): Promise<void>

      • Continously detect video frames

        +

      Returns Promise<void>

      • Validate current configuration schema

        +

        Parameters

        • OptionaluserConfig: Partial<Config>

        Returns { expected?: string; reason: string; where: string }[]

      • Continously detect video frames

        Parameters

        • element: HTMLVideoElement

          HTMLVideoElement input

        • run: boolean = true

          boolean run continously or stop if already running, default true

        • delay: number = 0

          number delay detection between frames for number of miliseconds, default 0

          -

        Returns Promise<void>

      warmup

      • warmup(userConfig?: Partial<Config>): Promise<undefined | Result>

      • Warmup method pre-initializes all configured models for faster inference

        +

      Returns Promise<void>

      • Warmup method pre-initializes all configured models for faster inference

        • can take significant time on startup
        • only used for webgl and humangl backends

        Parameters

        Returns Promise<undefined | Result>

        result - Result

        -

      Settings

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      On This Page

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      Properties
      Methods
      +

      Settings

      Member Visibility

      On This Page

      Constructors
      Properties
      Methods
      diff --git a/typedoc/classes/Tensor-1.html b/typedoc/classes/Tensor-1.html deleted file mode 100644 index f05c981e..00000000 --- a/typedoc/classes/Tensor-1.html +++ /dev/null @@ -1,225 +0,0 @@ -Tensor | @vladmandic/human - v3.3.5

      Class Tensor<R>

      A tf.Tensor object represents an immutable, multidimensional array of -numbers that has a shape and a data type.

      -

      For performance reasons, functions that create tensors do not necessarily -perform a copy of the data passed to them (e.g. if the data is passed as a -Float32Array), and changes to the data will change the tensor. This is not -a feature and is not supported. To avoid this behavior, use the tensor before -changing the input data or create a copy with copy = tf.add(yourTensor, 0).

      -

      See tf.tensor for details on how to create a tf.Tensor.

      -

      Doc

      Type Parameters

      Implements

      • TensorInfo

      Constructors

      constructor -

      Properties

      dataId -dtype -id -kept -kerasMask? -rankType -scopeId -shape -size -strides -

      Accessors

      isDisposed -rank -

      Methods

      abs -acos -acosh -add -all -any -argMax -argMin -array -arraySync -as1D -as2D -as3D -as4D -as5D -asin -asinh -asScalar -asType -atan -atan2 -atanh -avgPool -batchNorm -batchToSpaceND -broadcastTo -buffer -bufferSync -bytes -cast -ceil -clipByValue -clone -concat -conv1d -conv2d -conv2dTranspose -cos -cosh -cumprod -cumsum -data -dataSync -dataToGPU -depthToSpace -depthwiseConv2d -dilation2d -dispose -div -divNoNan -dot -elu -equal -erf -euclideanNorm -exp -expandDims -expm1 -fft -flatten -floor -floorDiv -gather -greater -greaterEqual -ifft -irfft -isFinite -isInf -isNaN -leakyRelu -less -lessEqual -localResponseNormalization -log -log1p -logicalAnd -logicalNot -logicalOr -logicalXor -logSigmoid -logSoftmax -logSumExp -matMul -max -maximum -maxPool -mean -min -minimum -mirrorPad -mod -mul -neg -norm -notEqual -oneHot -onesLike -pad -pool -pow -prelu -print -prod -reciprocal -relu -relu6 -reshape -reshapeAs -resizeBilinear -resizeNearestNeighbor -reverse -rfft -round -rsqrt -selu -separableConv2d -sigmoid -sign -sin -sinh -slice -softmax -softplus -spaceToBatchND -split -sqrt -square -squaredDifference -squeeze -stack -step -stridedSlice -sub -sum -tan -tanh -throwIfDisposed -tile -toBool -toFloat -toInt -topk -toString -transpose -unique -unsortedSegmentSum -unstack -variable -where -zerosLike -

      Constructors

      constructor

      • new Tensor<R extends Rank = Rank>(
            shape: ShapeMap[R],
            dtype: keyof DataTypeMap,
            dataId: object,
            id: number,
        ): Tensor<R>

      • Type Parameters

        Parameters

        • shape: ShapeMap[R]
        • dtype: keyof DataTypeMap
        • dataId: object
        • id: number

        Returns Tensor<R>

      Properties

      dataId

      dataId: object

      Id of the bucket holding the data for this tensor. Multiple arrays can -point to the same bucket (e.g. when calling array.reshape()).

      -

      Readonlydtype

      dtype: keyof DataTypeMap

      The data type for the array.

      -

      Readonlyid

      id: number

      Unique id of this tensor.

      -

      kept

      kept: boolean

      Whether this tensor has been globally kept.

      -

      OptionalkerasMask

      kerasMask?: Tensor

      The keras mask that some keras layers attach to the tensor

      -

      ReadonlyrankType

      rankType: R

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

      -

      scopeId

      scopeId: number

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

      -

      Readonlyshape

      shape: ShapeMap[R]

      The shape of the tensor.

      -

      Readonlysize

      size: number

      Number of elements in the tensor.

      -

      Readonlystrides

      strides: number[]

      Number of elements to skip in each dimension when indexing. See -https://docs.scipy.org/doc/numpy/reference/generated/\ -numpy.ndarray.strides.html

      -

      Accessors

      isDisposed

      • get isDisposed(): boolean

      • Returns boolean

      rank

      • get rank(): number

      • Returns number

      Methods

      abs

      • abs<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      acos

      • acos<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      acosh

      • acosh<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      add

      • add<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      all

      • all<T extends Tensor>(this: T, axis?: number | number[], keepDims?: boolean): T

      • Type Parameters

        Parameters

        • this: T
        • Optionalaxis: number | number[]
        • OptionalkeepDims: boolean

        Returns T

      any

      • any<T extends Tensor>(this: T, axis?: number | number[], keepDims?: boolean): T

      • Type Parameters

        Parameters

        • this: T
        • Optionalaxis: number | number[]
        • OptionalkeepDims: boolean

        Returns T

      argMax

      • argMax<T extends Tensor>(axis?: number): T

      • Type Parameters

        Parameters

        • Optionalaxis: number

        Returns T

      argMin

      • argMin<T extends Tensor>(axis?: number): T

      • Type Parameters

        Parameters

        • Optionalaxis: number

        Returns T

      array

      • array(): Promise<ArrayMap[R]>

      • Returns the tensor data as a nested array. The transfer of data is done -asynchronously.

        -

        Returns Promise<ArrayMap[R]>

        Doc

      arraySync

      • arraySync(): ArrayMap[R]

      • Returns the tensor data as a nested array. The transfer of data is done -synchronously.

        -

        Returns ArrayMap[R]

        Doc

      as1D

      • as1D<T extends Tensor>(): Tensor1D

      • Type Parameters

        Returns Tensor1D

      as2D

      • as2D<T extends Tensor>(rows: number, columns: number): Tensor2D

      • Type Parameters

        Parameters

        • rows: number
        • columns: number

        Returns Tensor2D

      as3D

      • as3D<T extends Tensor>(rows: number, columns: number, depth: number): Tensor3D

      • Type Parameters

        Parameters

        • rows: number
        • columns: number
        • depth: number

        Returns Tensor3D

      as4D

      • as4D<T extends Tensor>(
            rows: number,
            columns: number,
            depth: number,
            depth2: number,
        ): Tensor4D

      • Type Parameters

        Parameters

        • rows: number
        • columns: number
        • depth: number
        • depth2: number

        Returns Tensor4D

      as5D

      • as5D<T extends Tensor>(
            rows: number,
            columns: number,
            depth: number,
            depth2: number,
            depth3: number,
        ): Tensor5D

      • Type Parameters

        Parameters

        • rows: number
        • columns: number
        • depth: number
        • depth2: number
        • depth3: number

        Returns Tensor5D

      asin

      • asin<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      asinh

      • asinh<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      asScalar

      • asScalar<T extends Tensor>(): Scalar

      • Type Parameters

        Returns Scalar

      asType

      • asType<T extends Tensor>(this: T, dtype: keyof DataTypeMap): T

      • Type Parameters

        Parameters

        • this: T
        • dtype: keyof DataTypeMap

        Returns T

      atan

      • atan<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      atan2

      • atan2<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      atanh

      • atanh<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      avgPool

      • avgPool<T extends Tensor3D | Tensor4D>(
            filterSize: number | [number, number],
            strides: number | [number, number],
            pad: number | "valid" | "same" | ExplicitPadding,
            dimRoundingMode?: "floor" | "round" | "ceil",
        ): T

      • Type Parameters

        Parameters

        • filterSize: number | [number, number]
        • strides: number | [number, number]
        • pad: number | "valid" | "same" | ExplicitPadding
        • OptionaldimRoundingMode: "floor" | "round" | "ceil"

        Returns T

      batchNorm

      batchToSpaceND

      • batchToSpaceND<R extends Rank>(
            blockShape: number[],
            crops: number[][],
        ): Tensor<R>

      • Type Parameters

        Parameters

        • blockShape: number[]
        • crops: number[][]

        Returns Tensor<R>

      broadcastTo

      • broadcastTo<R extends Rank>(shape: ShapeMap[R]): Tensor<R>

      • Type Parameters

        Parameters

        • shape: ShapeMap[R]

        Returns Tensor<R>

      buffer

      • buffer<D extends keyof DataTypeMap = "float32">(): Promise<TensorBuffer<R, D>>

      • Returns a promise of tf.TensorBuffer that holds the underlying data.

        -

        Type Parameters

        • D extends keyof DataTypeMap = "float32"

        Returns Promise<TensorBuffer<R, D>>

        Doc

      bufferSync

      • bufferSync<D extends keyof DataTypeMap = "float32">(): TensorBuffer<R, D>

      • Returns a tf.TensorBuffer that holds the underlying data.

        -

        Type Parameters

        • D extends keyof DataTypeMap = "float32"

        Returns TensorBuffer<R, D>

        Doc

      bytes

      • bytes(): Promise<Uint8Array | Uint8Array[]>

      • Returns the underlying bytes of the tensor's data.

        -

        Returns Promise<Uint8Array | Uint8Array[]>

      cast

      • cast<T extends Tensor>(dtype: keyof DataTypeMap): T

      • Type Parameters

        Parameters

        • dtype: keyof DataTypeMap

        Returns T

      ceil

      • ceil<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      clipByValue

      • clipByValue<T extends Tensor>(min: number, max: number): Tensor

      • Type Parameters

        Parameters

        • min: number
        • max: number

        Returns Tensor

      clone

      • clone<T extends Tensor>(this: T): T

      • Returns a copy of the tensor. See tf.clone for details.

        -

        Type Parameters

        Parameters

        • this: T

        Returns T

        Doc

      concat

      • concat<T extends Tensor>(tensors: T | (TensorLike | T)[], axis?: number): T

      • Type Parameters

        Parameters

        Returns T

      conv1d

      • conv1d<T extends Tensor2D | Tensor3D>(
            filter: Tensor3D | TensorLike3D,
            stride: number,
            pad: number | "valid" | "same" | ExplicitPadding,
            dataFormat?: "NWC" | "NCW",
            dilation?: number,
            dimRoundingMode?: "floor" | "round" | "ceil",
        ): T

      • Type Parameters

        Parameters

        • filter: Tensor3D | TensorLike3D
        • stride: number
        • pad: number | "valid" | "same" | ExplicitPadding
        • OptionaldataFormat: "NWC" | "NCW"
        • Optionaldilation: number
        • OptionaldimRoundingMode: "floor" | "round" | "ceil"

        Returns T

      conv2d

      • conv2d<T extends Tensor3D | Tensor4D>(
            filter: Tensor4D | TensorLike4D,
            strides: number | [number, number],
            pad: number | "valid" | "same",
            dataFormat?: "NHWC" | "NCHW",
            dilations?: number | [number, number],
            dimRoundingMode?: "floor" | "round" | "ceil",
        ): T

      • Type Parameters

        Parameters

        • filter: Tensor4D | TensorLike4D
        • strides: number | [number, number]
        • pad: number | "valid" | "same"
        • OptionaldataFormat: "NHWC" | "NCHW"
        • Optionaldilations: number | [number, number]
        • OptionaldimRoundingMode: "floor" | "round" | "ceil"

        Returns T

      conv2dTranspose

      • conv2dTranspose<T extends Tensor3D | Tensor4D>(
            filter: Tensor4D | TensorLike4D,
            outputShape: [number, number, number] | [number, number, number, number],
            strides: number | [number, number],
            pad: number | "valid" | "same",
            dimRoundingMode?: "floor" | "round" | "ceil",
        ): T

      • Type Parameters

        Parameters

        • filter: Tensor4D | TensorLike4D
        • outputShape: [number, number, number] | [number, number, number, number]
        • strides: number | [number, number]
        • pad: number | "valid" | "same"
        • OptionaldimRoundingMode: "floor" | "round" | "ceil"

        Returns T

      cos

      • cos<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      cosh

      • cosh<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      cumprod

      • cumprod<R extends Rank>(
            axis?: number,
            exclusive?: boolean,
            reverse?: boolean,
        ): Tensor<R>

      • Type Parameters

        Parameters

        • Optionalaxis: number
        • Optionalexclusive: boolean
        • Optionalreverse: boolean

        Returns Tensor<R>

      cumsum

      • cumsum<R extends Rank>(
            axis?: number,
            exclusive?: boolean,
            reverse?: boolean,
        ): Tensor<R>

      • Type Parameters

        Parameters

        • Optionalaxis: number
        • Optionalexclusive: boolean
        • Optionalreverse: boolean

        Returns Tensor<R>

      data

      • data<D extends keyof DataTypeMap = NumericDataType>(): Promise<DataTypeMap[D]>

      • Asynchronously downloads the values from the tf.Tensor. Returns a -promise of TypedArray that resolves when the computation has finished.

        -

        Type Parameters

        • D extends keyof DataTypeMap = NumericDataType

        Returns Promise<DataTypeMap[D]>

        Doc

      dataSync

      • dataSync<D extends keyof DataTypeMap = NumericDataType>(): DataTypeMap[D]

      • Synchronously downloads the values from the tf.Tensor. This blocks the -UI thread until the values are ready, which can cause performance issues.

        -

        Type Parameters

        • D extends keyof DataTypeMap = NumericDataType

        Returns DataTypeMap[D]

        Doc

      dataToGPU

      • dataToGPU(options?: DataToGPUWebGLOption): GPUData

      • Copy the tensor's data to a new GPU resource. Comparing to the dataSync() -and data(), this method prevents data from being downloaded to CPU.

        -

        For WebGL backend, the data will be stored on a densely packed texture. -This means that the texture will use the RGBA channels to store value.

        -

        For WebGPU backend, the data will be stored on a buffer. There is no -parameter, so can not use a user-defined size to create the buffer.

        -

        Parameters

        • Optionaloptions: DataToGPUWebGLOption

        Returns GPUData

        For WebGL backend, a GPUData contains the new texture and -its information. -{ -tensorRef: The tensor that is associated with this texture, -texture: WebGLTexture, -texShape: [number, number] // [height, width] -}

        -
        For WebGPU backend, a GPUData contains the new buffer.
-{
-   tensorRef: The tensor that is associated with this buffer,
-   buffer: GPUBuffer,
-}
-
-Remember to dispose the GPUData after it is used by
-`res.tensorRef.dispose()`.
-
        -

        Doc

      depthToSpace

      • depthToSpace<T extends Tensor4D>(
            blockSize: number,
            dataFormat: "NHWC" | "NCHW",
        ): T

      • Type Parameters

        Parameters

        • blockSize: number
        • dataFormat: "NHWC" | "NCHW"

        Returns T

      depthwiseConv2d

      • depthwiseConv2d<T extends Tensor3D | Tensor4D>(
            filter: Tensor4D | TensorLike4D,
            strides: number | [number, number],
            pad: number | "valid" | "same",
            dataFormat?: "NHWC" | "NCHW",
            dilations?: number | [number, number],
            dimRoundingMode?: "floor" | "round" | "ceil",
        ): T

      • Type Parameters

        Parameters

        • filter: Tensor4D | TensorLike4D
        • strides: number | [number, number]
        • pad: number | "valid" | "same"
        • OptionaldataFormat: "NHWC" | "NCHW"
        • Optionaldilations: number | [number, number]
        • OptionaldimRoundingMode: "floor" | "round" | "ceil"

        Returns T

      dilation2d

      • dilation2d<T extends Tensor3D | Tensor4D>(
            filter: Tensor3D | TensorLike3D,
            strides: number | [number, number],
            pad: "valid" | "same",
            dilations?: number | [number, number],
            dataFormat?: "NHWC",
        ): T

      • Type Parameters

        Parameters

        • filter: Tensor3D | TensorLike3D
        • strides: number | [number, number]
        • pad: "valid" | "same"
        • Optionaldilations: number | [number, number]
        • OptionaldataFormat: "NHWC"

        Returns T

      dispose

      • dispose(): void

      • Disposes tf.Tensor from memory.

        -

        Returns void

        Doc

      div

      • div<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      divNoNan

      • divNoNan<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      dot

      elu

      • elu<T extends Tensor>(): T

      • Type Parameters

        Returns T

      equal

      • equal<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      erf

      • erf<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      euclideanNorm

      • euclideanNorm<T extends Tensor>(
            this: T,
            axis?: number | number[],
            keepDims?: boolean,
        ): T

      • Type Parameters

        Parameters

        • this: T
        • Optionalaxis: number | number[]
        • OptionalkeepDims: boolean

        Returns T

      exp

      • exp<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      expandDims

      • expandDims<T extends Tensor>(axis?: number): T

      • Type Parameters

        Parameters

        • Optionalaxis: number

        Returns T

      expm1

      • expm1<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      fft

      • fft<T extends Tensor>(this: Tensor): Tensor

      • Type Parameters

        Parameters

        Returns Tensor

      flatten

      • flatten<T extends Tensor>(): Tensor1D

      • Type Parameters

        Returns Tensor1D

      floor

      • floor<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      floorDiv

      • floorDiv<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      gather

      • gather<T extends Tensor>(
            this: T,
            indices: Tensor | TensorLike,
            axis?: number,
            batchDims?: number,
        ): T

      • Type Parameters

        Parameters

        Returns T

      greater

      • greater<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      greaterEqual

      • greaterEqual<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      ifft

      • ifft<T extends Tensor>(this: Tensor): Tensor

      • Type Parameters

        Parameters

        Returns Tensor

      irfft

      • irfft<T extends Tensor>(this: Tensor): Tensor

      • Type Parameters

        Parameters

        Returns Tensor

      isFinite

      • isFinite<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      isInf

      • isInf<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      isNaN

      • isNaN<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      leakyRelu

      • leakyRelu<T extends Tensor>(alpha: number): T

      • Type Parameters

        Parameters

        • alpha: number

        Returns T

      less

      • less<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      lessEqual

      • lessEqual<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      localResponseNormalization

      • localResponseNormalization<T extends Tensor>(
            depthRadius?: number,
            bias?: number,
            alpha?: number,
            beta?: number,
        ): T

      • Type Parameters

        Parameters

        • OptionaldepthRadius: number
        • Optionalbias: number
        • Optionalalpha: number
        • Optionalbeta: number

        Returns T

      log

      • log<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      log1p

      • log1p<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      logicalAnd

      • logicalAnd<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      logicalNot

      • logicalNot<T extends Tensor>(): T

      • Type Parameters

        Returns T

      logicalOr

      • logicalOr<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      logicalXor

      • logicalXor<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      logSigmoid

      • logSigmoid<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      logSoftmax

      • logSoftmax<T extends Tensor>(this: T, axis?: number): T

      • Type Parameters

        Parameters

        • this: T
        • Optionalaxis: number

        Returns T

      logSumExp

      • logSumExp<T extends Tensor>(
            this: T,
            axis?: number | number[],
            keepDims?: boolean,
        ): T

      • Type Parameters

        Parameters

        • this: T
        • Optionalaxis: number | number[]
        • OptionalkeepDims: boolean

        Returns T

      matMul

      • matMul<T extends Tensor>(
            b: Tensor | TensorLike,
            transposeA?: boolean,
            transposeB?: boolean,
        ): Tensor

      • Type Parameters

        Parameters

        Returns Tensor

      max

      • max<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T

      • Type Parameters

        Parameters

        • Optionalaxis: number | number[]
        • OptionalkeepDims: boolean

        Returns T

      maximum

      • maximum<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      maxPool

      • maxPool<T extends Tensor3D | Tensor4D>(
            filterSize: number | [number, number],
            strides: number | [number, number],
            pad: number | "valid" | "same" | ExplicitPadding,
            dimRoundingMode?: "floor" | "round" | "ceil",
        ): T

      • Type Parameters

        Parameters

        • filterSize: number | [number, number]
        • strides: number | [number, number]
        • pad: number | "valid" | "same" | ExplicitPadding
        • OptionaldimRoundingMode: "floor" | "round" | "ceil"

        Returns T

      mean

      • mean<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T

      • Type Parameters

        Parameters

        • Optionalaxis: number | number[]
        • OptionalkeepDims: boolean

        Returns T

      min

      • min<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T

      • Type Parameters

        Parameters

        • Optionalaxis: number | number[]
        • OptionalkeepDims: boolean

        Returns T

      minimum

      • minimum<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      mirrorPad

      • mirrorPad<T extends Tensor>(
            paddings: [number, number][],
            mode: "reflect" | "symmetric",
        ): T

      • Type Parameters

        Parameters

        • paddings: [number, number][]
        • mode: "reflect" | "symmetric"

        Returns T

      mod

      • mod<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      mul

      • mul<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      neg

      • neg<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      norm

      • norm<T extends Tensor>(
            ord?: number | "euclidean" | "fro",
            axis?: number | number[],
            keepDims?: boolean,
        ): Tensor

      • Type Parameters

        Parameters

        • Optionalord: number | "euclidean" | "fro"
        • Optionalaxis: number | number[]
        • OptionalkeepDims: boolean

        Returns Tensor

      notEqual

      • notEqual<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      oneHot

      • oneHot(depth: number, onValue: number, offValue: number): Tensor

      • Parameters

        • depth: number
        • onValue: number
        • offValue: number

        Returns Tensor

      onesLike

      • onesLike<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      pad

      • pad<T extends Tensor>(paddings: [number, number][], constantValue?: number): T

      • Type Parameters

        Parameters

        • paddings: [number, number][]
        • OptionalconstantValue: number

        Returns T

      pool

      • pool<T extends Tensor3D | Tensor4D>(
            windowShape: number | [number, number],
            poolingType: "avg" | "max",
            padding: number | "valid" | "same" | ExplicitPadding,
            diationRate?: number | [number, number],
            strides?: number | [number, number],
            dimRoundingMode?: "floor" | "round" | "ceil",
        ): T

      • Type Parameters

        Parameters

        • windowShape: number | [number, number]
        • poolingType: "avg" | "max"
        • padding: number | "valid" | "same" | ExplicitPadding
        • OptionaldiationRate: number | [number, number]
        • Optionalstrides: number | [number, number]
        • OptionaldimRoundingMode: "floor" | "round" | "ceil"

        Returns T

      pow

      • pow<T extends Tensor>(exp: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      prelu

      • prelu<T extends Tensor>(alpha: TensorLike | T): T

      • Type Parameters

        Parameters

        Returns T

      print

      • print(verbose?: boolean): void

      • Prints the tf.Tensor. See tf.print for details.

        -

        Parameters

        • Optionalverbose: boolean

          Whether to print verbose information about the tensor, -including dtype and size.

          -

        Returns void

        Doc

      prod

      • prod<T extends Tensor>(this: T, axis?: number | number[], keepDims?: boolean): T

      • Type Parameters

        Parameters

        • this: T
        • Optionalaxis: number | number[]
        • OptionalkeepDims: boolean

        Returns T

      reciprocal

      • reciprocal<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      relu

      • relu<T extends Tensor>(): T

      • Type Parameters

        Returns T

      relu6

      • relu6<T extends Tensor>(): T

      • Type Parameters

        Returns T

      reshape

      • reshape<T extends Tensor>(shape: number[]): T

      • Type Parameters

        Parameters

        • shape: number[]

        Returns T

      reshapeAs

      • reshapeAs<T extends Tensor>(x: T): T

      • Type Parameters

        Parameters

        Returns T

      resizeBilinear

      • resizeBilinear<T extends Tensor3D | Tensor4D>(
            newShape2D: [number, number],
            alignCorners?: boolean,
            halfPixelCenters?: boolean,
        ): T

      • Type Parameters

        Parameters

        • newShape2D: [number, number]
        • OptionalalignCorners: boolean
        • OptionalhalfPixelCenters: boolean

        Returns T

      resizeNearestNeighbor

      • resizeNearestNeighbor<T extends Tensor3D | Tensor4D>(
            newShape2D: [number, number],
            alignCorners?: boolean,
            halfFloatCenters?: boolean,
        ): T

      • Type Parameters

        Parameters

        • newShape2D: [number, number]
        • OptionalalignCorners: boolean
        • OptionalhalfFloatCenters: boolean

        Returns T

      reverse

      • reverse<T extends Tensor>(this: T, axis?: number | number[]): T

      • Type Parameters

        Parameters

        • this: T
        • Optionalaxis: number | number[]

        Returns T

      rfft

      • rfft<T extends Tensor>(this: Tensor): Tensor

      • Type Parameters

        Parameters

        Returns Tensor

      round

      • round<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      rsqrt

      • rsqrt<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      selu

      • selu<T extends Tensor>(): T

      • Type Parameters

        Returns T

      separableConv2d

      • separableConv2d<T extends Tensor3D | Tensor4D>(
            depthwiseFilter: Tensor4D | TensorLike4D,
            pointwiseFilter: TensorLike | Tensor4D,
            strides: number | [number, number],
            pad: "valid" | "same",
            dilation?: number | [number, number],
            dataFormat?: "NHWC" | "NCHW",
        ): T

      • Type Parameters

        Parameters

        • depthwiseFilter: Tensor4D | TensorLike4D
        • pointwiseFilter: TensorLike | Tensor4D
        • strides: number | [number, number]
        • pad: "valid" | "same"
        • Optionaldilation: number | [number, number]
        • OptionaldataFormat: "NHWC" | "NCHW"

        Returns T

      sigmoid

      • sigmoid<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      sign

      • sign<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      sin

      • sin<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      sinh

      • sinh<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      slice

      • slice<T extends Tensor>(
            this: T,
            begin: number | number[],
            size?: number | number[],
        ): T

      • Type Parameters

        Parameters

        • this: T
        • begin: number | number[]
        • Optionalsize: number | number[]

        Returns T

      softmax

      • softmax<T extends Tensor>(this: T, dim?: number): T

      • Type Parameters

        Parameters

        • this: T
        • Optionaldim: number

        Returns T

      softplus

      • softplus<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      spaceToBatchND

      • spaceToBatchND<R extends Rank>(
            blockShape: number[],
            paddings: number[][],
        ): Tensor<R>

      • Type Parameters

        Parameters

        • blockShape: number[]
        • paddings: number[][]

        Returns Tensor<R>

      split

      • split<T extends Tensor>(numOrSizeSplits: number | number[], axis?: number): T[]

      • Type Parameters

        Parameters

        • numOrSizeSplits: number | number[]
        • Optionalaxis: number

        Returns T[]

      sqrt

      • sqrt<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      square

      • square<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      squaredDifference

      • squaredDifference<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      squeeze

      • squeeze<T extends Tensor>(axis?: number[]): T

      • Type Parameters

        Parameters

        • Optionalaxis: number[]

        Returns T

      stack

      • stack<T extends Tensor>(x: Tensor | Tensor[], axis?: number): T

      • Type Parameters

        Parameters

        Returns T

      step

      • step<T extends Tensor>(this: T, alpha?: number): T

      • Type Parameters

        Parameters

        • this: T
        • Optionalalpha: number

        Returns T

      stridedSlice

      • stridedSlice<T extends Tensor>(
            this: Tensor,
            begin: number[],
            end: number[],
            strides: number[],
            beginMask?: number,
            endMask?: number,
            ellipsisMask?: number,
            newAxisMask?: number,
            shrinkAxisMask?: number,
        ): Tensor

      • Type Parameters

        Parameters

        • this: Tensor
        • begin: number[]
        • end: number[]
        • strides: number[]
        • OptionalbeginMask: number
        • OptionalendMask: number
        • OptionalellipsisMask: number
        • OptionalnewAxisMask: number
        • OptionalshrinkAxisMask: number

        Returns Tensor

      sub

      • sub<T extends Tensor>(b: Tensor | TensorLike): T

      • Type Parameters

        Parameters

        Returns T

      sum

      • sum<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T

      • Type Parameters

        Parameters

        • Optionalaxis: number | number[]
        • OptionalkeepDims: boolean

        Returns T

      tan

      • tan<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      tanh

      • tanh<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      throwIfDisposed

      • throwIfDisposed(): void

      • Returns void

      tile

      • tile<T extends Tensor>(b: number[]): T

      • Type Parameters

        Parameters

        • b: number[]

        Returns T

      toBool

      • toBool<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      toFloat

      • toFloat<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      toInt

      • toInt<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

      topk

      • topk<T extends Tensor>(
            this: T,
            k?: number,
            sorted?: boolean,
        ): { indices: T; values: T }

      • Type Parameters

        Parameters

        • this: T
        • Optionalk: number
        • Optionalsorted: boolean

        Returns { indices: T; values: T }

      toString

      • toString(verbose?: boolean): string

      • Returns a human-readable description of the tensor. Useful for logging.

        -

        Parameters

        • Optionalverbose: boolean

        Returns string

        Doc

      transpose

      • transpose<T extends Tensor>(perm?: number[]): T

      • Type Parameters

        Parameters

        • Optionalperm: number[]

        Returns T

      unique

      • unique<T extends Tensor>(this: T, axis?: number): { indices: T; values: T }

      • Type Parameters

        Parameters

        • this: T
        • Optionalaxis: number

        Returns { indices: T; values: T }

      unsortedSegmentSum

      • unsortedSegmentSum<T extends Tensor>(
            this: T,
            segmentIds: Tensor1D | TensorLike1D,
            numSegments: number,
        ): T

      • Type Parameters

        Parameters

        • this: T
        • segmentIds: Tensor1D | TensorLike1D
        • numSegments: number

        Returns T

      unstack

      • unstack<T extends Tensor>(axis?: number): T[]

      • Type Parameters

        Parameters

        • Optionalaxis: number

        Returns T[]

      variable

      • variable(
            trainable?: boolean,
            name?: string,
            dtype?: keyof DataTypeMap,
        ): Variable<R>

      • Parameters

        • Optionaltrainable: boolean
        • Optionalname: string
        • Optionaldtype: keyof DataTypeMap

        Returns Variable<R>

      where

      zerosLike

      • zerosLike<T extends Tensor>(this: T): T

      • Type Parameters

        Parameters

        • this: T

        Returns T

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      diff --git a/typedoc/classes/WebCam.html b/typedoc/classes/WebCam.html deleted file mode 100644 index c3ee282f..00000000 --- a/typedoc/classes/WebCam.html +++ /dev/null @@ -1,35 +0,0 @@ -WebCam | @vladmandic/human - v3.3.5

      Class WebCam

      Constructors

      constructor -

      Properties

      config -devices -element -stream -

      Accessors

      capabilities -constraints -height -label -paused -settings -track -width -

      Methods

      enumerate -pause -play -start -stop -

      Constructors

      constructor

      Properties

      config

      config: WebCamConfig

      current webcam configuration

      -

      devices

      devices: MediaDeviceInfo[] = []

      enumerated video devices

      -

      element

      element: undefined | HTMLVideoElement

      instance of dom element associated with webcam stream

      -

      stream

      stream: undefined | MediaStream

      active webcam stream

      -

      Accessors

      capabilities

      • get capabilities(): undefined | MediaTrackCapabilities

      • get webcam capabilities

        -

        Returns undefined | MediaTrackCapabilities

      constraints

      • get constraints(): undefined | MediaTrackConstraints

      • get webcam constraints

        -

        Returns undefined | MediaTrackConstraints

      height

      label

      paused

      settings

      • get settings(): undefined | MediaTrackSettings

      • get webcam settings

        -

        Returns undefined | MediaTrackSettings

      track

      • get track(): undefined | MediaStreamTrack

      • get active webcam stream track

        -

        Returns undefined | MediaStreamTrack

      width

      Methods

      enumerate

      • enumerate(): Promise<MediaDeviceInfo[]>

      • Returns Promise<MediaDeviceInfo[]>

      pause

      play

      start

      • start(webcamConfig?: Partial<WebCamConfig>): Promise<string>

      • start method initializizes webcam stream and associates it with a dom video element

        -

        Parameters

        Returns Promise<string>

      stop

      • stop(): void

      • stop method stops active webcam stream track and disconnects webcam

        -

        Returns void

      Settings

      Member Visibility

      On This Page

      Constructors
      Properties
      Accessors
      Methods
      diff --git a/typedoc/classes/models.Models.html b/typedoc/classes/models.Models.html index 1a2f92f1..83b08377 100644 --- a/typedoc/classes/models.Models.html +++ b/typedoc/classes/models.Models.html @@ -1,4 +1,4 @@ -Models | @vladmandic/human - v3.3.5

      Class Models

      Models class used by Human

      +Models | @vladmandic/human - v3.3.6
      @vladmandic/human - v3.3.6
        Preparing search index...

        Class Models

        Models class used by Human

        • models: record of all GraphModels
        • list: returns list of configured models with their stats
          • @@ -7,12 +7,12 @@
        • validate: checks loaded models for valid kernel ops vs current backend
        • stats: live detailed model stats that can be checked during model load phase
        -

        Constructors

        constructor -

        Properties

        models -

        Methods

        list -load -loaded -reset -stats -validate -

        Constructors

        constructor

        Properties

        models

        models: Record<string, null | GraphModel> = {}

        Methods

        list

        • list(): { loaded: boolean; name: string; size: number; url: any }[]

        • Returns { loaded: boolean; name: string; size: number; url: any }[]

        load

        loaded

        reset

        stats

        validate

        • validate(): { missing: string[]; name: string }[]

        • Returns { missing: string[]; name: string }[]

        Settings

        Member Visibility

        On This Page

        Constructors
        Properties
        Methods
        +
        Index

        Constructors

        constructor +

        Properties

        models +

        Methods

        list +load +loaded +reset +stats +validate +

        Constructors

        Properties

        models: Record<string, null | GraphModel> = {}

        Methods

        • Returns { loaded: boolean; name: string; size: number; url: any }[]

        • Returns { missing: string[]; name: string }[]

        Settings

        Member Visibility

        On This Page

        Constructors
        Properties
        Methods
        diff --git a/typedoc/enums/Rank.html b/typedoc/enums/Rank.html index edbe6fbc..483c4cb3 100644 --- a/typedoc/enums/Rank.html +++ b/typedoc/enums/Rank.html @@ -1,8 +1,9 @@ -Rank | @vladmandic/human - v3.3.5

        Enumeration Rank

        Enumeration Members

        R0 -R1 -R2 -R3 -R4 -R5 -R6 -

        Enumeration Members

        R0

        R0: "R0"

        R1

        R1: "R1"

        R2

        R2: "R2"

        R3

        R3: "R3"

        R4

        R4: "R4"

        R5

        R5: "R5"

        R6

        R6: "R6"

        Settings

        Member Visibility

        On This Page

        Enumeration Members
        +Rank | @vladmandic/human - v3.3.6
        @vladmandic/human - v3.3.6
          Preparing search index...

          Enumeration Rank

          Explict reexport of main @tensorflow/tfjs types

          +
          Index

          Enumeration Members

          R0 +R1 +R2 +R3 +R4 +R5 +R6 +

          Enumeration Members

          R0: "R0"
          R1: "R1"
          R2: "R2"
          R3: "R3"
          R4: "R4"
          R5: "R5"
          R6: "R6"

          Settings

          Member Visibility

          On This Page

          Enumeration Members
          diff --git a/typedoc/functions/draw.all.html b/typedoc/functions/draw.all.html index 4df823e0..455832a8 100644 --- a/typedoc/functions/draw.all.html +++ b/typedoc/functions/draw.all.html @@ -1,2 +1,2 @@ -all | @vladmandic/human - v3.3.5

          Function all

          • all(
                inCanvas: AnyCanvas,
                result: Result,
                drawOptions?: Partial<DrawOptions>,
            ): Promise<null | [void, void, void, void, void]>

          • meta-function that performs draw for: canvas, face, body, hand

            -

            Parameters

            Returns Promise<null | [void, void, void, void, void]>

          Settings

          Member Visibility
          +all | @vladmandic/human - v3.3.6
          @vladmandic/human - v3.3.6
            Preparing search index...

            Function all

            • meta-function that performs draw for: canvas, face, body, hand

              +

              Parameters

              Returns Promise<null | [void, void, void, void, void]>

            Settings

            Member Visibility
            diff --git a/typedoc/functions/draw.body.html b/typedoc/functions/draw.body.html index cf5b7f5b..892fd4f7 100644 --- a/typedoc/functions/draw.body.html +++ b/typedoc/functions/draw.body.html @@ -1,2 +1,2 @@ -body | @vladmandic/human - v3.3.5

            Function body

            Settings

            Member Visibility
            +body | @vladmandic/human - v3.3.6
            @vladmandic/human - v3.3.6
              Preparing search index...

              Function body

              Settings

              Member Visibility
              diff --git a/typedoc/functions/draw.canvas.html b/typedoc/functions/draw.canvas.html index c6bd7f0a..fa2f185d 100644 --- a/typedoc/functions/draw.canvas.html +++ b/typedoc/functions/draw.canvas.html @@ -1,2 +1,2 @@ -canvas | @vladmandic/human - v3.3.5

              Function canvas

              Settings

              Member Visibility
              +canvas | @vladmandic/human - v3.3.6
              @vladmandic/human - v3.3.6
                Preparing search index...

                Function canvas

                Settings

                Member Visibility
                diff --git a/typedoc/functions/draw.face.html b/typedoc/functions/draw.face.html index 026839e4..d0d5a1b4 100644 --- a/typedoc/functions/draw.face.html +++ b/typedoc/functions/draw.face.html @@ -1,2 +1,2 @@ -face | @vladmandic/human - v3.3.5

                Function face

                Settings

                Member Visibility
                +face | @vladmandic/human - v3.3.6
                @vladmandic/human - v3.3.6
                  Preparing search index...

                  Function face

                  Settings

                  Member Visibility
                  diff --git a/typedoc/functions/draw.gesture.html b/typedoc/functions/draw.gesture.html index 0c107791..2bcb5fe1 100644 --- a/typedoc/functions/draw.gesture.html +++ b/typedoc/functions/draw.gesture.html @@ -1,2 +1,2 @@ -gesture | @vladmandic/human - v3.3.5

                  Function gesture

                  Settings

                  Member Visibility
                  +gesture | @vladmandic/human - v3.3.6
                  @vladmandic/human - v3.3.6
                    Preparing search index...

                    Function gesture

                    Settings

                    Member Visibility
                    diff --git a/typedoc/functions/draw.hand.html b/typedoc/functions/draw.hand.html index d8961afb..0eb8720c 100644 --- a/typedoc/functions/draw.hand.html +++ b/typedoc/functions/draw.hand.html @@ -1,2 +1,2 @@ -hand | @vladmandic/human - v3.3.5

                    Function hand

                    Settings

                    Member Visibility
                    +hand | @vladmandic/human - v3.3.6
                    @vladmandic/human - v3.3.6
                      Preparing search index...

                      Function hand

                      Settings

                      Member Visibility
                      diff --git a/typedoc/functions/draw.init.html b/typedoc/functions/draw.init.html index ea4bdd8f..3ae3b304 100644 --- a/typedoc/functions/draw.init.html +++ b/typedoc/functions/draw.init.html @@ -1,2 +1,2 @@ -init | @vladmandic/human - v3.3.5

                      Function init

                      • init(): void

                      • sets default label templates for face/body/hand/object/gestures

                        -

                        Returns void

                      Settings

                      Member Visibility
                      +init | @vladmandic/human - v3.3.6
                      @vladmandic/human - v3.3.6
                        Preparing search index...

                        Function init

                        • sets default label templates for face/body/hand/object/gestures

                          +

                          Returns void

                        Settings

                        Member Visibility
                        diff --git a/typedoc/functions/draw.object.html b/typedoc/functions/draw.object.html index febb3887..d1afafda 100644 --- a/typedoc/functions/draw.object.html +++ b/typedoc/functions/draw.object.html @@ -1,2 +1,2 @@ -object | @vladmandic/human - v3.3.5

                        Function object

                        Settings

                        Member Visibility
                        +object | @vladmandic/human - v3.3.6
                        @vladmandic/human - v3.3.6
                          Preparing search index...

                          Function object

                          Settings

                          Member Visibility
                          diff --git a/typedoc/functions/draw.person.html b/typedoc/functions/draw.person.html index 20194a3e..639f6757 100644 --- a/typedoc/functions/draw.person.html +++ b/typedoc/functions/draw.person.html @@ -1,2 +1,2 @@ -person | @vladmandic/human - v3.3.5

                          Function person

                          Settings

                          Member Visibility
                          +person | @vladmandic/human - v3.3.6
                          @vladmandic/human - v3.3.6
                            Preparing search index...

                            Function person

                            Settings

                            Member Visibility
                            diff --git a/typedoc/functions/draw.tensor.html b/typedoc/functions/draw.tensor.html index ae389963..e04c7b5a 100644 --- a/typedoc/functions/draw.tensor.html +++ b/typedoc/functions/draw.tensor.html @@ -1,2 +1,2 @@ -tensor | @vladmandic/human - v3.3.5

                            Function tensor

                            • tensor(input: Tensor2D, output: HTMLCanvasElement): Promise<void>

                            • draw processed canvas

                              -

                              Parameters

                              • input: Tensor2D
                              • output: HTMLCanvasElement

                              Returns Promise<void>

                            Settings

                            Member Visibility
                            +tensor | @vladmandic/human - v3.3.6
                            @vladmandic/human - v3.3.6
                              Preparing search index...

                              Function tensor

                              • draw processed canvas

                                +

                                Parameters

                                • input: Tensor2D
                                • output: HTMLCanvasElement

                                Returns Promise<void>

                              Settings

                              Member Visibility
                              diff --git a/typedoc/functions/empty.html b/typedoc/functions/empty.html index 6ffb35bc..72db6f1d 100644 --- a/typedoc/functions/empty.html +++ b/typedoc/functions/empty.html @@ -1 +1 @@ -empty | @vladmandic/human - v3.3.5

                              Function empty

                              Settings

                              Member Visibility
                              +empty | @vladmandic/human - v3.3.6
                              @vladmandic/human - v3.3.6
                                Preparing search index...

                                Function empty

                                Settings

                                Member Visibility
                                diff --git a/typedoc/functions/match.distance.html b/typedoc/functions/match.distance.html index 81a6aceb..8be813cc 100644 --- a/typedoc/functions/match.distance.html +++ b/typedoc/functions/match.distance.html @@ -1,4 +1,4 @@ -distance | @vladmandic/human - v3.3.5

                                Function distance

                                • distance(
                                      descriptor1: Descriptor,
                                      descriptor2: Descriptor,
                                      options?: MatchOptions,
                                  ): number

                                • Calculates distance between two descriptors

                                  +distance | @vladmandic/human - v3.3.6
                                  @vladmandic/human - v3.3.6
                                    Preparing search index...

                                    Function distance

                                    • Calculates distance between two descriptors

                                      Parameters

                                      • descriptor1: Descriptor
                                      • descriptor2: Descriptor
                                      • options: MatchOptions = ...

                                        calculation options

                                        • order - algorithm to use @@ -6,4 +6,4 @@ Euclidean distance if order is 2 (default), Minkowski distance algo
                                        • multiplier - by how much to enhance difference analysis in range of 1..100 default is 20 which normalizes results to similarity above 0.5 can be considered a match
                                        -

                                      Returns number

                                    Settings

                                    Member Visibility
                                    +

                                  Returns number

                                  Settings

                                  Member Visibility
                                  diff --git a/typedoc/functions/match.find.html b/typedoc/functions/match.find.html index 6c4ca93d..96e5a696 100644 --- a/typedoc/functions/match.find.html +++ b/typedoc/functions/match.find.html @@ -1,4 +1,4 @@ -find | @vladmandic/human - v3.3.5

                                  Function find

                                  • find(
                                        descriptor: Descriptor,
                                        descriptors: Descriptor[],
                                        options?: MatchOptions,
                                    ): { distance: number; index: number; similarity: number }

                                  • Matches given descriptor to a closest entry in array of descriptors

                                    +find | @vladmandic/human - v3.3.6
                                    @vladmandic/human - v3.3.6
                                      Preparing search index...

                                      Function find

                                      • Matches given descriptor to a closest entry in array of descriptors

                                        Parameters

                                        • descriptor: Descriptor

                                          face descriptor

                                        • descriptors: Descriptor[]

                                          array of face descriptors to commpare given descriptor to

                                        • options: MatchOptions = ...

                                          see similarity method for options description @@ -8,4 +8,4 @@ Returns

                                        • distance calculated distance of given descriptor to the best match
                                        • similarity calculated normalized similarity of given descriptor to the best match
                                        -

                                      Returns { distance: number; index: number; similarity: number }

                                    Settings

                                    Member Visibility
                                    +

                                    Returns { distance: number; index: number; similarity: number }

                                    Settings

                                    Member Visibility
                                    diff --git a/typedoc/functions/match.similarity.html b/typedoc/functions/match.similarity.html index b4dc20c1..b2f47fe1 100644 --- a/typedoc/functions/match.similarity.html +++ b/typedoc/functions/match.similarity.html @@ -1,4 +1,4 @@ -similarity | @vladmandic/human - v3.3.5

                                    Function similarity

                                    • similarity(
                                          descriptor1: Descriptor,
                                          descriptor2: Descriptor,
                                          options?: MatchOptions,
                                      ): number

                                    • Calculates normalized similarity between two face descriptors based on their distance

                                      +similarity | @vladmandic/human - v3.3.6
                                      @vladmandic/human - v3.3.6
                                        Preparing search index...

                                        Function similarity

                                        • Calculates normalized similarity between two face descriptors based on their distance

                                          Parameters

                                          • descriptor1: Descriptor
                                          • descriptor2: Descriptor
                                          • options: MatchOptions = ...

                                            calculation options

                                            • order - algorithm to use @@ -10,4 +10,4 @@ default is 20 which normalizes results to similarity above 0.5 can be considered default is 0.2...0.8 Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity
                                            -

                                          Returns number

                                        Settings

                                        Member Visibility
                                        +

                                      Returns number

                                      Settings

                                      Member Visibility
                                      diff --git a/typedoc/functions/models.validateModel.html b/typedoc/functions/models.validateModel.html index b4ce1715..a41f9272 100644 --- a/typedoc/functions/models.validateModel.html +++ b/typedoc/functions/models.validateModel.html @@ -1 +1 @@ -validateModel | @vladmandic/human - v3.3.5

                                      Function validateModel

                                      Settings

                                      Member Visibility
                                      +validateModel | @vladmandic/human - v3.3.6
                                      @vladmandic/human - v3.3.6
                                        Preparing search index...

                                        Function validateModel

                                        Settings

                                        Member Visibility
                                        diff --git a/typedoc/hierarchy.html b/typedoc/hierarchy.html index 783089e8..fe45e7ed 100644 --- a/typedoc/hierarchy.html +++ b/typedoc/hierarchy.html @@ -1 +1 @@ -@vladmandic/human - v3.3.5

                                        @vladmandic/human - v3.3.5

                                        Hierarchy Summary

                                        Settings

                                        Member Visibility
                                        +@vladmandic/human - v3.3.6
                                        @vladmandic/human - v3.3.6
                                          Preparing search index...

                                          @vladmandic/human - v3.3.6

                                          Hierarchy Summary

                                          Settings

                                          Member Visibility
                                          diff --git a/typedoc/index.html b/typedoc/index.html index 19c22383..695cd0d5 100644 --- a/typedoc/index.html +++ b/typedoc/index.html @@ -1 +1 @@ -@vladmandic/human - v3.3.5

                                          @vladmandic/human - v3.3.5

                                          Namespaces

                                          draw
                                          match
                                          models
                                          Tensor

                                          Enumerations

                                          Rank

                                          Classes

                                          Env
                                          GraphModel
                                          Human
                                          Tensor
                                          WebCam

                                          Interfaces

                                          BodyConfig
                                          BodyKeypoint
                                          BodyResult
                                          Config
                                          DrawOptions
                                          FaceAntiSpoofConfig
                                          FaceAttentionConfig
                                          FaceConfig
                                          FaceDescriptionConfig
                                          FaceDetectorConfig
                                          FaceEmotionConfig
                                          FaceGearConfig
                                          FaceIrisConfig
                                          FaceLivenessConfig
                                          FaceMeshConfig
                                          FaceResult
                                          FilterConfig
                                          GenericConfig
                                          GestureConfig
                                          HandConfig
                                          HandResult
                                          ModelInfo
                                          ObjectConfig
                                          ObjectResult
                                          PersonResult
                                          Result
                                          SegmentationConfig
                                          WebCamConfig

                                          Type Aliases

                                          AnyCanvas
                                          AnyImage
                                          AnyVideo
                                          BackendEnum
                                          BodyAnnotation
                                          BodyAnnotationBlazePose
                                          BodyAnnotationEfficientPose
                                          BodyGesture
                                          BodyLandmark
                                          BodyLandmarkBlazePose
                                          BodyLandmarkEfficientNet
                                          BodyLandmarkMoveNet
                                          BodyLandmarkPoseNet
                                          Box
                                          Emotion
                                          Events
                                          ExternalCanvas
                                          FaceGesture
                                          FaceLandmark
                                          Finger
                                          FingerCurl
                                          FingerDirection
                                          Gender
                                          GestureResult
                                          HandGesture
                                          HandType
                                          ImageObjects
                                          Input
                                          IrisGesture
                                          ObjectType
                                          Point
                                          Race
                                          SegmentationEnum
                                          Tensor1D
                                          Tensor2D
                                          Tensor3D
                                          Tensor4D
                                          TensorLike
                                          WarmupEnum

                                          Variables

                                          defaults
                                          env

                                          Functions

                                          empty

                                          References

                                          default → Human

                                          Settings

                                          Member Visibility

                                          On This Page

                                          Namespaces
                                          Enumerations
                                          Classes
                                          Interfaces
                                          Type Aliases
                                          Variables
                                          Functions
                                          References
                                          +@vladmandic/human - v3.3.6
                                          @vladmandic/human - v3.3.6
                                            Preparing search index...

                                            @vladmandic/human - v3.3.6

                                            Namespaces

                                            draw
                                            match
                                            models
                                            Tensor

                                            Enumerations

                                            Rank

                                            Classes

                                            Env
                                            Human

                                            Interfaces

                                            BodyConfig
                                            BodyKeypoint
                                            BodyResult
                                            Config
                                            DrawOptions
                                            FaceAntiSpoofConfig
                                            FaceAttentionConfig
                                            FaceConfig
                                            FaceDescriptionConfig
                                            FaceDetectorConfig
                                            FaceEmotionConfig
                                            FaceGearConfig
                                            FaceIrisConfig
                                            FaceLivenessConfig
                                            FaceMeshConfig
                                            FaceResult
                                            FilterConfig
                                            GenericConfig
                                            GestureConfig
                                            GraphModel
                                            HandConfig
                                            HandResult
                                            ModelInfo
                                            ObjectConfig
                                            ObjectResult
                                            PersonResult
                                            Result
                                            SegmentationConfig
                                            Tensor
                                            WebCam
                                            WebCamConfig

                                            Type Aliases

                                            AnyCanvas
                                            AnyImage
                                            AnyVideo
                                            BackendEnum
                                            BodyAnnotation
                                            BodyAnnotationBlazePose
                                            BodyAnnotationEfficientPose
                                            BodyGesture
                                            BodyLandmark
                                            BodyLandmarkBlazePose
                                            BodyLandmarkEfficientNet
                                            BodyLandmarkMoveNet
                                            BodyLandmarkPoseNet
                                            Box
                                            Emotion
                                            Events
                                            ExternalCanvas
                                            FaceGesture
                                            FaceLandmark
                                            Finger
                                            FingerCurl
                                            FingerDirection
                                            Gender
                                            GestureResult
                                            HandGesture
                                            HandType
                                            ImageObjects
                                            Input
                                            IrisGesture
                                            ObjectType
                                            Point
                                            Race
                                            SegmentationEnum
                                            Tensor1D
                                            Tensor2D
                                            Tensor3D
                                            Tensor4D
                                            TensorLike
                                            WarmupEnum

                                            Variables

                                            defaults
                                            env

                                            Functions

                                            empty

                                            References

                                            default → Human

                                            Settings

                                            Member Visibility

                                            On This Page

                                            Namespaces
                                            Enumerations
                                            Classes
                                            Interfaces
                                            Type Aliases
                                            Variables
                                            Functions
                                            References
                                            diff --git a/typedoc/interfaces/BodyConfig.html b/typedoc/interfaces/BodyConfig.html index 2a54942a..6e487560 100644 --- a/typedoc/interfaces/BodyConfig.html +++ b/typedoc/interfaces/BodyConfig.html @@ -1,16 +1,16 @@ -BodyConfig | @vladmandic/human - v3.3.5

                                            Interface BodyConfig

                                            Configures all body detection specific options

                                            -
                                            interface BodyConfig {
                                                enabled: boolean;
                                                maxDetected: number;
                                                minConfidence: number;
                                                modelPath: string;
                                                skipFrames: number;
                                                skipTime: number;
                                            }

                                            Hierarchy (View Summary)

                                            Properties

                                            enabled -maxDetected -minConfidence -modelPath -skipFrames -skipTime -

                                            Properties

                                            enabled

                                            enabled: boolean

                                            is module enabled?

                                            -

                                            maxDetected

                                            maxDetected: number

                                            maximum number of detected bodies

                                            -

                                            minConfidence

                                            minConfidence: number

                                            minimum confidence for a detected body before results are discarded

                                            -

                                            modelPath

                                            modelPath: string

                                            path to model json file (relative to modelBasePath

                                            -

                                            skipFrames

                                            skipFrames: number

                                            how many max frames to go without re-running model if cached results are acceptable +BodyConfig | @vladmandic/human - v3.3.6

                                            @vladmandic/human - v3.3.6
                                              Preparing search index...

                                              Interface BodyConfig

                                              Configures all body detection specific options

                                              +
                                              interface BodyConfig {
                                                  enabled: boolean;
                                                  maxDetected: number;
                                                  minConfidence: number;
                                                  modelPath: string;
                                                  skipFrames: number;
                                                  skipTime: number;
                                              }

                                              Hierarchy (View Summary)

                                              Index

                                              Properties

                                              enabled +maxDetected +minConfidence +modelPath +skipFrames +skipTime +

                                              Properties

                                              enabled: boolean

                                              is module enabled?

                                              +
                                              maxDetected: number

                                              maximum number of detected bodies

                                              +
                                              minConfidence: number

                                              minimum confidence for a detected body before results are discarded

                                              +
                                              modelPath: string

                                              path to model json file (relative to modelBasePath

                                              +
                                              skipFrames: number

                                              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                              -

                                              skipTime

                                              skipTime: number

                                              how many max milliseconds to go without re-running model if cached results are acceptable +

                                              skipTime: number

                                              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                              -

                                              Settings

                                              Member Visibility

                                              On This Page

                                              Properties
                                              +

                                              Settings

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                                              On This Page

                                              Properties
                                              diff --git a/typedoc/interfaces/BodyKeypoint.html b/typedoc/interfaces/BodyKeypoint.html index 481100a6..8184c6fa 100644 --- a/typedoc/interfaces/BodyKeypoint.html +++ b/typedoc/interfaces/BodyKeypoint.html @@ -1,12 +1,12 @@ -BodyKeypoint | @vladmandic/human - v3.3.5

                                              Interface BodyKeypoint

                                              Body Result keypoints

                                              -
                                              interface BodyKeypoint {
                                                  distance?: Point;
                                                  part: BodyLandmark;
                                                  position: Point;
                                                  positionRaw: Point;
                                                  score: number;
                                              }

                                              Properties

                                              distance? -part -position -positionRaw -score -

                                              Properties

                                              Optionaldistance

                                              distance?: Point

                                              body part position relative to body center in meters

                                              -

                                              part

                                              part: BodyLandmark

                                              body part name

                                              -

                                              position

                                              position: Point

                                              body part position

                                              -

                                              positionRaw

                                              positionRaw: Point

                                              body part position normalized to 0..1

                                              -

                                              score

                                              score: number

                                              body part detection score

                                              -

                                              Settings

                                              Member Visibility

                                              On This Page

                                              Properties
                                              +BodyKeypoint | @vladmandic/human - v3.3.6
                                              @vladmandic/human - v3.3.6
                                                Preparing search index...

                                                Interface BodyKeypoint

                                                Body Result keypoints

                                                +
                                                interface BodyKeypoint {
                                                    distance?: Point;
                                                    part: BodyLandmark;
                                                    position: Point;
                                                    positionRaw: Point;
                                                    score: number;
                                                }
                                                Index

                                                Properties

                                                distance? +part +position +positionRaw +score +

                                                Properties

                                                distance?: Point

                                                body part position relative to body center in meters

                                                +
                                                part: BodyLandmark

                                                body part name

                                                +
                                                position: Point

                                                body part position

                                                +
                                                positionRaw: Point

                                                body part position normalized to 0..1

                                                +
                                                score: number

                                                body part detection score

                                                +

                                                Settings

                                                Member Visibility

                                                On This Page

                                                Properties
                                                diff --git a/typedoc/interfaces/BodyResult.html b/typedoc/interfaces/BodyResult.html index 254678ee..8a03083e 100644 --- a/typedoc/interfaces/BodyResult.html +++ b/typedoc/interfaces/BodyResult.html @@ -1,14 +1,14 @@ -BodyResult | @vladmandic/human - v3.3.5

                                                Interface BodyResult

                                                Body results

                                                -
                                                interface BodyResult {
                                                    annotations: Record<BodyAnnotation, Point[][]>;
                                                    box: Box;
                                                    boxRaw: Box;
                                                    id: number;
                                                    keypoints: BodyKeypoint[];
                                                    score: number;
                                                }

                                                Properties

                                                annotations -box -boxRaw -id -keypoints -score -

                                                Properties

                                                annotations

                                                annotations: Record<BodyAnnotation, Point[][]>

                                                detected body keypoints combined into annotated parts

                                                -

                                                box

                                                box: Box

                                                detected body box

                                                -

                                                boxRaw

                                                boxRaw: Box

                                                detected body box normalized to 0..1

                                                -

                                                id

                                                id: number

                                                body id

                                                -

                                                keypoints

                                                keypoints: BodyKeypoint[]

                                                detected body keypoints

                                                -

                                                score

                                                score: number

                                                body detection score

                                                -

                                                Settings

                                                Member Visibility

                                                On This Page

                                                Properties
                                                +BodyResult | @vladmandic/human - v3.3.6
                                                @vladmandic/human - v3.3.6
                                                  Preparing search index...

                                                  Interface BodyResult

                                                  Body results

                                                  +
                                                  interface BodyResult {
                                                      annotations: Record<BodyAnnotation, Point[][]>;
                                                      box: Box;
                                                      boxRaw: Box;
                                                      id: number;
                                                      keypoints: BodyKeypoint[];
                                                      score: number;
                                                  }
                                                  Index

                                                  Properties

                                                  annotations +box +boxRaw +id +keypoints +score +

                                                  Properties

                                                  annotations: Record<BodyAnnotation, Point[][]>

                                                  detected body keypoints combined into annotated parts

                                                  +
                                                  box: Box

                                                  detected body box

                                                  +
                                                  boxRaw: Box

                                                  detected body box normalized to 0..1

                                                  +
                                                  id: number

                                                  body id

                                                  +
                                                  keypoints: BodyKeypoint[]

                                                  detected body keypoints

                                                  +
                                                  score: number

                                                  body detection score

                                                  +

                                                  Settings

                                                  Member Visibility

                                                  On This Page

                                                  Properties
                                                  diff --git a/typedoc/interfaces/Config.html b/typedoc/interfaces/Config.html index a742dda7..8cf20672 100644 --- a/typedoc/interfaces/Config.html +++ b/typedoc/interfaces/Config.html @@ -1,74 +1,74 @@ -Config | @vladmandic/human - v3.3.5

                                                  Interface Config

                                                  Configuration interface definition for Human library +Config | @vladmandic/human - v3.3.6

                                                  @vladmandic/human - v3.3.6
                                                    Preparing search index...

                                                    Interface Config

                                                    Configuration interface definition for Human library Contains all configurable parameters Defaults: config

                                                    -
                                                    interface Config {
                                                        async: boolean;
                                                        backend: BackendEnum;
                                                        body: Partial<BodyConfig>;
                                                        cacheModels: boolean;
                                                        cacheSensitivity: number;
                                                        deallocate: boolean;
                                                        debug: boolean;
                                                        face: Partial<FaceConfig>;
                                                        filter: Partial<FilterConfig>;
                                                        flags: Record<string, unknown>;
                                                        gesture: Partial<GestureConfig>;
                                                        hand: Partial<HandConfig>;
                                                        modelBasePath: string;
                                                        object: Partial<ObjectConfig>;
                                                        segmentation: Partial<SegmentationConfig>;
                                                        skipAllowed: boolean;
                                                        softwareKernels: boolean;
                                                        validateModels: boolean;
                                                        warmup: WarmupEnum;
                                                        wasmPath: string;
                                                        wasmPlatformFetch: boolean;
                                                    }

                                                    Properties

                                                    async -backend -body -cacheModels -cacheSensitivity -deallocate -debug -face -filter -flags -gesture -hand -modelBasePath -object -segmentation -skipAllowed -softwareKernels -validateModels -warmup -wasmPath -wasmPlatformFetch -

                                                    Properties

                                                    async

                                                    async: boolean

                                                    Perform model loading and inference concurrently or sequentially

                                                    +
                                                    interface Config {
                                                        async: boolean;
                                                        backend: BackendEnum;
                                                        body: Partial<BodyConfig>;
                                                        cacheModels: boolean;
                                                        cacheSensitivity: number;
                                                        deallocate: boolean;
                                                        debug: boolean;
                                                        face: Partial<FaceConfig>;
                                                        filter: Partial<FilterConfig>;
                                                        flags: Record<string, unknown>;
                                                        gesture: Partial<GestureConfig>;
                                                        hand: Partial<HandConfig>;
                                                        modelBasePath: string;
                                                        object: Partial<ObjectConfig>;
                                                        segmentation: Partial<SegmentationConfig>;
                                                        skipAllowed: boolean;
                                                        softwareKernels: boolean;
                                                        validateModels: boolean;
                                                        warmup: WarmupEnum;
                                                        wasmPath: string;
                                                        wasmPlatformFetch: boolean;
                                                    }
                                                    Index

                                                    Properties

                                                    async +backend +body +cacheModels +cacheSensitivity +deallocate +debug +face +filter +flags +gesture +hand +modelBasePath +object +segmentation +skipAllowed +softwareKernels +validateModels +warmup +wasmPath +wasmPlatformFetch +

                                                    Properties

                                                    async: boolean

                                                    Perform model loading and inference concurrently or sequentially

                                                    default: true

                                                    -

                                                    backend

                                                    backend: BackendEnum

                                                    Backend used for TFJS operations +

                                                    backend: BackendEnum

                                                    Backend used for TFJS operations valid build-in backends are:

                                                    • Browser: cpu, wasm, webgl, humangl, webgpu
                                                    • NodeJS: cpu, wasm, tensorflow default: webgl for browser and tensorflow for nodejs
                                                    -

                                                    body

                                                    body: Partial<BodyConfig>

                                                    Body config BodyConfig

                                                    -

                                                    cacheModels

                                                    cacheModels: boolean

                                                    Cache models in IndexDB on first sucessfull load +

                                                    body

                                                    body: Partial<BodyConfig>

                                                    Body config BodyConfig

                                                    +
                                                    cacheModels: boolean

                                                    Cache models in IndexDB on first sucessfull load default: true if indexdb is available (browsers), false if its not (nodejs)

                                                    -

                                                    cacheSensitivity

                                                    cacheSensitivity: number

                                                    Cache sensitivity

                                                    +
                                                    cacheSensitivity: number

                                                    Cache sensitivity

                                                    • values 0..1 where 0.01 means reset cache if input changed more than 1%
                                                    • set to 0 to disable caching

                                                    default: 0.7

                                                    -

                                                    deallocate

                                                    deallocate: boolean

                                                    Perform immediate garbage collection on deallocated tensors instead of caching them

                                                    -

                                                    debug

                                                    debug: boolean

                                                    Print debug statements to console

                                                    +
                                                    deallocate: boolean

                                                    Perform immediate garbage collection on deallocated tensors instead of caching them

                                                    +
                                                    debug: boolean

                                                    Print debug statements to console

                                                    default: true

                                                    -

                                                    face

                                                    face: Partial<FaceConfig>

                                                    Face config FaceConfig

                                                    -

                                                    filter

                                                    filter: Partial<FilterConfig>

                                                    Filter config FilterConfig

                                                    -

                                                    flags

                                                    flags: Record<string, unknown>

                                                    Explicit flags passed to initialize TFJS

                                                    -

                                                    gesture

                                                    gesture: Partial<GestureConfig>

                                                    Gesture config GestureConfig

                                                    -

                                                    hand

                                                    hand: Partial<HandConfig>

                                                    Hand config HandConfig

                                                    -

                                                    modelBasePath

                                                    modelBasePath: string

                                                    Base model path (typically starting with file://, http:// or https://) for all models

                                                    +
                                                    face: Partial<FaceConfig>

                                                    Face config FaceConfig

                                                    +
                                                    filter: Partial<FilterConfig>

                                                    Filter config FilterConfig

                                                    +
                                                    flags: Record<string, unknown>

                                                    Explicit flags passed to initialize TFJS

                                                    +
                                                    gesture: Partial<GestureConfig>

                                                    Gesture config GestureConfig

                                                    +
                                                    hand: Partial<HandConfig>

                                                    Hand config HandConfig

                                                    +
                                                    modelBasePath: string

                                                    Base model path (typically starting with file://, http:// or https://) for all models

                                                    • individual modelPath values are relative to this path

                                                    default: ../models/ for browsers and file://models/ for nodejs

                                                    -

                                                    object

                                                    object: Partial<ObjectConfig>

                                                    Object config ObjectConfig

                                                    -

                                                    segmentation

                                                    segmentation: Partial<SegmentationConfig>

                                                    Segmentation config SegmentationConfig

                                                    -

                                                    skipAllowed

                                                    skipAllowed: boolean

                                                    Internal Variable

                                                    -

                                                    softwareKernels

                                                    softwareKernels: boolean

                                                    Software Kernels +

                                                    object: Partial<ObjectConfig>

                                                    Object config ObjectConfig

                                                    +
                                                    segmentation: Partial<SegmentationConfig>

                                                    Segmentation config SegmentationConfig

                                                    +
                                                    skipAllowed: boolean

                                                    Internal Variable

                                                    +
                                                    softwareKernels: boolean

                                                    Software Kernels Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend

                                                    -

                                                    validateModels

                                                    validateModels: boolean

                                                    Validate kernel ops used in model during model load +

                                                    validateModels: boolean

                                                    Validate kernel ops used in model during model load default: true any errors will be printed on console but will be treated as non-fatal

                                                    -

                                                    warmup

                                                    warmup: WarmupEnum

                                                    What to use for human.warmup()

                                                    +
                                                    warmup: WarmupEnum

                                                    What to use for human.warmup()

                                                    • warmup pre-initializes all models for faster inference but can take significant time on startup
                                                    • used by webgl, humangl and webgpu backends

                                                    default: full

                                                    -

                                                    wasmPath

                                                    wasmPath: string

                                                    Path to *.wasm files if backend is set to wasm

                                                    +
                                                    wasmPath: string

                                                    Path to *.wasm files if backend is set to wasm

                                                    default: auto-detects to link to CDN jsdelivr when running in browser

                                                    -

                                                    wasmPlatformFetch

                                                    wasmPlatformFetch: boolean

                                                    Force WASM loader to use platform fetch

                                                    +
                                                    wasmPlatformFetch: boolean

                                                    Force WASM loader to use platform fetch

                                                    default: false

                                                    -

                                                    Settings

                                                    Member Visibility

                                                    On This Page

                                                    Properties
                                                    +

                                                    Settings

                                                    Member Visibility

                                                    On This Page

                                                    Properties
                                                    diff --git a/typedoc/interfaces/DrawOptions.html b/typedoc/interfaces/DrawOptions.html index b2639782..52f831fe 100644 --- a/typedoc/interfaces/DrawOptions.html +++ b/typedoc/interfaces/DrawOptions.html @@ -1,57 +1,57 @@ -DrawOptions | @vladmandic/human - v3.3.5

                                                    Interface DrawOptions

                                                    Draw Options

                                                    +DrawOptions | @vladmandic/human - v3.3.6
                                                    @vladmandic/human - v3.3.6
                                                      Preparing search index...

                                                      Interface DrawOptions

                                                      Draw Options

                                                      • Accessed via human.draw.options or provided per each draw method as the drawOptions optional parameter
                                                      -
                                                      interface DrawOptions {
                                                          alpha: number;
                                                          bodyLabels: string;
                                                          bodyPartLabels: string;
                                                          color: string;
                                                          drawAttention: boolean;
                                                          drawBoxes: boolean;
                                                          drawGaze: boolean;
                                                          drawGestures: boolean;
                                                          drawLabels: boolean;
                                                          drawPoints: boolean;
                                                          drawPolygons: boolean;
                                                          faceLabels: string;
                                                          fillPolygons: boolean;
                                                          fingerLabels: string;
                                                          font: string;
                                                          gestureLabels: string;
                                                          handLabels: string;
                                                          labelColor: string;
                                                          lineHeight: number;
                                                          lineWidth: number;
                                                          objectLabels: string;
                                                          pointSize: number;
                                                          roundRect: number;
                                                          shadowColor: string;
                                                          useCurves: boolean;
                                                          useDepth: boolean;
                                                      }

                                                      Properties

                                                      alpha -bodyLabels -bodyPartLabels -color -drawAttention -drawBoxes -drawGaze -drawGestures -drawLabels -drawPoints -drawPolygons -faceLabels -fillPolygons -fingerLabels -font -gestureLabels -handLabels -labelColor -lineHeight -lineWidth -objectLabels -pointSize -roundRect -shadowColor -useCurves -useDepth -

                                                      Properties

                                                      alpha

                                                      alpha: number

                                                      alpha value used for lines

                                                      -

                                                      bodyLabels

                                                      bodyLabels: string

                                                      string template for body labels

                                                      -

                                                      bodyPartLabels

                                                      bodyPartLabels: string

                                                      string template for body part labels

                                                      -

                                                      color

                                                      color: string

                                                      draw line color

                                                      -

                                                      drawAttention

                                                      drawAttention: boolean

                                                      should face attention keypoints be highlighted

                                                      -

                                                      drawBoxes

                                                      drawBoxes: boolean

                                                      should draw boxes around detection results?

                                                      -

                                                      drawGaze

                                                      drawGaze: boolean

                                                      should draw gaze arrows?

                                                      -

                                                      drawGestures

                                                      drawGestures: boolean

                                                      should detected gestures be drawn?

                                                      -

                                                      drawLabels

                                                      drawLabels: boolean

                                                      should labels be drawn?

                                                      -

                                                      drawPoints

                                                      drawPoints: boolean

                                                      should points be drawn?

                                                      -

                                                      drawPolygons

                                                      drawPolygons: boolean

                                                      should draw polygons from detection points?

                                                      -

                                                      faceLabels

                                                      faceLabels: string

                                                      string template for face labels

                                                      -

                                                      fillPolygons

                                                      fillPolygons: boolean

                                                      should fill polygons?

                                                      -

                                                      fingerLabels

                                                      fingerLabels: string

                                                      string template for hand labels

                                                      -

                                                      font

                                                      font: string

                                                      label font

                                                      -

                                                      gestureLabels

                                                      gestureLabels: string

                                                      string template for gesture labels

                                                      -

                                                      handLabels

                                                      handLabels: string

                                                      string template for hand labels

                                                      -

                                                      labelColor

                                                      labelColor: string

                                                      label color

                                                      -

                                                      lineHeight

                                                      lineHeight: number

                                                      line spacing between labels

                                                      -

                                                      lineWidth

                                                      lineWidth: number

                                                      line width for drawn lines

                                                      -

                                                      objectLabels

                                                      objectLabels: string

                                                      string template for object labels

                                                      -

                                                      pointSize

                                                      pointSize: number

                                                      size of drawn points

                                                      -

                                                      roundRect

                                                      roundRect: number

                                                      draw rounded boxes by n pixels

                                                      -

                                                      shadowColor

                                                      shadowColor: string

                                                      label shadow color

                                                      -

                                                      useCurves

                                                      useCurves: boolean

                                                      should lines be curved?

                                                      -

                                                      useDepth

                                                      useDepth: boolean

                                                      use z-coordinate when available

                                                      -

                                                      Settings

                                                      Member Visibility

                                                      On This Page

                                                      Properties
                                                      +
                                                      interface DrawOptions {
                                                          alpha: number;
                                                          bodyLabels: string;
                                                          bodyPartLabels: string;
                                                          color: string;
                                                          drawAttention: boolean;
                                                          drawBoxes: boolean;
                                                          drawGaze: boolean;
                                                          drawGestures: boolean;
                                                          drawLabels: boolean;
                                                          drawPoints: boolean;
                                                          drawPolygons: boolean;
                                                          faceLabels: string;
                                                          fillPolygons: boolean;
                                                          fingerLabels: string;
                                                          font: string;
                                                          gestureLabels: string;
                                                          handLabels: string;
                                                          labelColor: string;
                                                          lineHeight: number;
                                                          lineWidth: number;
                                                          objectLabels: string;
                                                          pointSize: number;
                                                          roundRect: number;
                                                          shadowColor: string;
                                                          useCurves: boolean;
                                                          useDepth: boolean;
                                                      }
                                                      Index

                                                      Properties

                                                      alpha +bodyLabels +bodyPartLabels +color +drawAttention +drawBoxes +drawGaze +drawGestures +drawLabels +drawPoints +drawPolygons +faceLabels +fillPolygons +fingerLabels +font +gestureLabels +handLabels +labelColor +lineHeight +lineWidth +objectLabels +pointSize +roundRect +shadowColor +useCurves +useDepth +

                                                      Properties

                                                      alpha: number

                                                      alpha value used for lines

                                                      +
                                                      bodyLabels: string

                                                      string template for body labels

                                                      +
                                                      bodyPartLabels: string

                                                      string template for body part labels

                                                      +
                                                      color: string

                                                      draw line color

                                                      +
                                                      drawAttention: boolean

                                                      should face attention keypoints be highlighted

                                                      +
                                                      drawBoxes: boolean

                                                      should draw boxes around detection results?

                                                      +
                                                      drawGaze: boolean

                                                      should draw gaze arrows?

                                                      +
                                                      drawGestures: boolean

                                                      should detected gestures be drawn?

                                                      +
                                                      drawLabels: boolean

                                                      should labels be drawn?

                                                      +
                                                      drawPoints: boolean

                                                      should points be drawn?

                                                      +
                                                      drawPolygons: boolean

                                                      should draw polygons from detection points?

                                                      +
                                                      faceLabels: string

                                                      string template for face labels

                                                      +
                                                      fillPolygons: boolean

                                                      should fill polygons?

                                                      +
                                                      fingerLabels: string

                                                      string template for hand labels

                                                      +
                                                      font: string

                                                      label font

                                                      +
                                                      gestureLabels: string

                                                      string template for gesture labels

                                                      +
                                                      handLabels: string

                                                      string template for hand labels

                                                      +
                                                      labelColor: string

                                                      label color

                                                      +
                                                      lineHeight: number

                                                      line spacing between labels

                                                      +
                                                      lineWidth: number

                                                      line width for drawn lines

                                                      +
                                                      objectLabels: string

                                                      string template for object labels

                                                      +
                                                      pointSize: number

                                                      size of drawn points

                                                      +
                                                      roundRect: number

                                                      draw rounded boxes by n pixels

                                                      +
                                                      shadowColor: string

                                                      label shadow color

                                                      +
                                                      useCurves: boolean

                                                      should lines be curved?

                                                      +
                                                      useDepth: boolean

                                                      use z-coordinate when available

                                                      +

                                                      Settings

                                                      Member Visibility

                                                      On This Page

                                                      Properties
                                                      diff --git a/typedoc/interfaces/FaceAntiSpoofConfig.html b/typedoc/interfaces/FaceAntiSpoofConfig.html index 0e8432b2..3c890dc1 100644 --- a/typedoc/interfaces/FaceAntiSpoofConfig.html +++ b/typedoc/interfaces/FaceAntiSpoofConfig.html @@ -1,12 +1,12 @@ -FaceAntiSpoofConfig | @vladmandic/human - v3.3.5

                                                      Interface FaceAntiSpoofConfig

                                                      Anti-spoofing part of face configuration

                                                      -
                                                      interface FaceAntiSpoofConfig {
                                                          enabled: boolean;
                                                          modelPath: string;
                                                          skipFrames: number;
                                                          skipTime: number;
                                                      }

                                                      Hierarchy (View Summary)

                                                      Properties

                                                      enabled -modelPath -skipFrames -skipTime -

                                                      Properties

                                                      enabled

                                                      enabled: boolean

                                                      is module enabled?

                                                      -

                                                      modelPath

                                                      modelPath: string

                                                      path to model json file (relative to modelBasePath

                                                      -

                                                      skipFrames

                                                      skipFrames: number

                                                      how many max frames to go without re-running model if cached results are acceptable +FaceAntiSpoofConfig | @vladmandic/human - v3.3.6

                                                      @vladmandic/human - v3.3.6
                                                        Preparing search index...

                                                        Interface FaceAntiSpoofConfig

                                                        Anti-spoofing part of face configuration

                                                        +
                                                        interface FaceAntiSpoofConfig {
                                                            enabled: boolean;
                                                            modelPath: string;
                                                            skipFrames: number;
                                                            skipTime: number;
                                                        }

                                                        Hierarchy (View Summary)

                                                        Index

                                                        Properties

                                                        enabled +modelPath +skipFrames +skipTime +

                                                        Properties

                                                        enabled: boolean

                                                        is module enabled?

                                                        +
                                                        modelPath: string

                                                        path to model json file (relative to modelBasePath

                                                        +
                                                        skipFrames: number

                                                        how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                        -

                                                        skipTime

                                                        skipTime: number

                                                        how many max milliseconds to go without re-running model if cached results are acceptable +

                                                        skipTime: number

                                                        how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                        -

                                                        Settings

                                                        Member Visibility

                                                        On This Page

                                                        Properties
                                                        +

                                                        Settings

                                                        Member Visibility

                                                        On This Page

                                                        Properties
                                                        diff --git a/typedoc/interfaces/FaceAttentionConfig.html b/typedoc/interfaces/FaceAttentionConfig.html index 29725aad..8f3c4d23 100644 --- a/typedoc/interfaces/FaceAttentionConfig.html +++ b/typedoc/interfaces/FaceAttentionConfig.html @@ -1,12 +1,12 @@ -FaceAttentionConfig | @vladmandic/human - v3.3.5

                                                        Interface FaceAttentionConfig

                                                        Attention part of face configuration

                                                        -
                                                        interface FaceAttentionConfig {
                                                            enabled: boolean;
                                                            modelPath: string;
                                                            skipFrames: number;
                                                            skipTime: number;
                                                        }

                                                        Hierarchy (View Summary)

                                                        Properties

                                                        enabled -modelPath -skipFrames -skipTime -

                                                        Properties

                                                        enabled

                                                        enabled: boolean

                                                        is module enabled?

                                                        -

                                                        modelPath

                                                        modelPath: string

                                                        path to model json file (relative to modelBasePath

                                                        -

                                                        skipFrames

                                                        skipFrames: number

                                                        how many max frames to go without re-running model if cached results are acceptable +FaceAttentionConfig | @vladmandic/human - v3.3.6

                                                        @vladmandic/human - v3.3.6
                                                          Preparing search index...

                                                          Interface FaceAttentionConfig

                                                          Attention part of face configuration

                                                          +
                                                          interface FaceAttentionConfig {
                                                              enabled: boolean;
                                                              modelPath: string;
                                                              skipFrames: number;
                                                              skipTime: number;
                                                          }

                                                          Hierarchy (View Summary)

                                                          Index

                                                          Properties

                                                          enabled +modelPath +skipFrames +skipTime +

                                                          Properties

                                                          enabled: boolean

                                                          is module enabled?

                                                          +
                                                          modelPath: string

                                                          path to model json file (relative to modelBasePath

                                                          +
                                                          skipFrames: number

                                                          how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                          -

                                                          skipTime

                                                          skipTime: number

                                                          how many max milliseconds to go without re-running model if cached results are acceptable +

                                                          skipTime: number

                                                          how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                          -

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                                                          diff --git a/typedoc/interfaces/FaceConfig.html b/typedoc/interfaces/FaceConfig.html index 0252a354..2ca90bcf 100644 --- a/typedoc/interfaces/FaceConfig.html +++ b/typedoc/interfaces/FaceConfig.html @@ -1,21 +1,21 @@ -FaceConfig | @vladmandic/human - v3.3.5

                                                          Interface FaceConfig

                                                          Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description

                                                          -
                                                          interface FaceConfig {
                                                              antispoof: Partial<FaceAntiSpoofConfig>;
                                                              attention: Partial<FaceAttentionConfig>;
                                                              description: Partial<FaceDescriptionConfig>;
                                                              detector: Partial<FaceDetectorConfig>;
                                                              emotion: Partial<FaceEmotionConfig>;
                                                              enabled: boolean;
                                                              gear: Partial<FaceGearConfig>;
                                                              iris: Partial<FaceIrisConfig>;
                                                              liveness: Partial<FaceLivenessConfig>;
                                                              mesh: Partial<FaceMeshConfig>;
                                                              modelPath: string;
                                                              skipFrames: number;
                                                              skipTime: number;
                                                          }

                                                          Hierarchy (View Summary)

                                                          Properties

                                                          antispoof -attention -description -detector -emotion -enabled -gear -iris -liveness -mesh -modelPath -skipFrames -skipTime -

                                                          Properties

                                                          antispoof

                                                          antispoof: Partial<FaceAntiSpoofConfig>

                                                          attention

                                                          attention: Partial<FaceAttentionConfig>

                                                          description

                                                          description: Partial<FaceDescriptionConfig>

                                                          detector

                                                          detector: Partial<FaceDetectorConfig>

                                                          emotion

                                                          emotion: Partial<FaceEmotionConfig>

                                                          enabled

                                                          enabled: boolean

                                                          is module enabled?

                                                          -

                                                          gear

                                                          gear: Partial<FaceGearConfig>

                                                          iris

                                                          iris: Partial<FaceIrisConfig>

                                                          liveness

                                                          liveness: Partial<FaceLivenessConfig>

                                                          mesh

                                                          mesh: Partial<FaceMeshConfig>

                                                          modelPath

                                                          modelPath: string

                                                          path to model json file (relative to modelBasePath

                                                          -

                                                          skipFrames

                                                          skipFrames: number

                                                          how many max frames to go without re-running model if cached results are acceptable +FaceConfig | @vladmandic/human - v3.3.6

                                                          @vladmandic/human - v3.3.6
                                                            Preparing search index...

                                                            Interface FaceConfig

                                                            Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description

                                                            +
                                                            interface FaceConfig {
                                                                antispoof: Partial<FaceAntiSpoofConfig>;
                                                                attention: Partial<FaceAttentionConfig>;
                                                                description: Partial<FaceDescriptionConfig>;
                                                                detector: Partial<FaceDetectorConfig>;
                                                                emotion: Partial<FaceEmotionConfig>;
                                                                enabled: boolean;
                                                                gear: Partial<FaceGearConfig>;
                                                                iris: Partial<FaceIrisConfig>;
                                                                liveness: Partial<FaceLivenessConfig>;
                                                                mesh: Partial<FaceMeshConfig>;
                                                                modelPath: string;
                                                                skipFrames: number;
                                                                skipTime: number;
                                                            }

                                                            Hierarchy (View Summary)

                                                            Index

                                                            Properties

                                                            antispoof +attention +description +detector +emotion +enabled +gear +iris +liveness +mesh +modelPath +skipFrames +skipTime +

                                                            Properties

                                                            antispoof: Partial<FaceAntiSpoofConfig>
                                                            attention: Partial<FaceAttentionConfig>
                                                            description: Partial<FaceDescriptionConfig>
                                                            detector: Partial<FaceDetectorConfig>
                                                            emotion: Partial<FaceEmotionConfig>
                                                            enabled: boolean

                                                            is module enabled?

                                                            +
                                                            gear: Partial<FaceGearConfig>
                                                            iris: Partial<FaceIrisConfig>
                                                            liveness: Partial<FaceLivenessConfig>
                                                            mesh: Partial<FaceMeshConfig>
                                                            modelPath: string

                                                            path to model json file (relative to modelBasePath

                                                            +
                                                            skipFrames: number

                                                            how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                            -

                                                            skipTime

                                                            skipTime: number

                                                            how many max milliseconds to go without re-running model if cached results are acceptable +

                                                            skipTime: number

                                                            how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                            -

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                                                            diff --git a/typedoc/interfaces/FaceDescriptionConfig.html b/typedoc/interfaces/FaceDescriptionConfig.html index a977bd76..7c184e05 100644 --- a/typedoc/interfaces/FaceDescriptionConfig.html +++ b/typedoc/interfaces/FaceDescriptionConfig.html @@ -1,17 +1,17 @@ -FaceDescriptionConfig | @vladmandic/human - v3.3.5

                                                            Interface FaceDescriptionConfig

                                                            Description or face embedding part of face configuration

                                                            +FaceDescriptionConfig | @vladmandic/human - v3.3.6
                                                            @vladmandic/human - v3.3.6
                                                              Preparing search index...

                                                              Interface FaceDescriptionConfig

                                                              Description or face embedding part of face configuration

                                                              • also used by age and gender detection
                                                              -
                                                              interface FaceDescriptionConfig {
                                                                  enabled: boolean;
                                                                  minConfidence: number;
                                                                  modelPath: string;
                                                                  skipFrames: number;
                                                                  skipTime: number;
                                                              }

                                                              Hierarchy (View Summary)

                                                              Properties

                                                              enabled -minConfidence -modelPath -skipFrames -skipTime -

                                                              Properties

                                                              enabled

                                                              enabled: boolean

                                                              is module enabled?

                                                              -

                                                              minConfidence

                                                              minConfidence: number

                                                              minimum confidence for a detected face before results are discarded

                                                              -

                                                              modelPath

                                                              modelPath: string

                                                              path to model json file (relative to modelBasePath

                                                              -

                                                              skipFrames

                                                              skipFrames: number

                                                              how many max frames to go without re-running model if cached results are acceptable +

                                                              interface FaceDescriptionConfig {
                                                                  enabled: boolean;
                                                                  minConfidence: number;
                                                                  modelPath: string;
                                                                  skipFrames: number;
                                                                  skipTime: number;
                                                              }

                                                              Hierarchy (View Summary)

                                                              Index

                                                              Properties

                                                              enabled +minConfidence +modelPath +skipFrames +skipTime +

                                                              Properties

                                                              enabled: boolean

                                                              is module enabled?

                                                              +
                                                              minConfidence: number

                                                              minimum confidence for a detected face before results are discarded

                                                              +
                                                              modelPath: string

                                                              path to model json file (relative to modelBasePath

                                                              +
                                                              skipFrames: number

                                                              how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                              -

                                                              skipTime

                                                              skipTime: number

                                                              how many max milliseconds to go without re-running model if cached results are acceptable +

                                                              skipTime: number

                                                              how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                              -

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                                                              diff --git a/typedoc/interfaces/FaceDetectorConfig.html b/typedoc/interfaces/FaceDetectorConfig.html index 19a4eeb2..5b98ecd2 100644 --- a/typedoc/interfaces/FaceDetectorConfig.html +++ b/typedoc/interfaces/FaceDetectorConfig.html @@ -1,32 +1,32 @@ -FaceDetectorConfig | @vladmandic/human - v3.3.5

                                                              Interface FaceDetectorConfig

                                                              Detector part of face configuration

                                                              -
                                                              interface FaceDetectorConfig {
                                                                  enabled: boolean;
                                                                  iouThreshold: number;
                                                                  mask: boolean;
                                                                  maxDetected: number;
                                                                  minConfidence: number;
                                                                  minSize: number;
                                                                  modelPath: string;
                                                                  return: boolean;
                                                                  rotation: boolean;
                                                                  scale: number;
                                                                  skipFrames: number;
                                                                  skipTime: number;
                                                                  square: boolean;
                                                              }

                                                              Hierarchy (View Summary)

                                                              Properties

                                                              enabled -iouThreshold -mask -maxDetected -minConfidence -minSize -modelPath -return -rotation -scale -skipFrames -skipTime -square -

                                                              Properties

                                                              enabled

                                                              enabled: boolean

                                                              is module enabled?

                                                              -

                                                              iouThreshold

                                                              iouThreshold: number

                                                              minimum overlap between two detected faces before one is discarded

                                                              -

                                                              mask

                                                              mask: boolean

                                                              should child models perform on masked image of a face

                                                              -

                                                              maxDetected

                                                              maxDetected: number

                                                              maximum number of detected faces

                                                              -

                                                              minConfidence

                                                              minConfidence: number

                                                              minimum confidence for a detected face before results are discarded

                                                              -

                                                              minSize

                                                              minSize: number

                                                              minimum size in pixels of a detected face box before resutls are discared

                                                              -

                                                              modelPath

                                                              modelPath: string

                                                              path to model json file (relative to modelBasePath

                                                              -

                                                              return

                                                              return: boolean

                                                              should face detection return processed and cropped face tensor that can with an external model for addtional processing? +FaceDetectorConfig | @vladmandic/human - v3.3.6

                                                              @vladmandic/human - v3.3.6
                                                                Preparing search index...

                                                                Interface FaceDetectorConfig

                                                                Detector part of face configuration

                                                                +
                                                                interface FaceDetectorConfig {
                                                                    enabled: boolean;
                                                                    iouThreshold: number;
                                                                    mask: boolean;
                                                                    maxDetected: number;
                                                                    minConfidence: number;
                                                                    minSize: number;
                                                                    modelPath: string;
                                                                    return: boolean;
                                                                    rotation: boolean;
                                                                    scale: number;
                                                                    skipFrames: number;
                                                                    skipTime: number;
                                                                    square: boolean;
                                                                }

                                                                Hierarchy (View Summary)

                                                                Index

                                                                Properties

                                                                enabled +iouThreshold +mask +maxDetected +minConfidence +minSize +modelPath +return +rotation +scale +skipFrames +skipTime +square +

                                                                Properties

                                                                enabled: boolean

                                                                is module enabled?

                                                                +
                                                                iouThreshold: number

                                                                minimum overlap between two detected faces before one is discarded

                                                                +
                                                                mask: boolean

                                                                should child models perform on masked image of a face

                                                                +
                                                                maxDetected: number

                                                                maximum number of detected faces

                                                                +
                                                                minConfidence: number

                                                                minimum confidence for a detected face before results are discarded

                                                                +
                                                                minSize: number

                                                                minimum size in pixels of a detected face box before resutls are discared

                                                                +
                                                                modelPath: string

                                                                path to model json file (relative to modelBasePath

                                                                +
                                                                return: boolean

                                                                should face detection return processed and cropped face tensor that can with an external model for addtional processing? if enabled it must be manually deallocated to avoid memory leak

                                                                -

                                                                rotation

                                                                rotation: boolean

                                                                is face rotation correction performed after detecting face? +

                                                                rotation: boolean

                                                                is face rotation correction performed after detecting face? used to correctly analyze faces under high angles

                                                                -

                                                                scale

                                                                scale: number

                                                                how much should face box be enlarged over the min/max facial coordinates

                                                                -

                                                                skipFrames

                                                                skipFrames: number

                                                                how many max frames to go without re-running model if cached results are acceptable +

                                                                scale: number

                                                                how much should face box be enlarged over the min/max facial coordinates

                                                                +
                                                                skipFrames: number

                                                                how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                -

                                                                skipTime

                                                                skipTime: number

                                                                how many max milliseconds to go without re-running model if cached results are acceptable +

                                                                skipTime: number

                                                                how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                -

                                                                square

                                                                square: boolean

                                                                automatically pad image to square

                                                                -

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                                                                +
                                                                square: boolean

                                                                automatically pad image to square

                                                                +

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                                                                diff --git a/typedoc/interfaces/FaceEmotionConfig.html b/typedoc/interfaces/FaceEmotionConfig.html index 29661ba5..2e77ad38 100644 --- a/typedoc/interfaces/FaceEmotionConfig.html +++ b/typedoc/interfaces/FaceEmotionConfig.html @@ -1,14 +1,14 @@ -FaceEmotionConfig | @vladmandic/human - v3.3.5

                                                                Interface FaceEmotionConfig

                                                                Emotion part of face configuration

                                                                -
                                                                interface FaceEmotionConfig {
                                                                    enabled: boolean;
                                                                    minConfidence: number;
                                                                    modelPath: string;
                                                                    skipFrames: number;
                                                                    skipTime: number;
                                                                }

                                                                Hierarchy (View Summary)

                                                                Properties

                                                                enabled -minConfidence -modelPath -skipFrames -skipTime -

                                                                Properties

                                                                enabled

                                                                enabled: boolean

                                                                is module enabled?

                                                                -

                                                                minConfidence

                                                                minConfidence: number

                                                                minimum confidence for a detected face before results are discarded

                                                                -

                                                                modelPath

                                                                modelPath: string

                                                                path to model json file (relative to modelBasePath

                                                                -

                                                                skipFrames

                                                                skipFrames: number

                                                                how many max frames to go without re-running model if cached results are acceptable +FaceEmotionConfig | @vladmandic/human - v3.3.6

                                                                @vladmandic/human - v3.3.6
                                                                  Preparing search index...

                                                                  Interface FaceEmotionConfig

                                                                  Emotion part of face configuration

                                                                  +
                                                                  interface FaceEmotionConfig {
                                                                      enabled: boolean;
                                                                      minConfidence: number;
                                                                      modelPath: string;
                                                                      skipFrames: number;
                                                                      skipTime: number;
                                                                  }

                                                                  Hierarchy (View Summary)

                                                                  Index

                                                                  Properties

                                                                  enabled +minConfidence +modelPath +skipFrames +skipTime +

                                                                  Properties

                                                                  enabled: boolean

                                                                  is module enabled?

                                                                  +
                                                                  minConfidence: number

                                                                  minimum confidence for a detected face before results are discarded

                                                                  +
                                                                  modelPath: string

                                                                  path to model json file (relative to modelBasePath

                                                                  +
                                                                  skipFrames: number

                                                                  how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                  -

                                                                  skipTime

                                                                  skipTime: number

                                                                  how many max milliseconds to go without re-running model if cached results are acceptable +

                                                                  skipTime: number

                                                                  how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                  -

                                                                  Settings

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                                                                  +

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                                                                  diff --git a/typedoc/interfaces/FaceGearConfig.html b/typedoc/interfaces/FaceGearConfig.html index d07f94c8..63080b5e 100644 --- a/typedoc/interfaces/FaceGearConfig.html +++ b/typedoc/interfaces/FaceGearConfig.html @@ -1,14 +1,14 @@ -FaceGearConfig | @vladmandic/human - v3.3.5

                                                                  Interface FaceGearConfig

                                                                  Gear part of face configuration

                                                                  -
                                                                  interface FaceGearConfig {
                                                                      enabled: boolean;
                                                                      minConfidence: number;
                                                                      modelPath: string;
                                                                      skipFrames: number;
                                                                      skipTime: number;
                                                                  }

                                                                  Hierarchy (View Summary)

                                                                  Properties

                                                                  enabled -minConfidence -modelPath -skipFrames -skipTime -

                                                                  Properties

                                                                  enabled

                                                                  enabled: boolean

                                                                  is module enabled?

                                                                  -

                                                                  minConfidence

                                                                  minConfidence: number

                                                                  minimum confidence for a detected race before results are discarded

                                                                  -

                                                                  modelPath

                                                                  modelPath: string

                                                                  path to model json file (relative to modelBasePath

                                                                  -

                                                                  skipFrames

                                                                  skipFrames: number

                                                                  how many max frames to go without re-running model if cached results are acceptable +FaceGearConfig | @vladmandic/human - v3.3.6

                                                                  @vladmandic/human - v3.3.6
                                                                    Preparing search index...

                                                                    Interface FaceGearConfig

                                                                    Gear part of face configuration

                                                                    +
                                                                    interface FaceGearConfig {
                                                                        enabled: boolean;
                                                                        minConfidence: number;
                                                                        modelPath: string;
                                                                        skipFrames: number;
                                                                        skipTime: number;
                                                                    }

                                                                    Hierarchy (View Summary)

                                                                    Index

                                                                    Properties

                                                                    enabled +minConfidence +modelPath +skipFrames +skipTime +

                                                                    Properties

                                                                    enabled: boolean

                                                                    is module enabled?

                                                                    +
                                                                    minConfidence: number

                                                                    minimum confidence for a detected race before results are discarded

                                                                    +
                                                                    modelPath: string

                                                                    path to model json file (relative to modelBasePath

                                                                    +
                                                                    skipFrames: number

                                                                    how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                    -

                                                                    skipTime

                                                                    skipTime: number

                                                                    how many max milliseconds to go without re-running model if cached results are acceptable +

                                                                    skipTime: number

                                                                    how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                    -

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                                                                    diff --git a/typedoc/interfaces/FaceIrisConfig.html b/typedoc/interfaces/FaceIrisConfig.html index 1cb76d7c..aaa21379 100644 --- a/typedoc/interfaces/FaceIrisConfig.html +++ b/typedoc/interfaces/FaceIrisConfig.html @@ -1,14 +1,14 @@ -FaceIrisConfig | @vladmandic/human - v3.3.5

                                                                    Interface FaceIrisConfig

                                                                    Iris part of face configuration

                                                                    -
                                                                    interface FaceIrisConfig {
                                                                        enabled: boolean;
                                                                        modelPath: string;
                                                                        scale: number;
                                                                        skipFrames: number;
                                                                        skipTime: number;
                                                                    }

                                                                    Hierarchy (View Summary)

                                                                    Properties

                                                                    enabled -modelPath -scale -skipFrames -skipTime -

                                                                    Properties

                                                                    enabled

                                                                    enabled: boolean

                                                                    is module enabled?

                                                                    -

                                                                    modelPath

                                                                    modelPath: string

                                                                    path to model json file (relative to modelBasePath

                                                                    -

                                                                    scale

                                                                    scale: number

                                                                    how much should iris box be enlarged over the min/max iris coordinates

                                                                    -

                                                                    skipFrames

                                                                    skipFrames: number

                                                                    how many max frames to go without re-running model if cached results are acceptable +FaceIrisConfig | @vladmandic/human - v3.3.6

                                                                    @vladmandic/human - v3.3.6
                                                                      Preparing search index...

                                                                      Interface FaceIrisConfig

                                                                      Iris part of face configuration

                                                                      +
                                                                      interface FaceIrisConfig {
                                                                          enabled: boolean;
                                                                          modelPath: string;
                                                                          scale: number;
                                                                          skipFrames: number;
                                                                          skipTime: number;
                                                                      }

                                                                      Hierarchy (View Summary)

                                                                      Index

                                                                      Properties

                                                                      enabled +modelPath +scale +skipFrames +skipTime +

                                                                      Properties

                                                                      enabled: boolean

                                                                      is module enabled?

                                                                      +
                                                                      modelPath: string

                                                                      path to model json file (relative to modelBasePath

                                                                      +
                                                                      scale: number

                                                                      how much should iris box be enlarged over the min/max iris coordinates

                                                                      +
                                                                      skipFrames: number

                                                                      how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                      -

                                                                      skipTime

                                                                      skipTime: number

                                                                      how many max milliseconds to go without re-running model if cached results are acceptable +

                                                                      skipTime: number

                                                                      how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                      -

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                                                                      diff --git a/typedoc/interfaces/FaceLivenessConfig.html b/typedoc/interfaces/FaceLivenessConfig.html index a1475c0c..0b2a7416 100644 --- a/typedoc/interfaces/FaceLivenessConfig.html +++ b/typedoc/interfaces/FaceLivenessConfig.html @@ -1,12 +1,12 @@ -FaceLivenessConfig | @vladmandic/human - v3.3.5

                                                                      Interface FaceLivenessConfig

                                                                      Liveness part of face configuration

                                                                      -
                                                                      interface FaceLivenessConfig {
                                                                          enabled: boolean;
                                                                          modelPath: string;
                                                                          skipFrames: number;
                                                                          skipTime: number;
                                                                      }

                                                                      Hierarchy (View Summary)

                                                                      Properties

                                                                      enabled -modelPath -skipFrames -skipTime -

                                                                      Properties

                                                                      enabled

                                                                      enabled: boolean

                                                                      is module enabled?

                                                                      -

                                                                      modelPath

                                                                      modelPath: string

                                                                      path to model json file (relative to modelBasePath

                                                                      -

                                                                      skipFrames

                                                                      skipFrames: number

                                                                      how many max frames to go without re-running model if cached results are acceptable +FaceLivenessConfig | @vladmandic/human - v3.3.6

                                                                      @vladmandic/human - v3.3.6
                                                                        Preparing search index...

                                                                        Interface FaceLivenessConfig

                                                                        Liveness part of face configuration

                                                                        +
                                                                        interface FaceLivenessConfig {
                                                                            enabled: boolean;
                                                                            modelPath: string;
                                                                            skipFrames: number;
                                                                            skipTime: number;
                                                                        }

                                                                        Hierarchy (View Summary)

                                                                        Index

                                                                        Properties

                                                                        enabled +modelPath +skipFrames +skipTime +

                                                                        Properties

                                                                        enabled: boolean

                                                                        is module enabled?

                                                                        +
                                                                        modelPath: string

                                                                        path to model json file (relative to modelBasePath

                                                                        +
                                                                        skipFrames: number

                                                                        how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                        -

                                                                        skipTime

                                                                        skipTime: number

                                                                        how many max milliseconds to go without re-running model if cached results are acceptable +

                                                                        skipTime: number

                                                                        how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                        -

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                                                                        diff --git a/typedoc/interfaces/FaceMeshConfig.html b/typedoc/interfaces/FaceMeshConfig.html index 9cc4c27f..f320094c 100644 --- a/typedoc/interfaces/FaceMeshConfig.html +++ b/typedoc/interfaces/FaceMeshConfig.html @@ -1,14 +1,14 @@ -FaceMeshConfig | @vladmandic/human - v3.3.5

                                                                        Interface FaceMeshConfig

                                                                        Mesh part of face configuration

                                                                        -
                                                                        interface FaceMeshConfig {
                                                                            enabled: boolean;
                                                                            keepInvalid: boolean;
                                                                            modelPath: string;
                                                                            skipFrames: number;
                                                                            skipTime: number;
                                                                        }

                                                                        Hierarchy (View Summary)

                                                                        Properties

                                                                        enabled -keepInvalid -modelPath -skipFrames -skipTime -

                                                                        Properties

                                                                        enabled

                                                                        enabled: boolean

                                                                        is module enabled?

                                                                        -

                                                                        keepInvalid

                                                                        keepInvalid: boolean

                                                                        Keep detected faces that cannot be verified using facemesh

                                                                        -

                                                                        modelPath

                                                                        modelPath: string

                                                                        path to model json file (relative to modelBasePath

                                                                        -

                                                                        skipFrames

                                                                        skipFrames: number

                                                                        how many max frames to go without re-running model if cached results are acceptable +FaceMeshConfig | @vladmandic/human - v3.3.6

                                                                        @vladmandic/human - v3.3.6
                                                                          Preparing search index...

                                                                          Interface FaceMeshConfig

                                                                          Mesh part of face configuration

                                                                          +
                                                                          interface FaceMeshConfig {
                                                                              enabled: boolean;
                                                                              keepInvalid: boolean;
                                                                              modelPath: string;
                                                                              skipFrames: number;
                                                                              skipTime: number;
                                                                          }

                                                                          Hierarchy (View Summary)

                                                                          Index

                                                                          Properties

                                                                          enabled +keepInvalid +modelPath +skipFrames +skipTime +

                                                                          Properties

                                                                          enabled: boolean

                                                                          is module enabled?

                                                                          +
                                                                          keepInvalid: boolean

                                                                          Keep detected faces that cannot be verified using facemesh

                                                                          +
                                                                          modelPath: string

                                                                          path to model json file (relative to modelBasePath

                                                                          +
                                                                          skipFrames: number

                                                                          how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                          -

                                                                          skipTime

                                                                          skipTime: number

                                                                          how many max milliseconds to go without re-running model if cached results are acceptable +

                                                                          skipTime: number

                                                                          how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                          -

                                                                          Settings

                                                                          Member Visibility

                                                                          On This Page

                                                                          Properties
                                                                          +

                                                                          Settings

                                                                          Member Visibility

                                                                          On This Page

                                                                          Properties
                                                                          diff --git a/typedoc/interfaces/FaceResult.html b/typedoc/interfaces/FaceResult.html index fac2cf60..d96276af 100644 --- a/typedoc/interfaces/FaceResult.html +++ b/typedoc/interfaces/FaceResult.html @@ -1,48 +1,48 @@ -FaceResult | @vladmandic/human - v3.3.5

                                                                          Interface FaceResult

                                                                          Face results

                                                                          +FaceResult | @vladmandic/human - v3.3.6
                                                                          @vladmandic/human - v3.3.6
                                                                            Preparing search index...

                                                                            Interface FaceResult

                                                                            Face results

                                                                            • Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models
                                                                            • Some values may be null if specific model is not enabled
                                                                            -
                                                                            interface FaceResult {
                                                                                age?: number;
                                                                                annotations: Record<FaceLandmark, Point[]>;
                                                                                box: Box;
                                                                                boxRaw: Box;
                                                                                boxScore: number;
                                                                                distance?: number;
                                                                                embedding?: number[];
                                                                                emotion?: { emotion: Emotion; score: number }[];
                                                                                faceScore: number;
                                                                                gender?: Gender;
                                                                                genderScore?: number;
                                                                                id: number;
                                                                                live?: number;
                                                                                mesh: Point[];
                                                                                meshRaw: Point[];
                                                                                race?: { race: Race; score: number }[];
                                                                                real?: number;
                                                                                rotation?:
                                                                                    | null
                                                                                    | {
                                                                                        angle: { pitch: number; roll: number; yaw: number };
                                                                                        gaze: { bearing: number; strength: number };
                                                                                        matrix: [
                                                                                            number,
                                                                                            number,
                                                                                            number,
                                                                                            number,
                                                                                            number,
                                                                                            number,
                                                                                            number,
                                                                                            number,
                                                                                            number,
                                                                                        ];
                                                                                    };
                                                                                score: number;
                                                                                size: [number, number];
                                                                                tensor?: Tensor;
                                                                            }

                                                                            Properties

                                                                            age? -annotations -box -boxRaw -boxScore -distance? -embedding? -emotion? -faceScore -gender? -genderScore? -id -live? -mesh -meshRaw -race? -real? -rotation? -score -size -tensor? -

                                                                            Properties

                                                                            Optionalage

                                                                            age?: number

                                                                            detected age

                                                                            -

                                                                            annotations

                                                                            annotations: Record<FaceLandmark, Point[]>

                                                                            mesh keypoints combined into annotated results

                                                                            -

                                                                            box

                                                                            box: Box

                                                                            detected face box

                                                                            -

                                                                            boxRaw

                                                                            boxRaw: Box

                                                                            detected face box normalized to 0..1

                                                                            -

                                                                            boxScore

                                                                            boxScore: number

                                                                            detection score

                                                                            -

                                                                            Optionaldistance

                                                                            distance?: number

                                                                            face distance from camera

                                                                            -

                                                                            Optionalembedding

                                                                            embedding?: number[]

                                                                            face descriptor

                                                                            -

                                                                            Optionalemotion

                                                                            emotion?: { emotion: Emotion; score: number }[]

                                                                            detected emotions

                                                                            -

                                                                            faceScore

                                                                            faceScore: number

                                                                            mesh score

                                                                            -

                                                                            Optionalgender

                                                                            gender?: Gender

                                                                            detected gender

                                                                            -

                                                                            OptionalgenderScore

                                                                            genderScore?: number

                                                                            gender detection score

                                                                            -

                                                                            id

                                                                            id: number

                                                                            face id

                                                                            -

                                                                            Optionallive

                                                                            live?: number

                                                                            face liveness result confidence

                                                                            -

                                                                            mesh

                                                                            mesh: Point[]

                                                                            detected face mesh

                                                                            -

                                                                            meshRaw

                                                                            meshRaw: Point[]

                                                                            detected face mesh normalized to 0..1

                                                                            -

                                                                            Optionalrace

                                                                            race?: { race: Race; score: number }[]

                                                                            detected race

                                                                            -

                                                                            Optionalreal

                                                                            real?: number

                                                                            face anti-spoofing result confidence

                                                                            -

                                                                            Optionalrotation

                                                                            rotation?:
                                                                                | null
                                                                                | {
                                                                                    angle: { pitch: number; roll: number; yaw: number };
                                                                                    gaze: { bearing: number; strength: number };
                                                                                    matrix: [
                                                                                        number,
                                                                                        number,
                                                                                        number,
                                                                                        number,
                                                                                        number,
                                                                                        number,
                                                                                        number,
                                                                                        number,
                                                                                        number,
                                                                                    ];
                                                                                }

                                                                            face rotation details

                                                                            -

                                                                            score

                                                                            score: number

                                                                            overall face score

                                                                            -

                                                                            size

                                                                            size: [number, number]

                                                                            detected face box size

                                                                            -

                                                                            Optionaltensor

                                                                            tensor?: Tensor

                                                                            detected face as tensor that can be used in further pipelines

                                                                            -

                                                                            Settings

                                                                            Member Visibility

                                                                            On This Page

                                                                            Properties
                                                                            +
                                                                            interface FaceResult {
                                                                                age?: number;
                                                                                annotations: Record<FaceLandmark, Point[]>;
                                                                                box: Box;
                                                                                boxRaw: Box;
                                                                                boxScore: number;
                                                                                distance?: number;
                                                                                embedding?: number[];
                                                                                emotion?: { emotion: Emotion; score: number }[];
                                                                                faceScore: number;
                                                                                gender?: Gender;
                                                                                genderScore?: number;
                                                                                id: number;
                                                                                live?: number;
                                                                                mesh: Point[];
                                                                                meshRaw: Point[];
                                                                                race?: { race: Race; score: number }[];
                                                                                real?: number;
                                                                                rotation?:
                                                                                    | null
                                                                                    | {
                                                                                        angle: { pitch: number; roll: number; yaw: number };
                                                                                        gaze: { bearing: number; strength: number };
                                                                                        matrix: [
                                                                                            number,
                                                                                            number,
                                                                                            number,
                                                                                            number,
                                                                                            number,
                                                                                            number,
                                                                                            number,
                                                                                            number,
                                                                                            number,
                                                                                        ];
                                                                                    };
                                                                                score: number;
                                                                                size: [number, number];
                                                                                tensor?: Tensor<Rank>;
                                                                            }
                                                                            Index

                                                                            Properties

                                                                            age? +annotations +box +boxRaw +boxScore +distance? +embedding? +emotion? +faceScore +gender? +genderScore? +id +live? +mesh +meshRaw +race? +real? +rotation? +score +size +tensor? +

                                                                            Properties

                                                                            age?: number

                                                                            detected age

                                                                            +
                                                                            annotations: Record<FaceLandmark, Point[]>

                                                                            mesh keypoints combined into annotated results

                                                                            +
                                                                            box: Box

                                                                            detected face box

                                                                            +
                                                                            boxRaw: Box

                                                                            detected face box normalized to 0..1

                                                                            +
                                                                            boxScore: number

                                                                            detection score

                                                                            +
                                                                            distance?: number

                                                                            face distance from camera

                                                                            +
                                                                            embedding?: number[]

                                                                            face descriptor

                                                                            +
                                                                            emotion?: { emotion: Emotion; score: number }[]

                                                                            detected emotions

                                                                            +
                                                                            faceScore: number

                                                                            mesh score

                                                                            +
                                                                            gender?: Gender

                                                                            detected gender

                                                                            +
                                                                            genderScore?: number

                                                                            gender detection score

                                                                            +
                                                                            id: number

                                                                            face id

                                                                            +
                                                                            live?: number

                                                                            face liveness result confidence

                                                                            +
                                                                            mesh: Point[]

                                                                            detected face mesh

                                                                            +
                                                                            meshRaw: Point[]

                                                                            detected face mesh normalized to 0..1

                                                                            +
                                                                            race?: { race: Race; score: number }[]

                                                                            detected race

                                                                            +
                                                                            real?: number

                                                                            face anti-spoofing result confidence

                                                                            +
                                                                            rotation?:
                                                                                | null
                                                                                | {
                                                                                    angle: { pitch: number; roll: number; yaw: number };
                                                                                    gaze: { bearing: number; strength: number };
                                                                                    matrix: [
                                                                                        number,
                                                                                        number,
                                                                                        number,
                                                                                        number,
                                                                                        number,
                                                                                        number,
                                                                                        number,
                                                                                        number,
                                                                                        number,
                                                                                    ];
                                                                                }

                                                                            face rotation details

                                                                            +
                                                                            score: number

                                                                            overall face score

                                                                            +
                                                                            size: [number, number]

                                                                            detected face box size

                                                                            +
                                                                            tensor?: Tensor<Rank>

                                                                            detected face as tensor that can be used in further pipelines

                                                                            +

                                                                            Settings

                                                                            Member Visibility

                                                                            On This Page

                                                                            Properties
                                                                            diff --git a/typedoc/interfaces/FilterConfig.html b/typedoc/interfaces/FilterConfig.html index 16a7c031..0e50b7ed 100644 --- a/typedoc/interfaces/FilterConfig.html +++ b/typedoc/interfaces/FilterConfig.html @@ -1,59 +1,59 @@ -FilterConfig | @vladmandic/human - v3.3.5

                                                                            Interface FilterConfig

                                                                            Run input through image filters before inference

                                                                            +FilterConfig | @vladmandic/human - v3.3.6
                                                                            @vladmandic/human - v3.3.6
                                                                              Preparing search index...

                                                                              Interface FilterConfig

                                                                              Run input through image filters before inference

                                                                              • available only in Browser environments
                                                                              • image filters run with near-zero latency as they are executed on the GPU using WebGL
                                                                              -
                                                                              interface FilterConfig {
                                                                                  autoBrightness: boolean;
                                                                                  blur: number;
                                                                                  brightness: number;
                                                                                  contrast: number;
                                                                                  enabled: boolean;
                                                                                  equalization: boolean;
                                                                                  flip: boolean;
                                                                                  height: number;
                                                                                  hue: number;
                                                                                  kodachrome: boolean;
                                                                                  negative: boolean;
                                                                                  pixelate: number;
                                                                                  polaroid: boolean;
                                                                                  return: boolean;
                                                                                  saturation: number;
                                                                                  sepia: boolean;
                                                                                  sharpness: number;
                                                                                  technicolor: boolean;
                                                                                  vintage: boolean;
                                                                                  width: number;
                                                                              }

                                                                              Properties

                                                                              autoBrightness -blur -brightness -contrast -enabled -equalization -flip -height -hue -kodachrome -negative -pixelate -polaroid -return -saturation -sepia -sharpness -technicolor -vintage -width -

                                                                              Properties

                                                                              autoBrightness

                                                                              autoBrightness: boolean

                                                                              apply auto-brighness

                                                                              -

                                                                              blur

                                                                              blur: number

                                                                              range: 0 (no blur) to N (blur radius in pixels)

                                                                              -

                                                                              brightness

                                                                              brightness: number

                                                                              range: -1 (darken) to 1 (lighten)

                                                                              -

                                                                              contrast

                                                                              contrast: number

                                                                              range: -1 (reduce contrast) to 1 (increase contrast)

                                                                              -

                                                                              enabled

                                                                              enabled: boolean

                                                                              are image filters enabled?

                                                                              -

                                                                              equalization

                                                                              equalization: boolean

                                                                              perform image histogram equalization

                                                                              +
                                                                              interface FilterConfig {
                                                                                  autoBrightness: boolean;
                                                                                  blur: number;
                                                                                  brightness: number;
                                                                                  contrast: number;
                                                                                  enabled: boolean;
                                                                                  equalization: boolean;
                                                                                  flip: boolean;
                                                                                  height: number;
                                                                                  hue: number;
                                                                                  kodachrome: boolean;
                                                                                  negative: boolean;
                                                                                  pixelate: number;
                                                                                  polaroid: boolean;
                                                                                  return: boolean;
                                                                                  saturation: number;
                                                                                  sepia: boolean;
                                                                                  sharpness: number;
                                                                                  technicolor: boolean;
                                                                                  vintage: boolean;
                                                                                  width: number;
                                                                              }
                                                                              Index

                                                                              Properties

                                                                              autoBrightness +blur +brightness +contrast +enabled +equalization +flip +height +hue +kodachrome +negative +pixelate +polaroid +return +saturation +sepia +sharpness +technicolor +vintage +width +

                                                                              Properties

                                                                              autoBrightness: boolean

                                                                              apply auto-brighness

                                                                              +
                                                                              blur: number

                                                                              range: 0 (no blur) to N (blur radius in pixels)

                                                                              +
                                                                              brightness: number

                                                                              range: -1 (darken) to 1 (lighten)

                                                                              +
                                                                              contrast: number

                                                                              range: -1 (reduce contrast) to 1 (increase contrast)

                                                                              +
                                                                              enabled: boolean

                                                                              are image filters enabled?

                                                                              +
                                                                              equalization: boolean

                                                                              perform image histogram equalization

                                                                              • equalization is performed on input as a whole and detected face before its passed for further analysis
                                                                              -

                                                                              flip

                                                                              flip: boolean

                                                                              flip input as mirror image

                                                                              -

                                                                              height

                                                                              height: number

                                                                              resize input height

                                                                              +
                                                                              flip: boolean

                                                                              flip input as mirror image

                                                                              +
                                                                              height: number

                                                                              resize input height

                                                                              • if both width and height are set to 0, there is no resizing
                                                                              • if just one is set, second one is scaled automatically
                                                                              • if both are set, values are used as-is
                                                                              -

                                                                              hue

                                                                              hue: number

                                                                              range: 0 (no change) to 360 (hue rotation in degrees)

                                                                              -

                                                                              kodachrome

                                                                              kodachrome: boolean

                                                                              image kodachrome colors

                                                                              -

                                                                              negative

                                                                              negative: boolean

                                                                              image negative

                                                                              -

                                                                              pixelate

                                                                              pixelate: number

                                                                              range: 0 (no pixelate) to N (number of pixels to pixelate)

                                                                              -

                                                                              polaroid

                                                                              polaroid: boolean

                                                                              image polaroid camera effect

                                                                              -

                                                                              return

                                                                              return: boolean

                                                                              return processed canvas imagedata in result

                                                                              -

                                                                              saturation

                                                                              saturation: number

                                                                              range: -1 (reduce saturation) to 1 (increase saturation)

                                                                              -

                                                                              sepia

                                                                              sepia: boolean

                                                                              image sepia colors

                                                                              -

                                                                              sharpness

                                                                              sharpness: number

                                                                              range: 0 (no sharpening) to 1 (maximum sharpening)

                                                                              -

                                                                              technicolor

                                                                              technicolor: boolean

                                                                              image technicolor colors

                                                                              -

                                                                              vintage

                                                                              vintage: boolean

                                                                              image vintage colors

                                                                              -

                                                                              width

                                                                              width: number

                                                                              resize input width

                                                                              +
                                                                              hue: number

                                                                              range: 0 (no change) to 360 (hue rotation in degrees)

                                                                              +
                                                                              kodachrome: boolean

                                                                              image kodachrome colors

                                                                              +
                                                                              negative: boolean

                                                                              image negative

                                                                              +
                                                                              pixelate: number

                                                                              range: 0 (no pixelate) to N (number of pixels to pixelate)

                                                                              +
                                                                              polaroid: boolean

                                                                              image polaroid camera effect

                                                                              +
                                                                              return: boolean

                                                                              return processed canvas imagedata in result

                                                                              +
                                                                              saturation: number

                                                                              range: -1 (reduce saturation) to 1 (increase saturation)

                                                                              +
                                                                              sepia: boolean

                                                                              image sepia colors

                                                                              +
                                                                              sharpness: number

                                                                              range: 0 (no sharpening) to 1 (maximum sharpening)

                                                                              +
                                                                              technicolor: boolean

                                                                              image technicolor colors

                                                                              +
                                                                              vintage: boolean

                                                                              image vintage colors

                                                                              +
                                                                              width: number

                                                                              resize input width

                                                                              • if both width and height are set to 0, there is no resizing
                                                                              • if just one is set, second one is scaled automatically
                                                                              • if both are set, values are used as-is
                                                                              -

                                                                              Settings

                                                                              Member Visibility

                                                                              On This Page

                                                                              Properties
                                                                              +

                                                                              Settings

                                                                              Member Visibility

                                                                              On This Page

                                                                              Properties
                                                                              diff --git a/typedoc/interfaces/GenericConfig.html b/typedoc/interfaces/GenericConfig.html index 5406b10b..6940e147 100644 --- a/typedoc/interfaces/GenericConfig.html +++ b/typedoc/interfaces/GenericConfig.html @@ -1,12 +1,12 @@ -GenericConfig | @vladmandic/human - v3.3.5

                                                                              Interface GenericConfig

                                                                              Generic config type inherited by all module types

                                                                              -
                                                                              interface GenericConfig {
                                                                                  enabled: boolean;
                                                                                  modelPath: string;
                                                                                  skipFrames: number;
                                                                                  skipTime: number;
                                                                              }

                                                                              Hierarchy (View Summary)

                                                                              Properties

                                                                              enabled -modelPath -skipFrames -skipTime -

                                                                              Properties

                                                                              enabled

                                                                              enabled: boolean

                                                                              is module enabled?

                                                                              -

                                                                              modelPath

                                                                              modelPath: string

                                                                              path to model json file (relative to modelBasePath

                                                                              -

                                                                              skipFrames

                                                                              skipFrames: number

                                                                              how many max frames to go without re-running model if cached results are acceptable +GenericConfig | @vladmandic/human - v3.3.6

                                                                              @vladmandic/human - v3.3.6
                                                                                Preparing search index...

                                                                                Interface GenericConfig

                                                                                Generic config type inherited by all module types

                                                                                +
                                                                                interface GenericConfig {
                                                                                    enabled: boolean;
                                                                                    modelPath: string;
                                                                                    skipFrames: number;
                                                                                    skipTime: number;
                                                                                }

                                                                                Hierarchy (View Summary)

                                                                                Index

                                                                                Properties

                                                                                enabled +modelPath +skipFrames +skipTime +

                                                                                Properties

                                                                                enabled: boolean

                                                                                is module enabled?

                                                                                +
                                                                                modelPath: string

                                                                                path to model json file (relative to modelBasePath

                                                                                +
                                                                                skipFrames: number

                                                                                how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                                -

                                                                                skipTime

                                                                                skipTime: number

                                                                                how many max milliseconds to go without re-running model if cached results are acceptable +

                                                                                skipTime: number

                                                                                how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                                -

                                                                                Settings

                                                                                Member Visibility

                                                                                On This Page

                                                                                Properties
                                                                                +

                                                                                Settings

                                                                                Member Visibility

                                                                                On This Page

                                                                                Properties
                                                                                diff --git a/typedoc/interfaces/GestureConfig.html b/typedoc/interfaces/GestureConfig.html index 8740f05b..ac875754 100644 --- a/typedoc/interfaces/GestureConfig.html +++ b/typedoc/interfaces/GestureConfig.html @@ -1,4 +1,4 @@ -GestureConfig | @vladmandic/human - v3.3.5

                                                                                Interface GestureConfig

                                                                                Controlls gesture detection

                                                                                -
                                                                                interface GestureConfig {
                                                                                    enabled: boolean;
                                                                                }

                                                                                Properties

                                                                                enabled -

                                                                                Properties

                                                                                enabled

                                                                                enabled: boolean

                                                                                is gesture detection enabled?

                                                                                -

                                                                                Settings

                                                                                Member Visibility

                                                                                On This Page

                                                                                Properties
                                                                                +GestureConfig | @vladmandic/human - v3.3.6
                                                                                @vladmandic/human - v3.3.6
                                                                                  Preparing search index...

                                                                                  Interface GestureConfig

                                                                                  Controlls gesture detection

                                                                                  +
                                                                                  interface GestureConfig {
                                                                                      enabled: boolean;
                                                                                  }
                                                                                  Index

                                                                                  Properties

                                                                                  enabled +

                                                                                  Properties

                                                                                  enabled: boolean

                                                                                  is gesture detection enabled?

                                                                                  +

                                                                                  Settings

                                                                                  Member Visibility

                                                                                  On This Page

                                                                                  Properties
                                                                                  diff --git a/typedoc/interfaces/GraphModel.html b/typedoc/interfaces/GraphModel.html new file mode 100644 index 00000000..1caec52e --- /dev/null +++ b/typedoc/interfaces/GraphModel.html @@ -0,0 +1,126 @@ +GraphModel | @vladmandic/human - v3.3.6
                                                                                  @vladmandic/human - v3.3.6
                                                                                    Preparing search index...

                                                                                    Interface GraphModel<ModelURL>

                                                                                    A tf.GraphModel is a directed, acyclic graph built from a +SavedModel GraphDef and allows inference execution.

                                                                                    +

                                                                                    A tf.GraphModel can only be created by loading from a model converted from +a TensorFlow SavedModel using +the command line converter tool and loaded via tf.loadGraphModel.

                                                                                    +
                                                                                    interface GraphModel<ModelURL extends Url = string | io.IOHandler> {
                                                                                        get inputNodes(): string[];
                                                                                        get inputs(): TensorInfo[];
                                                                                        get metadata(): {};
                                                                                        get modelSignature(): {};
                                                                                        get modelStructuredOutputKeys(): {};
                                                                                        get modelVersion(): string;
                                                                                        get outputNodes(): string[];
                                                                                        get outputs(): TensorInfo[];
                                                                                        get weights(): NamedTensorsMap;
                                                                                        dispose(): void;
                                                                                        disposeIntermediateTensors(): void;
                                                                                        execute(
                                                                                            inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap,
                                                                                            outputs?: string | string[],
                                                                                        ): Tensor<Rank> | Tensor<Rank>[];
                                                                                        executeAsync(
                                                                                            inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap,
                                                                                            outputs?: string | string[],
                                                                                        ): Promise<Tensor<Rank> | Tensor<Rank>[]>;
                                                                                        getIntermediateTensors(): NamedTensorsMap;
                                                                                        load(): UrlIOHandler<ModelURL> extends IOHandlerSync
                                                                                            ? boolean
                                                                                            : Promise<boolean>;
                                                                                        loadSync(artifacts: ModelArtifacts): boolean;
                                                                                        predict(
                                                                                            inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap,
                                                                                            config?: ModelPredictConfig,
                                                                                        ): Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap;
                                                                                        predictAsync(
                                                                                            inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap,
                                                                                            config?: ModelPredictConfig,
                                                                                        ): Promise<Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap>;
                                                                                        save(
                                                                                            handlerOrURL: string | IOHandler,
                                                                                            config?: SaveConfig,
                                                                                        ): Promise<SaveResult>;
                                                                                    }

                                                                                    Type Parameters

                                                                                    • ModelURL extends Url = string | io.IOHandler

                                                                                    Implements

                                                                                    • InferenceModel
                                                                                    Index

                                                                                    Accessors

                                                                                    inputNodes +inputs +metadata +modelSignature +modelStructuredOutputKeys +modelVersion +outputNodes +outputs +weights +

                                                                                    Methods

                                                                                    dispose +disposeIntermediateTensors +execute +executeAsync +getIntermediateTensors +load +loadSync +predict +predictAsync +save +

                                                                                    Accessors

                                                                                    • get inputNodes(): string[]

                                                                                      Returns string[]

                                                                                    • get inputs(): TensorInfo[]

                                                                                      Return the array of input tensor info.

                                                                                      +

                                                                                      Returns TensorInfo[]

                                                                                    • get metadata(): {}

                                                                                      Returns {}

                                                                                    • get modelSignature(): {}

                                                                                      Returns {}

                                                                                    • get modelStructuredOutputKeys(): {}

                                                                                      Returns {}

                                                                                    • get modelVersion(): string

                                                                                      Returns string

                                                                                    • get outputNodes(): string[]

                                                                                      Returns string[]

                                                                                    • get outputs(): TensorInfo[]

                                                                                      Return the array of output tensor info.

                                                                                      +

                                                                                      Returns TensorInfo[]

                                                                                    • get weights(): NamedTensorsMap

                                                                                      Returns NamedTensorsMap

                                                                                    Methods

                                                                                    • Releases the memory used by the weight tensors and resourceManager.

                                                                                      +

                                                                                      Returns void

                                                                                    • Dispose intermediate tensors for model debugging mode (flag +KEEP_INTERMEDIATE_TENSORS is true).

                                                                                      +

                                                                                      Returns void

                                                                                    • Executes inference for the model for given input tensors.

                                                                                      +

                                                                                      Parameters

                                                                                      • inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap

                                                                                        tensor, tensor array or tensor map of the inputs for the +model, keyed by the input node names.

                                                                                        +
                                                                                      • Optionaloutputs: string | string[]

                                                                                        output node name from the TensorFlow model, if no +outputs are specified, the default outputs of the model would be used. +You can inspect intermediate nodes of the model by adding them to the +outputs array.

                                                                                        +

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

                                                                                      A single tensor if provided with a single output or no outputs +are provided and there is only one default output, otherwise return a +tensor array. The order of the tensor array is the same as the outputs +if provided, otherwise the order of outputNodes attribute of the model.

                                                                                      +

                                                                                    • Executes inference for the model for given input tensors in async +fashion, use this method when your model contains control flow ops.

                                                                                      +

                                                                                      Parameters

                                                                                      • inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap

                                                                                        tensor, tensor array or tensor map of the inputs for the +model, keyed by the input node names.

                                                                                        +
                                                                                      • Optionaloutputs: string | string[]

                                                                                        output node name from the TensorFlow model, if no outputs +are specified, the default outputs of the model would be used. You can +inspect intermediate nodes of the model by adding them to the outputs +array.

                                                                                        +

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

                                                                                      A Promise of single tensor if provided with a single output or +no outputs are provided and there is only one default output, otherwise +return a tensor map.

                                                                                      +

                                                                                    • Get intermediate tensors for model debugging mode (flag +KEEP_INTERMEDIATE_TENSORS is true).

                                                                                      +

                                                                                      Returns NamedTensorsMap

                                                                                    • Loads the model and weight files, construct the in memory weight map and +compile the inference graph.

                                                                                      +

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

                                                                                    • Synchronously construct the in memory weight map and +compile the inference graph.

                                                                                      +

                                                                                      Parameters

                                                                                      • artifacts: ModelArtifacts

                                                                                      Returns boolean

                                                                                    • Execute the inference for the input tensors.

                                                                                      +

                                                                                      Parameters

                                                                                      • inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap
                                                                                      • Optionalconfig: ModelPredictConfig

                                                                                        Prediction configuration for specifying the batch size. +Currently the batch size option is ignored for graph model.

                                                                                        +

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

                                                                                      Inference result tensors. If the model is converted and it +originally had structured_outputs in tensorflow, then a NamedTensorMap +will be returned matching the structured_outputs. If no structured_outputs +are present, the output will be single tf.Tensor if the model has single +output node, otherwise Tensor[].

                                                                                      +

                                                                                      GraphModel.inputNodes

                                                                                      +

                                                                                      You can also feed any intermediate nodes using the NamedTensorMap as the +input type. For example, given the graph +InputNode => Intermediate => OutputNode, +you can execute the subgraph Intermediate => OutputNode by calling +model.execute('IntermediateNode' : tf.tensor(...));

                                                                                      +

                                                                                      This is useful for models that uses tf.dynamic_rnn, where the intermediate +state needs to be fed manually.

                                                                                      +

                                                                                      For batch inference execution, the tensors for each input need to be +concatenated together. For example with mobilenet, the required input shape +is [1, 244, 244, 3], which represents the [batch, height, width, channel]. +If we are provide a batched data of 100 images, the input tensor should be +in the shape of [100, 244, 244, 3].

                                                                                      +

                                                                                    • Execute the inference for the input tensors in async fashion, use this +method when your model contains control flow ops.

                                                                                      +

                                                                                      Parameters

                                                                                      • inputs: Tensor<Rank> | Tensor<Rank>[] | NamedTensorMap
                                                                                      • Optionalconfig: ModelPredictConfig

                                                                                        Prediction configuration for specifying the batch size. +Currently the batch size option is ignored for graph model.

                                                                                        +

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

                                                                                      A Promise of inference result tensors. If the model is converted +and it originally had structured_outputs in tensorflow, then a +NamedTensorMap will be returned matching the structured_outputs. If no +structured_outputs are present, the output will be single tf.Tensor if +the model has single output node, otherwise Tensor[].

                                                                                      +

                                                                                      GraphModel.inputNodes

                                                                                      +

                                                                                      You can also feed any intermediate nodes using the NamedTensorMap as the +input type. For example, given the graph +InputNode => Intermediate => OutputNode, +you can execute the subgraph Intermediate => OutputNode by calling +model.execute('IntermediateNode' : tf.tensor(...));

                                                                                      +

                                                                                      This is useful for models that uses tf.dynamic_rnn, where the intermediate +state needs to be fed manually.

                                                                                      +

                                                                                      For batch inference execution, the tensors for each input need to be +concatenated together. For example with mobilenet, the required input shape +is [1, 244, 244, 3], which represents the [batch, height, width, channel]. +If we are provide a batched data of 100 images, the input tensor should be +in the shape of [100, 244, 244, 3].

                                                                                      +

                                                                                    • Save the configuration and/or weights of the GraphModel.

                                                                                      +

                                                                                      An IOHandler is an object that has a save method of the proper +signature defined. The save method manages the storing or +transmission of serialized data ("artifacts") that represent the +model's topology and weights onto or via a specific medium, such as +file downloads, local storage, IndexedDB in the web browser and HTTP +requests to a server. TensorFlow.js provides IOHandler +implementations for a number of frequently used saving mediums, such as +tf.io.browserDownloads and tf.io.browserLocalStorage. See tf.io +for more details.

                                                                                      +

                                                                                      This method also allows you to refer to certain types of IOHandlers +as URL-like string shortcuts, such as 'localstorage://' and +'indexeddb://'.

                                                                                      +

                                                                                      Example 1: Save model's topology and weights to browser local +storage; +then load it back.

                                                                                      +
                                                                                      const modelUrl =
                                                                                         'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
                                                                                      const model = await tf.loadGraphModel(modelUrl);
                                                                                      const zeros = tf.zeros([1, 224, 224, 3]);
                                                                                      model.predict(zeros).print();

                                                                                      const saveResults = await model.save('localstorage://my-model-1');

                                                                                      const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');
                                                                                      console.log('Prediction from loaded model:');
                                                                                      model.predict(zeros).print();
+
                                                                                      + +

                                                                                      Parameters

                                                                                      • handlerOrURL: string | IOHandler

                                                                                        An instance of IOHandler or a URL-like, +scheme-based string shortcut for IOHandler.

                                                                                        +
                                                                                      • Optionalconfig: SaveConfig

                                                                                        Options for saving the model.

                                                                                        +

                                                                                      Returns Promise<SaveResult>

                                                                                      A Promise of SaveResult, which summarizes the result of +the saving, such as byte sizes of the saved artifacts for the model's +topology and weight values.

                                                                                      +

                                                                                    Settings

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                                                                                    Accessors
                                                                                    Methods
                                                                                    diff --git a/typedoc/interfaces/HandConfig.html b/typedoc/interfaces/HandConfig.html index 01031105..b2328b3b 100644 --- a/typedoc/interfaces/HandConfig.html +++ b/typedoc/interfaces/HandConfig.html @@ -1,26 +1,26 @@ -HandConfig | @vladmandic/human - v3.3.5

                                                                                    Interface HandConfig

                                                                                    Configures all hand detection specific options

                                                                                    -
                                                                                    interface HandConfig {
                                                                                        detector: { modelPath?: string };
                                                                                        enabled: boolean;
                                                                                        iouThreshold: number;
                                                                                        landmarks: boolean;
                                                                                        maxDetected: number;
                                                                                        minConfidence: number;
                                                                                        modelPath: string;
                                                                                        rotation: boolean;
                                                                                        skeleton: { modelPath?: string };
                                                                                        skipFrames: number;
                                                                                        skipTime: number;
                                                                                    }

                                                                                    Hierarchy (View Summary)

                                                                                    Properties

                                                                                    detector -enabled -iouThreshold -landmarks -maxDetected -minConfidence -modelPath -rotation -skeleton -skipFrames -skipTime -

                                                                                    Properties

                                                                                    detector

                                                                                    detector: { modelPath?: string }

                                                                                    Type declaration

                                                                                    • OptionalmodelPath?: string

                                                                                      path to hand detector model json

                                                                                      -

                                                                                    enabled

                                                                                    enabled: boolean

                                                                                    is module enabled?

                                                                                    -

                                                                                    iouThreshold

                                                                                    iouThreshold: number

                                                                                    minimum overlap between two detected hands before one is discarded

                                                                                    -

                                                                                    landmarks

                                                                                    landmarks: boolean

                                                                                    should hand landmarks be detected or just return detected hand box

                                                                                    -

                                                                                    maxDetected

                                                                                    maxDetected: number

                                                                                    maximum number of detected hands

                                                                                    -

                                                                                    minConfidence

                                                                                    minConfidence: number

                                                                                    minimum confidence for a detected hand before results are discarded

                                                                                    -

                                                                                    modelPath

                                                                                    modelPath: string

                                                                                    path to model json file (relative to modelBasePath

                                                                                    -

                                                                                    rotation

                                                                                    rotation: boolean

                                                                                    should hand rotation correction be performed after hand detection?

                                                                                    -

                                                                                    skeleton

                                                                                    skeleton: { modelPath?: string }

                                                                                    Type declaration

                                                                                    • OptionalmodelPath?: string

                                                                                      path to hand skeleton model json

                                                                                      -

                                                                                    skipFrames

                                                                                    skipFrames: number

                                                                                    how many max frames to go without re-running model if cached results are acceptable +HandConfig | @vladmandic/human - v3.3.6

                                                                                    @vladmandic/human - v3.3.6
                                                                                      Preparing search index...

                                                                                      Interface HandConfig

                                                                                      Configures all hand detection specific options

                                                                                      +
                                                                                      interface HandConfig {
                                                                                          detector: { modelPath?: string };
                                                                                          enabled: boolean;
                                                                                          iouThreshold: number;
                                                                                          landmarks: boolean;
                                                                                          maxDetected: number;
                                                                                          minConfidence: number;
                                                                                          modelPath: string;
                                                                                          rotation: boolean;
                                                                                          skeleton: { modelPath?: string };
                                                                                          skipFrames: number;
                                                                                          skipTime: number;
                                                                                      }

                                                                                      Hierarchy (View Summary)

                                                                                      Index

                                                                                      Properties

                                                                                      detector +enabled +iouThreshold +landmarks +maxDetected +minConfidence +modelPath +rotation +skeleton +skipFrames +skipTime +

                                                                                      Properties

                                                                                      detector: { modelPath?: string }

                                                                                      Type Declaration

                                                                                      • OptionalmodelPath?: string

                                                                                        path to hand detector model json

                                                                                        +
                                                                                      enabled: boolean

                                                                                      is module enabled?

                                                                                      +
                                                                                      iouThreshold: number

                                                                                      minimum overlap between two detected hands before one is discarded

                                                                                      +
                                                                                      landmarks: boolean

                                                                                      should hand landmarks be detected or just return detected hand box

                                                                                      +
                                                                                      maxDetected: number

                                                                                      maximum number of detected hands

                                                                                      +
                                                                                      minConfidence: number

                                                                                      minimum confidence for a detected hand before results are discarded

                                                                                      +
                                                                                      modelPath: string

                                                                                      path to model json file (relative to modelBasePath

                                                                                      +
                                                                                      rotation: boolean

                                                                                      should hand rotation correction be performed after hand detection?

                                                                                      +
                                                                                      skeleton: { modelPath?: string }

                                                                                      Type Declaration

                                                                                      • OptionalmodelPath?: string

                                                                                        path to hand skeleton model json

                                                                                        +
                                                                                      skipFrames: number

                                                                                      how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                                      -

                                                                                      skipTime

                                                                                      skipTime: number

                                                                                      how many max milliseconds to go without re-running model if cached results are acceptable +

                                                                                      skipTime: number

                                                                                      how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                                      -

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                                                                                      +

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                                                                                      Properties
                                                                                      diff --git a/typedoc/interfaces/HandResult.html b/typedoc/interfaces/HandResult.html index e1d3c6fd..ce39cac7 100644 --- a/typedoc/interfaces/HandResult.html +++ b/typedoc/interfaces/HandResult.html @@ -1,22 +1,22 @@ -HandResult | @vladmandic/human - v3.3.5

                                                                                      Interface HandResult

                                                                                      Hand results

                                                                                      -
                                                                                      interface HandResult {
                                                                                          annotations: Record<Finger, Point[]>;
                                                                                          box: Box;
                                                                                          boxRaw: Box;
                                                                                          boxScore: number;
                                                                                          fingerScore: number;
                                                                                          id: number;
                                                                                          keypoints: Point[];
                                                                                          label: HandType;
                                                                                          landmarks: Record<Finger, { curl: FingerCurl; direction: FingerDirection }>;
                                                                                          score: number;
                                                                                      }

                                                                                      Properties

                                                                                      annotations -box -boxRaw -boxScore -fingerScore -id -keypoints -label -landmarks -score -

                                                                                      Properties

                                                                                      annotations

                                                                                      annotations: Record<Finger, Point[]>

                                                                                      detected hand keypoints combined into annotated parts

                                                                                      -

                                                                                      box

                                                                                      box: Box

                                                                                      detected hand box

                                                                                      -

                                                                                      boxRaw

                                                                                      boxRaw: Box

                                                                                      detected hand box normalized to 0..1

                                                                                      -

                                                                                      boxScore

                                                                                      boxScore: number

                                                                                      hand detection score

                                                                                      -

                                                                                      fingerScore

                                                                                      fingerScore: number

                                                                                      hand skelton score

                                                                                      -

                                                                                      id

                                                                                      id: number

                                                                                      hand id

                                                                                      -

                                                                                      keypoints

                                                                                      keypoints: Point[]

                                                                                      detected hand keypoints

                                                                                      -

                                                                                      label

                                                                                      label: HandType

                                                                                      detected hand class

                                                                                      -

                                                                                      landmarks

                                                                                      landmarks: Record<Finger, { curl: FingerCurl; direction: FingerDirection }>

                                                                                      detected hand parts annotated with part gestures

                                                                                      -

                                                                                      score

                                                                                      score: number

                                                                                      hand overal score

                                                                                      -

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                                                                                      +HandResult | @vladmandic/human - v3.3.6
                                                                                      @vladmandic/human - v3.3.6
                                                                                        Preparing search index...

                                                                                        Interface HandResult

                                                                                        Hand results

                                                                                        +
                                                                                        interface HandResult {
                                                                                            annotations: Record<Finger, Point[]>;
                                                                                            box: Box;
                                                                                            boxRaw: Box;
                                                                                            boxScore: number;
                                                                                            fingerScore: number;
                                                                                            id: number;
                                                                                            keypoints: Point[];
                                                                                            label: HandType;
                                                                                            landmarks: Record<Finger, { curl: FingerCurl; direction: FingerDirection }>;
                                                                                            score: number;
                                                                                        }
                                                                                        Index

                                                                                        Properties

                                                                                        annotations +box +boxRaw +boxScore +fingerScore +id +keypoints +label +landmarks +score +

                                                                                        Properties

                                                                                        annotations: Record<Finger, Point[]>

                                                                                        detected hand keypoints combined into annotated parts

                                                                                        +
                                                                                        box: Box

                                                                                        detected hand box

                                                                                        +
                                                                                        boxRaw: Box

                                                                                        detected hand box normalized to 0..1

                                                                                        +
                                                                                        boxScore: number

                                                                                        hand detection score

                                                                                        +
                                                                                        fingerScore: number

                                                                                        hand skelton score

                                                                                        +
                                                                                        id: number

                                                                                        hand id

                                                                                        +
                                                                                        keypoints: Point[]

                                                                                        detected hand keypoints

                                                                                        +
                                                                                        label: HandType

                                                                                        detected hand class

                                                                                        +
                                                                                        landmarks: Record<Finger, { curl: FingerCurl; direction: FingerDirection }>

                                                                                        detected hand parts annotated with part gestures

                                                                                        +
                                                                                        score: number

                                                                                        hand overal score

                                                                                        +

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                                                                                        diff --git a/typedoc/interfaces/ModelInfo.html b/typedoc/interfaces/ModelInfo.html index 2bc67c14..6cc50133 100644 --- a/typedoc/interfaces/ModelInfo.html +++ b/typedoc/interfaces/ModelInfo.html @@ -1,8 +1,8 @@ -ModelInfo | @vladmandic/human - v3.3.5

                                                                                        Interface ModelInfo

                                                                                        interface ModelInfo {
                                                                                            inCache: boolean;
                                                                                            loaded: boolean;
                                                                                            name: string;
                                                                                            sizeDesired: number;
                                                                                            sizeFromManifest: number;
                                                                                            sizeLoadedWeights: number;
                                                                                            url: string;
                                                                                        }

                                                                                        Properties

                                                                                        inCache -loaded -name -sizeDesired -sizeFromManifest -sizeLoadedWeights -url -

                                                                                        Properties

                                                                                        inCache

                                                                                        inCache: boolean

                                                                                        loaded

                                                                                        loaded: boolean

                                                                                        name

                                                                                        name: string

                                                                                        sizeDesired

                                                                                        sizeDesired: number

                                                                                        sizeFromManifest

                                                                                        sizeFromManifest: number

                                                                                        sizeLoadedWeights

                                                                                        sizeLoadedWeights: number

                                                                                        url

                                                                                        url: string

                                                                                        Settings

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                                                                                        +ModelInfo | @vladmandic/human - v3.3.6
                                                                                        @vladmandic/human - v3.3.6
                                                                                          Preparing search index...

                                                                                          Interface ModelInfo

                                                                                          interface ModelInfo {
                                                                                              inCache: boolean;
                                                                                              loaded: boolean;
                                                                                              name: string;
                                                                                              sizeDesired: number;
                                                                                              sizeFromManifest: number;
                                                                                              sizeLoadedWeights: number;
                                                                                              url: string;
                                                                                          }
                                                                                          Index

                                                                                          Properties

                                                                                          inCache +loaded +name +sizeDesired +sizeFromManifest +sizeLoadedWeights +url +

                                                                                          Properties

                                                                                          inCache: boolean
                                                                                          loaded: boolean
                                                                                          name: string
                                                                                          sizeDesired: number
                                                                                          sizeFromManifest: number
                                                                                          sizeLoadedWeights: number
                                                                                          url: string

                                                                                          Settings

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                                                                                          On This Page

                                                                                          Properties
                                                                                          diff --git a/typedoc/interfaces/ObjectConfig.html b/typedoc/interfaces/ObjectConfig.html index f762f260..3d3e47ac 100644 --- a/typedoc/interfaces/ObjectConfig.html +++ b/typedoc/interfaces/ObjectConfig.html @@ -1,18 +1,18 @@ -ObjectConfig | @vladmandic/human - v3.3.5

                                                                                          Interface ObjectConfig

                                                                                          Configures all object detection specific options

                                                                                          -
                                                                                          interface ObjectConfig {
                                                                                              enabled: boolean;
                                                                                              iouThreshold: number;
                                                                                              maxDetected: number;
                                                                                              minConfidence: number;
                                                                                              modelPath: string;
                                                                                              skipFrames: number;
                                                                                              skipTime: number;
                                                                                          }

                                                                                          Hierarchy (View Summary)

                                                                                          Properties

                                                                                          enabled -iouThreshold -maxDetected -minConfidence -modelPath -skipFrames -skipTime -

                                                                                          Properties

                                                                                          enabled

                                                                                          enabled: boolean

                                                                                          is module enabled?

                                                                                          -

                                                                                          iouThreshold

                                                                                          iouThreshold: number

                                                                                          minimum overlap between two detected objects before one is discarded

                                                                                          -

                                                                                          maxDetected

                                                                                          maxDetected: number

                                                                                          maximum number of detected objects

                                                                                          -

                                                                                          minConfidence

                                                                                          minConfidence: number

                                                                                          minimum confidence for a detected objects before results are discarded

                                                                                          -

                                                                                          modelPath

                                                                                          modelPath: string

                                                                                          path to model json file (relative to modelBasePath

                                                                                          -

                                                                                          skipFrames

                                                                                          skipFrames: number

                                                                                          how many max frames to go without re-running model if cached results are acceptable +ObjectConfig | @vladmandic/human - v3.3.6

                                                                                          @vladmandic/human - v3.3.6
                                                                                            Preparing search index...

                                                                                            Interface ObjectConfig

                                                                                            Configures all object detection specific options

                                                                                            +
                                                                                            interface ObjectConfig {
                                                                                                enabled: boolean;
                                                                                                iouThreshold: number;
                                                                                                maxDetected: number;
                                                                                                minConfidence: number;
                                                                                                modelPath: string;
                                                                                                skipFrames: number;
                                                                                                skipTime: number;
                                                                                            }

                                                                                            Hierarchy (View Summary)

                                                                                            Index

                                                                                            Properties

                                                                                            enabled +iouThreshold +maxDetected +minConfidence +modelPath +skipFrames +skipTime +

                                                                                            Properties

                                                                                            enabled: boolean

                                                                                            is module enabled?

                                                                                            +
                                                                                            iouThreshold: number

                                                                                            minimum overlap between two detected objects before one is discarded

                                                                                            +
                                                                                            maxDetected: number

                                                                                            maximum number of detected objects

                                                                                            +
                                                                                            minConfidence: number

                                                                                            minimum confidence for a detected objects before results are discarded

                                                                                            +
                                                                                            modelPath: string

                                                                                            path to model json file (relative to modelBasePath

                                                                                            +
                                                                                            skipFrames: number

                                                                                            how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                                            -

                                                                                            skipTime

                                                                                            skipTime: number

                                                                                            how many max milliseconds to go without re-running model if cached results are acceptable +

                                                                                            skipTime: number

                                                                                            how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                                            -

                                                                                            Settings

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                                                                                            On This Page

                                                                                            Properties
                                                                                            +

                                                                                            Settings

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                                                                                            On This Page

                                                                                            Properties
                                                                                            diff --git a/typedoc/interfaces/ObjectResult.html b/typedoc/interfaces/ObjectResult.html index 215a15f1..c9b04af3 100644 --- a/typedoc/interfaces/ObjectResult.html +++ b/typedoc/interfaces/ObjectResult.html @@ -1,14 +1,14 @@ -ObjectResult | @vladmandic/human - v3.3.5

                                                                                            Interface ObjectResult

                                                                                            Object results

                                                                                            -
                                                                                            interface ObjectResult {
                                                                                                box: Box;
                                                                                                boxRaw: Box;
                                                                                                class: number;
                                                                                                id: number;
                                                                                                label: ObjectType;
                                                                                                score: number;
                                                                                            }

                                                                                            Properties

                                                                                            box -boxRaw -class -id -label -score -

                                                                                            Properties

                                                                                            box

                                                                                            box: Box

                                                                                            detected object box

                                                                                            -

                                                                                            boxRaw

                                                                                            boxRaw: Box

                                                                                            detected object box normalized to 0..1

                                                                                            -

                                                                                            class

                                                                                            class: number

                                                                                            detected object class id

                                                                                            -

                                                                                            id

                                                                                            id: number

                                                                                            object id

                                                                                            -

                                                                                            label

                                                                                            label: ObjectType

                                                                                            detected object class name

                                                                                            -

                                                                                            score

                                                                                            score: number

                                                                                            object detection score

                                                                                            -

                                                                                            Settings

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                                                                                            +ObjectResult | @vladmandic/human - v3.3.6
                                                                                            @vladmandic/human - v3.3.6
                                                                                              Preparing search index...

                                                                                              Interface ObjectResult

                                                                                              Object results

                                                                                              +
                                                                                              interface ObjectResult {
                                                                                                  box: Box;
                                                                                                  boxRaw: Box;
                                                                                                  class: number;
                                                                                                  id: number;
                                                                                                  label: ObjectType;
                                                                                                  score: number;
                                                                                              }
                                                                                              Index

                                                                                              Properties

                                                                                              box +boxRaw +class +id +label +score +

                                                                                              Properties

                                                                                              box: Box

                                                                                              detected object box

                                                                                              +
                                                                                              boxRaw: Box

                                                                                              detected object box normalized to 0..1

                                                                                              +
                                                                                              class: number

                                                                                              detected object class id

                                                                                              +
                                                                                              id: number

                                                                                              object id

                                                                                              +
                                                                                              label: ObjectType

                                                                                              detected object class name

                                                                                              +
                                                                                              score: number

                                                                                              object detection score

                                                                                              +

                                                                                              Settings

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                                                                                              Properties
                                                                                              diff --git a/typedoc/interfaces/PersonResult.html b/typedoc/interfaces/PersonResult.html index 81912adb..c122f081 100644 --- a/typedoc/interfaces/PersonResult.html +++ b/typedoc/interfaces/PersonResult.html @@ -1,19 +1,19 @@ -PersonResult | @vladmandic/human - v3.3.5

                                                                                              Interface PersonResult

                                                                                              Person getter

                                                                                              +PersonResult | @vladmandic/human - v3.3.6
                                                                                              @vladmandic/human - v3.3.6
                                                                                                Preparing search index...

                                                                                                Interface PersonResult

                                                                                                Person getter

                                                                                                • Triggers combining all individual results into a virtual person object
                                                                                                -
                                                                                                interface PersonResult {
                                                                                                    body: null | BodyResult;
                                                                                                    box: Box;
                                                                                                    boxRaw?: Box;
                                                                                                    face: FaceResult;
                                                                                                    gestures: GestureResult[];
                                                                                                    hands: { left: null | HandResult; right: null | HandResult };
                                                                                                    id: number;
                                                                                                }

                                                                                                Properties

                                                                                                body -box -boxRaw? -face -gestures -hands -id -

                                                                                                Properties

                                                                                                body

                                                                                                body: null | BodyResult

                                                                                                body result that belongs to this person

                                                                                                -

                                                                                                box

                                                                                                box: Box

                                                                                                box that defines the person

                                                                                                -

                                                                                                OptionalboxRaw

                                                                                                boxRaw?: Box

                                                                                                box that defines the person normalized to 0..1

                                                                                                -

                                                                                                face

                                                                                                face: FaceResult

                                                                                                face result that belongs to this person

                                                                                                -

                                                                                                gestures

                                                                                                gestures: GestureResult[]

                                                                                                detected gestures specific to this person

                                                                                                -

                                                                                                hands

                                                                                                hands: { left: null | HandResult; right: null | HandResult }

                                                                                                left and right hand results that belong to this person

                                                                                                -

                                                                                                id

                                                                                                id: number

                                                                                                person id

                                                                                                -

                                                                                                Settings

                                                                                                Member Visibility

                                                                                                On This Page

                                                                                                Properties
                                                                                                +
                                                                                                interface PersonResult {
                                                                                                    body: null | BodyResult;
                                                                                                    box: Box;
                                                                                                    boxRaw?: Box;
                                                                                                    face: FaceResult;
                                                                                                    gestures: GestureResult[];
                                                                                                    hands: { left: null | HandResult; right: null | HandResult };
                                                                                                    id: number;
                                                                                                }
                                                                                                Index

                                                                                                Properties

                                                                                                body +box +boxRaw? +face +gestures +hands +id +

                                                                                                Properties

                                                                                                body

                                                                                                body: null | BodyResult

                                                                                                body result that belongs to this person

                                                                                                +
                                                                                                box: Box

                                                                                                box that defines the person

                                                                                                +
                                                                                                boxRaw?: Box

                                                                                                box that defines the person normalized to 0..1

                                                                                                +
                                                                                                face: FaceResult

                                                                                                face result that belongs to this person

                                                                                                +
                                                                                                gestures: GestureResult[]

                                                                                                detected gestures specific to this person

                                                                                                +
                                                                                                hands: { left: null | HandResult; right: null | HandResult }

                                                                                                left and right hand results that belong to this person

                                                                                                +
                                                                                                id: number

                                                                                                person id

                                                                                                +

                                                                                                Settings

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                                                                                                On This Page

                                                                                                Properties
                                                                                                diff --git a/typedoc/interfaces/Result.html b/typedoc/interfaces/Result.html index a970b240..99e68469 100644 --- a/typedoc/interfaces/Result.html +++ b/typedoc/interfaces/Result.html @@ -1,27 +1,27 @@ -Result | @vladmandic/human - v3.3.5

                                                                                                Interface Result

                                                                                                Result interface definition for Human library

                                                                                                +Result | @vladmandic/human - v3.3.6
                                                                                                @vladmandic/human - v3.3.6
                                                                                                  Preparing search index...

                                                                                                  Interface Result

                                                                                                  Result interface definition for Human library

                                                                                                  Contains all possible detection results

                                                                                                  -
                                                                                                  interface Result {
                                                                                                      body: BodyResult[];
                                                                                                      canvas?: null | AnyCanvas;
                                                                                                      error: null | string;
                                                                                                      face: FaceResult[];
                                                                                                      gesture: GestureResult[];
                                                                                                      hand: HandResult[];
                                                                                                      height: number;
                                                                                                      object: ObjectResult[];
                                                                                                      performance: Record<string, number>;
                                                                                                      persons: PersonResult[];
                                                                                                      timestamp: number;
                                                                                                      width: number;
                                                                                                  }

                                                                                                  Properties

                                                                                                  body -canvas? -error -face -gesture -hand -height -object -performance -persons -timestamp -width -

                                                                                                  Properties

                                                                                                  body

                                                                                                  body: BodyResult[]

                                                                                                  BodyResult: detection & analysis results

                                                                                                  -

                                                                                                  Optionalcanvas

                                                                                                  canvas?: null | AnyCanvas

                                                                                                  optional processed canvas that can be used to draw input on screen

                                                                                                  -

                                                                                                  error

                                                                                                  error: null | string

                                                                                                  Last known error message

                                                                                                  -

                                                                                                  face

                                                                                                  face: FaceResult[]

                                                                                                  FaceResult: detection & analysis results

                                                                                                  -

                                                                                                  gesture

                                                                                                  gesture: GestureResult[]

                                                                                                  GestureResult: detection & analysis results

                                                                                                  -

                                                                                                  hand

                                                                                                  hand: HandResult[]

                                                                                                  HandResult: detection & analysis results

                                                                                                  -

                                                                                                  height

                                                                                                  height: number

                                                                                                  Resolution height

                                                                                                  -

                                                                                                  object

                                                                                                  object: ObjectResult[]

                                                                                                  ObjectResult: detection & analysis results

                                                                                                  -

                                                                                                  performance

                                                                                                  performance: Record<string, number>

                                                                                                  global performance object with timing values for each operation

                                                                                                  -

                                                                                                  persons

                                                                                                  persons: PersonResult[]

                                                                                                  getter property that returns unified persons object

                                                                                                  -

                                                                                                  Readonlytimestamp

                                                                                                  timestamp: number

                                                                                                  timestamp of detection representing the milliseconds elapsed since the UNIX epoch

                                                                                                  -

                                                                                                  width

                                                                                                  width: number

                                                                                                  Resolution width

                                                                                                  -

                                                                                                  Settings

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                                                                                                  Properties
                                                                                                  +
                                                                                                  interface Result {
                                                                                                      body: BodyResult[];
                                                                                                      canvas?: null | AnyCanvas;
                                                                                                      error: null | string;
                                                                                                      face: FaceResult[];
                                                                                                      gesture: GestureResult[];
                                                                                                      hand: HandResult[];
                                                                                                      height: number;
                                                                                                      object: ObjectResult[];
                                                                                                      performance: Record<string, number>;
                                                                                                      persons: PersonResult[];
                                                                                                      timestamp: number;
                                                                                                      width: number;
                                                                                                  }
                                                                                                  Index

                                                                                                  Properties

                                                                                                  body +canvas? +error +face +gesture +hand +height +object +performance +persons +timestamp +width +

                                                                                                  Properties

                                                                                                  body

                                                                                                  body: BodyResult[]

                                                                                                  BodyResult: detection & analysis results

                                                                                                  +
                                                                                                  canvas?: null | AnyCanvas

                                                                                                  optional processed canvas that can be used to draw input on screen

                                                                                                  +
                                                                                                  error: null | string

                                                                                                  Last known error message

                                                                                                  +
                                                                                                  face: FaceResult[]

                                                                                                  FaceResult: detection & analysis results

                                                                                                  +
                                                                                                  gesture: GestureResult[]

                                                                                                  GestureResult: detection & analysis results

                                                                                                  +
                                                                                                  hand: HandResult[]

                                                                                                  HandResult: detection & analysis results

                                                                                                  +
                                                                                                  height: number

                                                                                                  Resolution height

                                                                                                  +
                                                                                                  object: ObjectResult[]

                                                                                                  ObjectResult: detection & analysis results

                                                                                                  +
                                                                                                  performance: Record<string, number>

                                                                                                  global performance object with timing values for each operation

                                                                                                  +
                                                                                                  persons: PersonResult[]

                                                                                                  getter property that returns unified persons object

                                                                                                  +
                                                                                                  timestamp: number

                                                                                                  timestamp of detection representing the milliseconds elapsed since the UNIX epoch

                                                                                                  +
                                                                                                  width: number

                                                                                                  Resolution width

                                                                                                  +

                                                                                                  Settings

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                                                                                                  On This Page

                                                                                                  Properties
                                                                                                  diff --git a/typedoc/interfaces/SegmentationConfig.html b/typedoc/interfaces/SegmentationConfig.html index 5166a6a0..29f00b4f 100644 --- a/typedoc/interfaces/SegmentationConfig.html +++ b/typedoc/interfaces/SegmentationConfig.html @@ -1,20 +1,20 @@ -SegmentationConfig | @vladmandic/human - v3.3.5

                                                                                                  Interface SegmentationConfig

                                                                                                  Configures all body segmentation module +SegmentationConfig | @vladmandic/human - v3.3.6

                                                                                                  @vladmandic/human - v3.3.6
                                                                                                    Preparing search index...

                                                                                                    Interface SegmentationConfig

                                                                                                    Configures all body segmentation module removes background from input containing person if segmentation is enabled it will run as preprocessing task before any other model alternatively leave it disabled and use it on-demand using human.segmentation method which can remove background or replace it with user-provided background

                                                                                                    -
                                                                                                    interface SegmentationConfig {
                                                                                                        enabled: boolean;
                                                                                                        mode: SegmentationEnum;
                                                                                                        modelPath: string;
                                                                                                        ratio: number;
                                                                                                        skipFrames: number;
                                                                                                        skipTime: number;
                                                                                                    }

                                                                                                    Hierarchy (View Summary)

                                                                                                    Properties

                                                                                                    enabled -mode -modelPath -ratio -skipFrames -skipTime -

                                                                                                    Properties

                                                                                                    enabled

                                                                                                    enabled: boolean

                                                                                                    is module enabled?

                                                                                                    -

                                                                                                    mode

                                                                                                    mode: SegmentationEnum

                                                                                                    possible rvm segmentation mode

                                                                                                    -

                                                                                                    modelPath

                                                                                                    modelPath: string

                                                                                                    path to model json file (relative to modelBasePath

                                                                                                    -

                                                                                                    ratio

                                                                                                    ratio: number

                                                                                                    downsample ratio, adjust to reflect approximately how much of input is taken by body

                                                                                                    -

                                                                                                    skipFrames

                                                                                                    skipFrames: number

                                                                                                    how many max frames to go without re-running model if cached results are acceptable +

                                                                                                    interface SegmentationConfig {
                                                                                                        enabled: boolean;
                                                                                                        mode: SegmentationEnum;
                                                                                                        modelPath: string;
                                                                                                        ratio: number;
                                                                                                        skipFrames: number;
                                                                                                        skipTime: number;
                                                                                                    }

                                                                                                    Hierarchy (View Summary)

                                                                                                    Index

                                                                                                    Properties

                                                                                                    enabled +mode +modelPath +ratio +skipFrames +skipTime +

                                                                                                    Properties

                                                                                                    enabled: boolean

                                                                                                    is module enabled?

                                                                                                    +
                                                                                                    mode: SegmentationEnum

                                                                                                    possible rvm segmentation mode

                                                                                                    +
                                                                                                    modelPath: string

                                                                                                    path to model json file (relative to modelBasePath

                                                                                                    +
                                                                                                    ratio: number

                                                                                                    downsample ratio, adjust to reflect approximately how much of input is taken by body

                                                                                                    +
                                                                                                    skipFrames: number

                                                                                                    how many max frames to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                                                    -

                                                                                                    skipTime

                                                                                                    skipTime: number

                                                                                                    how many max milliseconds to go without re-running model if cached results are acceptable +

                                                                                                    skipTime: number

                                                                                                    how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

                                                                                                    -

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                                                                                                    +

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                                                                                                    diff --git a/typedoc/interfaces/Tensor.html b/typedoc/interfaces/Tensor.html new file mode 100644 index 00000000..a993af8a --- /dev/null +++ b/typedoc/interfaces/Tensor.html @@ -0,0 +1,224 @@ +Tensor | @vladmandic/human - v3.3.6
                                                                                                    @vladmandic/human - v3.3.6
                                                                                                      Preparing search index...

                                                                                                      Interface Tensor<R>

                                                                                                      A tf.Tensor object represents an immutable, multidimensional array of +numbers that has a shape and a data type.

                                                                                                      +

                                                                                                      For performance reasons, functions that create tensors do not necessarily +perform a copy of the data passed to them (e.g. if the data is passed as a +Float32Array), and changes to the data will change the tensor. This is not +a feature and is not supported. To avoid this behavior, use the tensor before +changing the input data or create a copy with copy = tf.add(yourTensor, 0).

                                                                                                      +

                                                                                                      See tf.tensor for details on how to create a tf.Tensor.

                                                                                                      +
                                                                                                      interface Tensor<R extends Rank = Rank> {
                                                                                                          dataId: object;
                                                                                                          dtype: keyof DataTypeMap;
                                                                                                          id: number;
                                                                                                          kept: boolean;
                                                                                                          kerasMask?: Tensor<Rank>;
                                                                                                          rankType: R;
                                                                                                          scopeId: number;
                                                                                                          shape: ShapeMap[R];
                                                                                                          size: number;
                                                                                                          strides: number[];
                                                                                                          get isDisposed(): boolean;
                                                                                                          get rank(): number;
                                                                                                          abs<T extends Tensor<Rank>>(this: T): T;
                                                                                                          acos<T extends Tensor<Rank>>(this: T): T;
                                                                                                          acosh<T extends Tensor<Rank>>(this: T): T;
                                                                                                          add<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          all<T extends Tensor<Rank>>(
                                                                                                              this: T,
                                                                                                              axis?: number | number[],
                                                                                                              keepDims?: boolean,
                                                                                                          ): T;
                                                                                                          any<T extends Tensor<Rank>>(
                                                                                                              this: T,
                                                                                                              axis?: number | number[],
                                                                                                              keepDims?: boolean,
                                                                                                          ): T;
                                                                                                          argMax<T extends Tensor<Rank>>(axis?: number): T;
                                                                                                          argMin<T extends Tensor<Rank>>(axis?: number): T;
                                                                                                          array(): Promise<ArrayMap[R]>;
                                                                                                          arraySync(): ArrayMap[R];
                                                                                                          as1D<T extends Tensor<Rank>>(): Tensor1D;
                                                                                                          as2D<T extends Tensor<Rank>>(rows: number, columns: number): Tensor2D;
                                                                                                          as3D<T extends Tensor<Rank>>(
                                                                                                              rows: number,
                                                                                                              columns: number,
                                                                                                              depth: number,
                                                                                                          ): Tensor3D;
                                                                                                          as4D<T extends Tensor<Rank>>(
                                                                                                              rows: number,
                                                                                                              columns: number,
                                                                                                              depth: number,
                                                                                                              depth2: number,
                                                                                                          ): Tensor4D;
                                                                                                          as5D<T extends Tensor<Rank>>(
                                                                                                              rows: number,
                                                                                                              columns: number,
                                                                                                              depth: number,
                                                                                                              depth2: number,
                                                                                                              depth3: number,
                                                                                                          ): Tensor5D;
                                                                                                          asin<T extends Tensor<Rank>>(this: T): T;
                                                                                                          asinh<T extends Tensor<Rank>>(this: T): T;
                                                                                                          asScalar<T extends Tensor<Rank>>(): Scalar;
                                                                                                          asType<T extends Tensor<Rank>>(this: T, dtype: keyof DataTypeMap): T;
                                                                                                          atan<T extends Tensor<Rank>>(this: T): T;
                                                                                                          atan2<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          atanh<T extends Tensor<Rank>>(this: T): T;
                                                                                                          avgPool<T extends Tensor3D | Tensor4D>(
                                                                                                              filterSize: number | [number, number],
                                                                                                              strides: number | [number, number],
                                                                                                              pad: number | "valid" | "same" | ExplicitPadding,
                                                                                                              dimRoundingMode?: "floor" | "round" | "ceil",
                                                                                                          ): T;
                                                                                                          batchNorm<T extends Tensor<Rank>>(
                                                                                                              mean: Tensor<R> | TensorLike | Tensor1D,
                                                                                                              variance: Tensor<R> | TensorLike | Tensor1D,
                                                                                                              offset?: Tensor<R> | TensorLike | Tensor1D,
                                                                                                              scale?: Tensor<R> | TensorLike | Tensor1D,
                                                                                                              varianceEpsilon?: number,
                                                                                                          ): Tensor<R>;
                                                                                                          batchToSpaceND<R extends Rank>(
                                                                                                              blockShape: number[],
                                                                                                              crops: number[][],
                                                                                                          ): Tensor<R>;
                                                                                                          broadcastTo<R extends Rank>(shape: ShapeMap[R]): Tensor<R>;
                                                                                                          buffer<D extends keyof DataTypeMap = "float32">(): Promise<
                                                                                                              TensorBuffer<R, D>,
                                                                                                          >;
                                                                                                          bufferSync<D extends keyof DataTypeMap = "float32">(): TensorBuffer<R, D>;
                                                                                                          bytes(): Promise<
                                                                                                              Uint8Array<ArrayBufferLike>
                                                                                                              | Uint8Array<ArrayBufferLike>[],
                                                                                                          >;
                                                                                                          cast<T extends Tensor<Rank>>(dtype: keyof DataTypeMap): T;
                                                                                                          ceil<T extends Tensor<Rank>>(this: T): T;
                                                                                                          clipByValue<T extends Tensor<Rank>>(min: number, max: number): Tensor;
                                                                                                          clone<T extends Tensor<Rank>>(this: T): T;
                                                                                                          concat<T extends Tensor<Rank>>(
                                                                                                              tensors: T | (TensorLike | T)[],
                                                                                                              axis?: number,
                                                                                                          ): T;
                                                                                                          conv1d<T extends Tensor2D | Tensor3D>(
                                                                                                              filter: Tensor3D | TensorLike3D,
                                                                                                              stride: number,
                                                                                                              pad: number | "valid" | "same" | ExplicitPadding,
                                                                                                              dataFormat?: "NWC" | "NCW",
                                                                                                              dilation?: number,
                                                                                                              dimRoundingMode?: "floor" | "round" | "ceil",
                                                                                                          ): T;
                                                                                                          conv2d<T extends Tensor3D | Tensor4D>(
                                                                                                              filter: Tensor4D | TensorLike4D,
                                                                                                              strides: number | [number, number],
                                                                                                              pad: number | "valid" | "same",
                                                                                                              dataFormat?: "NHWC" | "NCHW",
                                                                                                              dilations?: number | [number, number],
                                                                                                              dimRoundingMode?: "floor" | "round" | "ceil",
                                                                                                          ): T;
                                                                                                          conv2dTranspose<T extends Tensor3D | Tensor4D>(
                                                                                                              filter: Tensor4D | TensorLike4D,
                                                                                                              outputShape: [number, number, number] | [number, number, number, number],
                                                                                                              strides: number | [number, number],
                                                                                                              pad: number | "valid" | "same",
                                                                                                              dimRoundingMode?: "floor" | "round" | "ceil",
                                                                                                          ): T;
                                                                                                          cos<T extends Tensor<Rank>>(this: T): T;
                                                                                                          cosh<T extends Tensor<Rank>>(this: T): T;
                                                                                                          cumprod<R extends Rank>(
                                                                                                              axis?: number,
                                                                                                              exclusive?: boolean,
                                                                                                              reverse?: boolean,
                                                                                                          ): Tensor<R>;
                                                                                                          cumsum<R extends Rank>(
                                                                                                              axis?: number,
                                                                                                              exclusive?: boolean,
                                                                                                              reverse?: boolean,
                                                                                                          ): Tensor<R>;
                                                                                                          data<D extends keyof DataTypeMap = NumericDataType>(): Promise<
                                                                                                              DataTypeMap[D],
                                                                                                          >;
                                                                                                          dataSync<D extends keyof DataTypeMap = NumericDataType>(): DataTypeMap[D];
                                                                                                          dataToGPU(options?: DataToGPUWebGLOption): GPUData;
                                                                                                          depthToSpace<T extends Tensor4D>(
                                                                                                              blockSize: number,
                                                                                                              dataFormat: "NHWC" | "NCHW",
                                                                                                          ): T;
                                                                                                          depthwiseConv2d<T extends Tensor3D | Tensor4D>(
                                                                                                              filter: Tensor4D | TensorLike4D,
                                                                                                              strides: number | [number, number],
                                                                                                              pad: number | "valid" | "same",
                                                                                                              dataFormat?: "NHWC" | "NCHW",
                                                                                                              dilations?: number | [number, number],
                                                                                                              dimRoundingMode?: "floor" | "round" | "ceil",
                                                                                                          ): T;
                                                                                                          dilation2d<T extends Tensor3D | Tensor4D>(
                                                                                                              filter: Tensor3D | TensorLike3D,
                                                                                                              strides: number | [number, number],
                                                                                                              pad: "valid" | "same",
                                                                                                              dilations?: number | [number, number],
                                                                                                              dataFormat?: "NHWC",
                                                                                                          ): T;
                                                                                                          dispose(): void;
                                                                                                          div<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          divNoNan<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          dot<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): Tensor;
                                                                                                          elu<T extends Tensor<Rank>>(): T;
                                                                                                          equal<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          erf<T extends Tensor<Rank>>(this: T): T;
                                                                                                          euclideanNorm<T extends Tensor<Rank>>(
                                                                                                              this: T,
                                                                                                              axis?: number | number[],
                                                                                                              keepDims?: boolean,
                                                                                                          ): T;
                                                                                                          exp<T extends Tensor<Rank>>(this: T): T;
                                                                                                          expandDims<T extends Tensor<Rank>>(axis?: number): T;
                                                                                                          expm1<T extends Tensor<Rank>>(this: T): T;
                                                                                                          fft<T extends Tensor<Rank>>(this: Tensor): Tensor;
                                                                                                          flatten<T extends Tensor<Rank>>(): Tensor1D;
                                                                                                          floor<T extends Tensor<Rank>>(this: T): T;
                                                                                                          floorDiv<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          gather<T extends Tensor<Rank>>(
                                                                                                              this: T,
                                                                                                              indices: Tensor<Rank> | TensorLike,
                                                                                                              axis?: number,
                                                                                                              batchDims?: number,
                                                                                                          ): T;
                                                                                                          greater<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          greaterEqual<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          ifft<T extends Tensor<Rank>>(this: Tensor): Tensor;
                                                                                                          irfft<T extends Tensor<Rank>>(this: Tensor): Tensor;
                                                                                                          isFinite<T extends Tensor<Rank>>(this: T): T;
                                                                                                          isInf<T extends Tensor<Rank>>(this: T): T;
                                                                                                          isNaN<T extends Tensor<Rank>>(this: T): T;
                                                                                                          leakyRelu<T extends Tensor<Rank>>(alpha: number): T;
                                                                                                          less<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          lessEqual<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          localResponseNormalization<T extends Tensor<Rank>>(
                                                                                                              depthRadius?: number,
                                                                                                              bias?: number,
                                                                                                              alpha?: number,
                                                                                                              beta?: number,
                                                                                                          ): T;
                                                                                                          log<T extends Tensor<Rank>>(this: T): T;
                                                                                                          log1p<T extends Tensor<Rank>>(this: T): T;
                                                                                                          logicalAnd<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          logicalNot<T extends Tensor<Rank>>(): T;
                                                                                                          logicalOr<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          logicalXor<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          logSigmoid<T extends Tensor<Rank>>(this: T): T;
                                                                                                          logSoftmax<T extends Tensor<Rank>>(this: T, axis?: number): T;
                                                                                                          logSumExp<T extends Tensor<Rank>>(
                                                                                                              this: T,
                                                                                                              axis?: number | number[],
                                                                                                              keepDims?: boolean,
                                                                                                          ): T;
                                                                                                          matMul<T extends Tensor<Rank>>(
                                                                                                              b: Tensor<Rank> | TensorLike,
                                                                                                              transposeA?: boolean,
                                                                                                              transposeB?: boolean,
                                                                                                          ): Tensor;
                                                                                                          max<T extends Tensor<Rank>>(
                                                                                                              axis?: number | number[],
                                                                                                              keepDims?: boolean,
                                                                                                          ): T;
                                                                                                          maximum<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          maxPool<T extends Tensor3D | Tensor4D>(
                                                                                                              filterSize: number | [number, number],
                                                                                                              strides: number | [number, number],
                                                                                                              pad: number | "valid" | "same" | ExplicitPadding,
                                                                                                              dimRoundingMode?: "floor" | "round" | "ceil",
                                                                                                          ): T;
                                                                                                          mean<T extends Tensor<Rank>>(
                                                                                                              axis?: number | number[],
                                                                                                              keepDims?: boolean,
                                                                                                          ): T;
                                                                                                          min<T extends Tensor<Rank>>(
                                                                                                              axis?: number | number[],
                                                                                                              keepDims?: boolean,
                                                                                                          ): T;
                                                                                                          minimum<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          mirrorPad<T extends Tensor<Rank>>(
                                                                                                              paddings: [number, number][],
                                                                                                              mode: "reflect" | "symmetric",
                                                                                                          ): T;
                                                                                                          mod<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          mul<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          neg<T extends Tensor<Rank>>(this: T): T;
                                                                                                          norm<T extends Tensor<Rank>>(
                                                                                                              ord?: number | "euclidean" | "fro",
                                                                                                              axis?: number | number[],
                                                                                                              keepDims?: boolean,
                                                                                                          ): Tensor;
                                                                                                          notEqual<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          oneHot(depth: number, onValue: number, offValue: number): Tensor;
                                                                                                          onesLike<T extends Tensor<Rank>>(this: T): T;
                                                                                                          pad<T extends Tensor<Rank>>(
                                                                                                              paddings: [number, number][],
                                                                                                              constantValue?: number,
                                                                                                          ): T;
                                                                                                          pool<T extends Tensor3D | Tensor4D>(
                                                                                                              windowShape: number | [number, number],
                                                                                                              poolingType: "avg" | "max",
                                                                                                              padding: number | "valid" | "same" | ExplicitPadding,
                                                                                                              diationRate?: number | [number, number],
                                                                                                              strides?: number | [number, number],
                                                                                                              dimRoundingMode?: "floor" | "round" | "ceil",
                                                                                                          ): T;
                                                                                                          pow<T extends Tensor<Rank>>(exp: Tensor<Rank> | TensorLike): T;
                                                                                                          prelu<T extends Tensor<Rank>>(alpha: TensorLike | T): T;
                                                                                                          print(verbose?: boolean): void;
                                                                                                          prod<T extends Tensor<Rank>>(
                                                                                                              this: T,
                                                                                                              axis?: number | number[],
                                                                                                              keepDims?: boolean,
                                                                                                          ): T;
                                                                                                          reciprocal<T extends Tensor<Rank>>(this: T): T;
                                                                                                          relu<T extends Tensor<Rank>>(): T;
                                                                                                          relu6<T extends Tensor<Rank>>(): T;
                                                                                                          reshape<T extends Tensor<Rank>>(shape: number[]): T;
                                                                                                          reshapeAs<T extends Tensor<Rank>>(x: T): T;
                                                                                                          resizeBilinear<T extends Tensor3D | Tensor4D>(
                                                                                                              newShape2D: [number, number],
                                                                                                              alignCorners?: boolean,
                                                                                                              halfPixelCenters?: boolean,
                                                                                                          ): T;
                                                                                                          resizeNearestNeighbor<T extends Tensor3D | Tensor4D>(
                                                                                                              newShape2D: [number, number],
                                                                                                              alignCorners?: boolean,
                                                                                                              halfFloatCenters?: boolean,
                                                                                                          ): T;
                                                                                                          reverse<T extends Tensor<Rank>>(this: T, axis?: number | number[]): T;
                                                                                                          rfft<T extends Tensor<Rank>>(this: Tensor): Tensor;
                                                                                                          round<T extends Tensor<Rank>>(this: T): T;
                                                                                                          rsqrt<T extends Tensor<Rank>>(this: T): T;
                                                                                                          selu<T extends Tensor<Rank>>(): T;
                                                                                                          separableConv2d<T extends Tensor3D | Tensor4D>(
                                                                                                              depthwiseFilter: Tensor4D | TensorLike4D,
                                                                                                              pointwiseFilter: TensorLike | Tensor4D,
                                                                                                              strides: number | [number, number],
                                                                                                              pad: "valid" | "same",
                                                                                                              dilation?: number | [number, number],
                                                                                                              dataFormat?: "NHWC" | "NCHW",
                                                                                                          ): T;
                                                                                                          sigmoid<T extends Tensor<Rank>>(this: T): T;
                                                                                                          sign<T extends Tensor<Rank>>(this: T): T;
                                                                                                          sin<T extends Tensor<Rank>>(this: T): T;
                                                                                                          sinh<T extends Tensor<Rank>>(this: T): T;
                                                                                                          slice<T extends Tensor<Rank>>(
                                                                                                              this: T,
                                                                                                              begin: number | number[],
                                                                                                              size?: number | number[],
                                                                                                          ): T;
                                                                                                          softmax<T extends Tensor<Rank>>(this: T, dim?: number): T;
                                                                                                          softplus<T extends Tensor<Rank>>(this: T): T;
                                                                                                          spaceToBatchND<R extends Rank>(
                                                                                                              blockShape: number[],
                                                                                                              paddings: number[][],
                                                                                                          ): Tensor<R>;
                                                                                                          split<T extends Tensor<Rank>>(
                                                                                                              numOrSizeSplits: number | number[],
                                                                                                              axis?: number,
                                                                                                          ): T[];
                                                                                                          sqrt<T extends Tensor<Rank>>(this: T): T;
                                                                                                          square<T extends Tensor<Rank>>(this: T): T;
                                                                                                          squaredDifference<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          squeeze<T extends Tensor<Rank>>(axis?: number[]): T;
                                                                                                          stack<T extends Tensor<Rank>>(
                                                                                                              x: Tensor<Rank> | Tensor<Rank>[],
                                                                                                              axis?: number,
                                                                                                          ): T;
                                                                                                          step<T extends Tensor<Rank>>(this: T, alpha?: number): T;
                                                                                                          stridedSlice<T extends Tensor<Rank>>(
                                                                                                              this: Tensor,
                                                                                                              begin: number[],
                                                                                                              end: number[],
                                                                                                              strides: number[],
                                                                                                              beginMask?: number,
                                                                                                              endMask?: number,
                                                                                                              ellipsisMask?: number,
                                                                                                              newAxisMask?: number,
                                                                                                              shrinkAxisMask?: number,
                                                                                                          ): Tensor;
                                                                                                          sub<T extends Tensor<Rank>>(b: Tensor<Rank> | TensorLike): T;
                                                                                                          sum<T extends Tensor<Rank>>(
                                                                                                              axis?: number | number[],
                                                                                                              keepDims?: boolean,
                                                                                                          ): T;
                                                                                                          tan<T extends Tensor<Rank>>(this: T): T;
                                                                                                          tanh<T extends Tensor<Rank>>(this: T): T;
                                                                                                          throwIfDisposed(): void;
                                                                                                          tile<T extends Tensor<Rank>>(b: number[]): T;
                                                                                                          toBool<T extends Tensor<Rank>>(this: T): T;
                                                                                                          toFloat<T extends Tensor<Rank>>(this: T): T;
                                                                                                          toInt<T extends Tensor<Rank>>(this: T): T;
                                                                                                          topk<T extends Tensor<Rank>>(
                                                                                                              this: T,
                                                                                                              k?: number,
                                                                                                              sorted?: boolean,
                                                                                                          ): { indices: T; values: T };
                                                                                                          toString(verbose?: boolean): string;
                                                                                                          transpose<T extends Tensor<Rank>>(perm?: number[]): T;
                                                                                                          unique<T extends Tensor<Rank>>(
                                                                                                              this: T,
                                                                                                              axis?: number,
                                                                                                          ): { indices: T; values: T };
                                                                                                          unsortedSegmentSum<T extends Tensor<Rank>>(
                                                                                                              this: T,
                                                                                                              segmentIds: Tensor1D | TensorLike1D,
                                                                                                              numSegments: number,
                                                                                                          ): T;
                                                                                                          unstack<T extends Tensor<Rank>>(axis?: number): T[];
                                                                                                          variable(
                                                                                                              trainable?: boolean,
                                                                                                              name?: string,
                                                                                                              dtype?: keyof DataTypeMap,
                                                                                                          ): Variable<R>;
                                                                                                          where<T extends Tensor<Rank>>(
                                                                                                              condition: Tensor<Rank> | TensorLike,
                                                                                                              x: Tensor<Rank> | TensorLike,
                                                                                                          ): T;
                                                                                                          zerosLike<T extends Tensor<Rank>>(this: T): T;
                                                                                                      }

                                                                                                      Type Parameters

                                                                                                      Implements

                                                                                                      • TensorInfo
                                                                                                      Index

                                                                                                      Properties

                                                                                                      dataId +dtype +id +kept +kerasMask? +rankType +scopeId +shape +size +strides +

                                                                                                      Accessors

                                                                                                      isDisposed +rank +

                                                                                                      Methods

                                                                                                      abs +acos +acosh +add +all +any +argMax +argMin +array +arraySync +as1D +as2D +as3D +as4D +as5D +asin +asinh +asScalar +asType +atan +atan2 +atanh +avgPool +batchNorm +batchToSpaceND +broadcastTo +buffer +bufferSync +bytes +cast +ceil +clipByValue +clone +concat +conv1d +conv2d +conv2dTranspose +cos +cosh +cumprod +cumsum +data +dataSync +dataToGPU +depthToSpace +depthwiseConv2d +dilation2d +dispose +div +divNoNan +dot +elu +equal +erf +euclideanNorm +exp +expandDims +expm1 +fft +flatten +floor +floorDiv +gather +greater +greaterEqual +ifft +irfft +isFinite +isInf +isNaN +leakyRelu +less +lessEqual +localResponseNormalization +log +log1p +logicalAnd +logicalNot +logicalOr +logicalXor +logSigmoid +logSoftmax +logSumExp +matMul +max +maximum +maxPool +mean +min +minimum +mirrorPad +mod +mul +neg +norm +notEqual +oneHot +onesLike +pad +pool +pow +prelu +print +prod +reciprocal +relu +relu6 +reshape +reshapeAs +resizeBilinear +resizeNearestNeighbor +reverse +rfft +round +rsqrt +selu +separableConv2d +sigmoid +sign +sin +sinh +slice +softmax +softplus +spaceToBatchND +split +sqrt +square +squaredDifference +squeeze +stack +step +stridedSlice +sub +sum +tan +tanh +throwIfDisposed +tile +toBool +toFloat +toInt +topk +toString +transpose +unique +unsortedSegmentSum +unstack +variable +where +zerosLike +

                                                                                                      Properties

                                                                                                      dataId: object

                                                                                                      Id of the bucket holding the data for this tensor. Multiple arrays can +point to the same bucket (e.g. when calling array.reshape()).

                                                                                                      +
                                                                                                      dtype: keyof DataTypeMap

                                                                                                      The data type for the array.

                                                                                                      +
                                                                                                      id: number

                                                                                                      Unique id of this tensor.

                                                                                                      +
                                                                                                      kept: boolean

                                                                                                      Whether this tensor has been globally kept.

                                                                                                      +
                                                                                                      kerasMask?: Tensor<Rank>

                                                                                                      The keras mask that some keras layers attach to the tensor

                                                                                                      +
                                                                                                      rankType: R

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

                                                                                                      +
                                                                                                      scopeId: number

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

                                                                                                      +
                                                                                                      shape: ShapeMap[R]

                                                                                                      The shape of the tensor.

                                                                                                      +
                                                                                                      size: number

                                                                                                      Number of elements in the tensor.

                                                                                                      +
                                                                                                      strides: number[]

                                                                                                      Number of elements to skip in each dimension when indexing. See +https://docs.scipy.org/doc/numpy/reference/generated/\ +numpy.ndarray.strides.html

                                                                                                      +

                                                                                                      Accessors

                                                                                                      • get isDisposed(): boolean

                                                                                                        Returns boolean

                                                                                                      • get rank(): number

                                                                                                        Returns number

                                                                                                      Methods

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T
                                                                                                        • Optionalaxis: number | number[]
                                                                                                        • OptionalkeepDims: boolean

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T
                                                                                                        • Optionalaxis: number | number[]
                                                                                                        • OptionalkeepDims: boolean

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • Optionalaxis: number

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • Optionalaxis: number

                                                                                                        Returns T

                                                                                                      • Returns the tensor data as a nested array. The transfer of data is done +asynchronously.

                                                                                                        +

                                                                                                        Returns Promise<ArrayMap[R]>

                                                                                                      • Returns the tensor data as a nested array. The transfer of data is done +synchronously.

                                                                                                        +

                                                                                                        Returns ArrayMap[R]

                                                                                                      • Type Parameters

                                                                                                        Returns Tensor1D

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • rows: number
                                                                                                        • columns: number

                                                                                                        Returns Tensor2D

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • rows: number
                                                                                                        • columns: number
                                                                                                        • depth: number

                                                                                                        Returns Tensor3D

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • rows: number
                                                                                                        • columns: number
                                                                                                        • depth: number
                                                                                                        • depth2: number

                                                                                                        Returns Tensor4D

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • rows: number
                                                                                                        • columns: number
                                                                                                        • depth: number
                                                                                                        • depth2: number
                                                                                                        • depth3: number

                                                                                                        Returns Tensor5D

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Returns Scalar

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T
                                                                                                        • dtype: keyof DataTypeMap

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • filterSize: number | [number, number]
                                                                                                        • strides: number | [number, number]
                                                                                                        • pad: number | "valid" | "same" | ExplicitPadding
                                                                                                        • OptionaldimRoundingMode: "floor" | "round" | "ceil"

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • blockShape: number[]
                                                                                                        • crops: number[][]

                                                                                                        Returns Tensor<R>

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • shape: ShapeMap[R]

                                                                                                        Returns Tensor<R>

                                                                                                      • Returns a promise of tf.TensorBuffer that holds the underlying data.

                                                                                                        +

                                                                                                        Type Parameters

                                                                                                        • D extends keyof DataTypeMap = "float32"

                                                                                                        Returns Promise<TensorBuffer<R, D>>

                                                                                                      • Returns a tf.TensorBuffer that holds the underlying data.

                                                                                                        +

                                                                                                        Type Parameters

                                                                                                        • D extends keyof DataTypeMap = "float32"

                                                                                                        Returns TensorBuffer<R, D>

                                                                                                      • Returns the underlying bytes of the tensor's data.

                                                                                                        +

                                                                                                        Returns Promise<Uint8Array<ArrayBufferLike> | Uint8Array<ArrayBufferLike>[]>

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • dtype: keyof DataTypeMap

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • min: number
                                                                                                        • max: number

                                                                                                        Returns Tensor

                                                                                                      • Returns a copy of the tensor. See tf.clone for details.

                                                                                                        +

                                                                                                        Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • filter: Tensor3D | TensorLike3D
                                                                                                        • stride: number
                                                                                                        • pad: number | "valid" | "same" | ExplicitPadding
                                                                                                        • OptionaldataFormat: "NWC" | "NCW"
                                                                                                        • Optionaldilation: number
                                                                                                        • OptionaldimRoundingMode: "floor" | "round" | "ceil"

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • filter: Tensor4D | TensorLike4D
                                                                                                        • strides: number | [number, number]
                                                                                                        • pad: number | "valid" | "same"
                                                                                                        • OptionaldataFormat: "NHWC" | "NCHW"
                                                                                                        • Optionaldilations: number | [number, number]
                                                                                                        • OptionaldimRoundingMode: "floor" | "round" | "ceil"

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • filter: Tensor4D | TensorLike4D
                                                                                                        • outputShape: [number, number, number] | [number, number, number, number]
                                                                                                        • strides: number | [number, number]
                                                                                                        • pad: number | "valid" | "same"
                                                                                                        • OptionaldimRoundingMode: "floor" | "round" | "ceil"

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • Optionalaxis: number
                                                                                                        • Optionalexclusive: boolean
                                                                                                        • Optionalreverse: boolean

                                                                                                        Returns Tensor<R>

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • Optionalaxis: number
                                                                                                        • Optionalexclusive: boolean
                                                                                                        • Optionalreverse: boolean

                                                                                                        Returns Tensor<R>

                                                                                                      • Asynchronously downloads the values from the tf.Tensor. Returns a +promise of TypedArray that resolves when the computation has finished.

                                                                                                        +

                                                                                                        Type Parameters

                                                                                                        • D extends keyof DataTypeMap = NumericDataType

                                                                                                        Returns Promise<DataTypeMap[D]>

                                                                                                      • Synchronously downloads the values from the tf.Tensor. This blocks the +UI thread until the values are ready, which can cause performance issues.

                                                                                                        +

                                                                                                        Type Parameters

                                                                                                        • D extends keyof DataTypeMap = NumericDataType

                                                                                                        Returns DataTypeMap[D]

                                                                                                      • Copy the tensor's data to a new GPU resource. Comparing to the dataSync() +and data(), this method prevents data from being downloaded to CPU.

                                                                                                        +

                                                                                                        For WebGL backend, the data will be stored on a densely packed texture. +This means that the texture will use the RGBA channels to store value.

                                                                                                        +

                                                                                                        For WebGPU backend, the data will be stored on a buffer. There is no +parameter, so can not use a user-defined size to create the buffer.

                                                                                                        +

                                                                                                        Parameters

                                                                                                        • Optionaloptions: DataToGPUWebGLOption

                                                                                                        Returns GPUData

                                                                                                        For WebGL backend, a GPUData contains the new texture and +its information. +{ +tensorRef: The tensor that is associated with this texture, +texture: WebGLTexture, +texShape: [number, number] // [height, width] +}

                                                                                                        +
                                                                                                        For WebGPU backend, a GPUData contains the new buffer.
+{
+   tensorRef: The tensor that is associated with this buffer,
+   buffer: GPUBuffer,
+}
+
+Remember to dispose the GPUData after it is used by
+`res.tensorRef.dispose()`.
+
                                                                                                        +

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • blockSize: number
                                                                                                        • dataFormat: "NHWC" | "NCHW"

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • filter: Tensor4D | TensorLike4D
                                                                                                        • strides: number | [number, number]
                                                                                                        • pad: number | "valid" | "same"
                                                                                                        • OptionaldataFormat: "NHWC" | "NCHW"
                                                                                                        • Optionaldilations: number | [number, number]
                                                                                                        • OptionaldimRoundingMode: "floor" | "round" | "ceil"

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • filter: Tensor3D | TensorLike3D
                                                                                                        • strides: number | [number, number]
                                                                                                        • pad: "valid" | "same"
                                                                                                        • Optionaldilations: number | [number, number]
                                                                                                        • OptionaldataFormat: "NHWC"

                                                                                                        Returns T

                                                                                                      • Disposes tf.Tensor from memory.

                                                                                                        +

                                                                                                        Returns void

                                                                                                      • Type Parameters

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T
                                                                                                        • Optionalaxis: number | number[]
                                                                                                        • OptionalkeepDims: boolean

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • Optionalaxis: number

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        Returns Tensor

                                                                                                      • Type Parameters

                                                                                                        Returns Tensor1D

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        Returns Tensor

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        Returns Tensor

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • alpha: number

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • OptionaldepthRadius: number
                                                                                                        • Optionalbias: number
                                                                                                        • Optionalalpha: number
                                                                                                        • Optionalbeta: number

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T
                                                                                                        • Optionalaxis: number

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T
                                                                                                        • Optionalaxis: number | number[]
                                                                                                        • OptionalkeepDims: boolean

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        Returns Tensor

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • Optionalaxis: number | number[]
                                                                                                        • OptionalkeepDims: boolean

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • filterSize: number | [number, number]
                                                                                                        • strides: number | [number, number]
                                                                                                        • pad: number | "valid" | "same" | ExplicitPadding
                                                                                                        • OptionaldimRoundingMode: "floor" | "round" | "ceil"

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • Optionalaxis: number | number[]
                                                                                                        • OptionalkeepDims: boolean

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • Optionalaxis: number | number[]
                                                                                                        • OptionalkeepDims: boolean

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • paddings: [number, number][]
                                                                                                        • mode: "reflect" | "symmetric"

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • Optionalord: number | "euclidean" | "fro"
                                                                                                        • Optionalaxis: number | number[]
                                                                                                        • OptionalkeepDims: boolean

                                                                                                        Returns Tensor

                                                                                                      • Parameters

                                                                                                        • depth: number
                                                                                                        • onValue: number
                                                                                                        • offValue: number

                                                                                                        Returns Tensor

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • paddings: [number, number][]
                                                                                                        • OptionalconstantValue: number

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • windowShape: number | [number, number]
                                                                                                        • poolingType: "avg" | "max"
                                                                                                        • padding: number | "valid" | "same" | ExplicitPadding
                                                                                                        • OptionaldiationRate: number | [number, number]
                                                                                                        • Optionalstrides: number | [number, number]
                                                                                                        • OptionaldimRoundingMode: "floor" | "round" | "ceil"

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        Returns T

                                                                                                      • Prints the tf.Tensor. See tf.print for details.

                                                                                                        +

                                                                                                        Parameters

                                                                                                        • Optionalverbose: boolean

                                                                                                          Whether to print verbose information about the tensor, +including dtype and size.

                                                                                                          +

                                                                                                        Returns void

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T
                                                                                                        • Optionalaxis: number | number[]
                                                                                                        • OptionalkeepDims: boolean

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • shape: number[]

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • newShape2D: [number, number]
                                                                                                        • OptionalalignCorners: boolean
                                                                                                        • OptionalhalfPixelCenters: boolean

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • newShape2D: [number, number]
                                                                                                        • OptionalalignCorners: boolean
                                                                                                        • OptionalhalfFloatCenters: boolean

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T
                                                                                                        • Optionalaxis: number | number[]

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        Returns Tensor

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • depthwiseFilter: Tensor4D | TensorLike4D
                                                                                                        • pointwiseFilter: TensorLike | Tensor4D
                                                                                                        • strides: number | [number, number]
                                                                                                        • pad: "valid" | "same"
                                                                                                        • Optionaldilation: number | [number, number]
                                                                                                        • OptionaldataFormat: "NHWC" | "NCHW"

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T
                                                                                                        • begin: number | number[]
                                                                                                        • Optionalsize: number | number[]

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T
                                                                                                        • Optionaldim: number

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • blockShape: number[]
                                                                                                        • paddings: number[][]

                                                                                                        Returns Tensor<R>

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • numOrSizeSplits: number | number[]
                                                                                                        • Optionalaxis: number

                                                                                                        Returns T[]

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • Optionalaxis: number[]

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T
                                                                                                        • Optionalalpha: number

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: Tensor
                                                                                                        • begin: number[]
                                                                                                        • end: number[]
                                                                                                        • strides: number[]
                                                                                                        • OptionalbeginMask: number
                                                                                                        • OptionalendMask: number
                                                                                                        • OptionalellipsisMask: number
                                                                                                        • OptionalnewAxisMask: number
                                                                                                        • OptionalshrinkAxisMask: number

                                                                                                        Returns Tensor

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • Optionalaxis: number | number[]
                                                                                                        • OptionalkeepDims: boolean

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Returns void

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • b: number[]

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T
                                                                                                        • Optionalk: number
                                                                                                        • Optionalsorted: boolean

                                                                                                        Returns { indices: T; values: T }

                                                                                                      • Returns a human-readable description of the tensor. Useful for logging.

                                                                                                        +

                                                                                                        Parameters

                                                                                                        • Optionalverbose: boolean

                                                                                                        Returns string

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • Optionalperm: number[]

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T
                                                                                                        • Optionalaxis: number

                                                                                                        Returns { indices: T; values: T }

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T
                                                                                                        • segmentIds: Tensor1D | TensorLike1D
                                                                                                        • numSegments: number

                                                                                                        Returns T

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • Optionalaxis: number

                                                                                                        Returns T[]

                                                                                                      • Parameters

                                                                                                        • Optionaltrainable: boolean
                                                                                                        • Optionalname: string
                                                                                                        • Optionaldtype: keyof DataTypeMap

                                                                                                        Returns Variable<R>

                                                                                                      • Type Parameters

                                                                                                        Parameters

                                                                                                        • this: T

                                                                                                        Returns T

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                                                                                                      Accessors
                                                                                                      Methods
                                                                                                      absacosacoshaddallanyargMaxargMinarrayarraySyncas1Das2Das3Das4Das5DasinasinhasScalarasTypeatanatan2atanhavgPoolbatchNormbatchToSpaceNDbroadcastTobufferbufferSyncbytescastceilclipByValuecloneconcatconv1dconv2dconv2dTransposecoscoshcumprodcumsumdatadataSyncdataToGPUdepthToSpacedepthwiseConv2ddilation2ddisposedivdivNoNandoteluequalerfeuclideanNormexpexpandDimsexpm1fftflattenfloorfloorDivgathergreatergreaterEqualifftirfftisFiniteisInfisNaNleakyRelulesslessEquallocalResponseNormalizationloglog1plogicalAndlogicalNotlogicalOrlogicalXorlogSigmoidlogSoftmaxlogSumExpmatMulmaxmaximummaxPoolmeanminminimummirrorPadmodmulnegnormnotEqualoneHotonesLikepadpoolpowpreluprintprodreciprocalrelurelu6reshapereshapeAsresizeBilinearresizeNearestNeighborreverserfftroundrsqrtseluseparableConv2dsigmoidsignsinsinhslicesoftmaxsoftplusspaceToBatchNDsplitsqrtsquaresquaredDifferencesqueezestackstepstridedSlicesubsumtantanhthrowIfDisposedtiletoBooltoFloattoInttopktoStringtransposeuniqueunsortedSegmentSumunstackvariablewherezerosLike
                                                                                                      diff --git a/typedoc/interfaces/WebCam.html b/typedoc/interfaces/WebCam.html new file mode 100644 index 00000000..78eecfe8 --- /dev/null +++ b/typedoc/interfaces/WebCam.html @@ -0,0 +1,34 @@ +WebCam | @vladmandic/human - v3.3.6
                                                                                                      @vladmandic/human - v3.3.6
                                                                                                        Preparing search index...

                                                                                                        Interface WebCam

                                                                                                        interface WebCam {
                                                                                                            config: WebCamConfig;
                                                                                                            devices: MediaDeviceInfo[];
                                                                                                            element: undefined | HTMLVideoElement;
                                                                                                            stream: undefined | MediaStream;
                                                                                                            get capabilities(): undefined | MediaTrackCapabilities;
                                                                                                            get constraints(): undefined | MediaTrackConstraints;
                                                                                                            get height(): number;
                                                                                                            get label(): string;
                                                                                                            get paused(): boolean;
                                                                                                            get settings(): undefined | MediaTrackSettings;
                                                                                                            get track(): undefined | MediaStreamTrack;
                                                                                                            get width(): number;
                                                                                                            enumerate(): Promise<MediaDeviceInfo[]>;
                                                                                                            pause(): void;
                                                                                                            play(): Promise<void>;
                                                                                                            start(webcamConfig?: Partial<WebCamConfig>): Promise<string>;
                                                                                                            stop(): void;
                                                                                                        }
                                                                                                        Index

                                                                                                        Properties

                                                                                                        config +devices +element +stream +

                                                                                                        Accessors

                                                                                                        capabilities +constraints +height +label +paused +settings +track +width +

                                                                                                        Methods

                                                                                                        enumerate +pause +play +start +stop +

                                                                                                        Properties

                                                                                                        config: WebCamConfig

                                                                                                        current webcam configuration

                                                                                                        +
                                                                                                        devices: MediaDeviceInfo[] = []

                                                                                                        enumerated video devices

                                                                                                        +
                                                                                                        element: undefined | HTMLVideoElement

                                                                                                        instance of dom element associated with webcam stream

                                                                                                        +
                                                                                                        stream: undefined | MediaStream

                                                                                                        active webcam stream

                                                                                                        +

                                                                                                        Accessors

                                                                                                        • get capabilities(): undefined | MediaTrackCapabilities

                                                                                                          get webcam capabilities

                                                                                                          +

                                                                                                          Returns undefined | MediaTrackCapabilities

                                                                                                        • get constraints(): undefined | MediaTrackConstraints

                                                                                                          get webcam constraints

                                                                                                          +

                                                                                                          Returns undefined | MediaTrackConstraints

                                                                                                        • get settings(): undefined | MediaTrackSettings

                                                                                                          get webcam settings

                                                                                                          +

                                                                                                          Returns undefined | MediaTrackSettings

                                                                                                        • get track(): undefined | MediaStreamTrack

                                                                                                          get active webcam stream track

                                                                                                          +

                                                                                                          Returns undefined | MediaStreamTrack

                                                                                                        Methods

                                                                                                        • Returns Promise<MediaDeviceInfo[]>

                                                                                                        • start method initializizes webcam stream and associates it with a dom video element

                                                                                                          +

                                                                                                          Parameters

                                                                                                          Returns Promise<string>

                                                                                                        • stop method stops active webcam stream track and disconnects webcam

                                                                                                          +

                                                                                                          Returns void

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                                                                                                        diff --git a/typedoc/interfaces/WebCamConfig.html b/typedoc/interfaces/WebCamConfig.html index ef92084b..64c7ebdb 100644 --- a/typedoc/interfaces/WebCamConfig.html +++ b/typedoc/interfaces/WebCamConfig.html @@ -1,21 +1,21 @@ -WebCamConfig | @vladmandic/human - v3.3.5

                                                                                                        Interface WebCamConfig

                                                                                                        WebCam configuration

                                                                                                        -
                                                                                                        interface WebCamConfig {
                                                                                                            crop: boolean;
                                                                                                            debug: boolean;
                                                                                                            element: undefined | string | HTMLVideoElement;
                                                                                                            height: number;
                                                                                                            id?: string;
                                                                                                            mode: "front" | "back";
                                                                                                            width: number;
                                                                                                        }

                                                                                                        Properties

                                                                                                        crop -debug -element -height -id? -mode -width -

                                                                                                        Properties

                                                                                                        crop

                                                                                                        crop: boolean

                                                                                                        camera crop mode

                                                                                                        -

                                                                                                        debug

                                                                                                        debug: boolean

                                                                                                        print messages on console

                                                                                                        -

                                                                                                        element

                                                                                                        element: undefined | string | HTMLVideoElement

                                                                                                        element can be:

                                                                                                        +WebCamConfig | @vladmandic/human - v3.3.6
                                                                                                        @vladmandic/human - v3.3.6
                                                                                                          Preparing search index...

                                                                                                          Interface WebCamConfig

                                                                                                          WebCam configuration

                                                                                                          +
                                                                                                          interface WebCamConfig {
                                                                                                              crop: boolean;
                                                                                                              debug: boolean;
                                                                                                              element: undefined | string | HTMLVideoElement;
                                                                                                              height: number;
                                                                                                              id?: string;
                                                                                                              mode: "front" | "back";
                                                                                                              width: number;
                                                                                                          }
                                                                                                          Index

                                                                                                          Properties

                                                                                                          crop +debug +element +height +id? +mode +width +

                                                                                                          Properties

                                                                                                          crop: boolean

                                                                                                          camera crop mode

                                                                                                          +
                                                                                                          debug: boolean

                                                                                                          print messages on console

                                                                                                          +
                                                                                                          element: undefined | string | HTMLVideoElement

                                                                                                          element can be:

                                                                                                          • string which indicates dom element id
                                                                                                          • actual HTMLVideo dom element
                                                                                                          • undefined in which case a new HTMLVideoElement will be created
                                                                                                          -

                                                                                                          height

                                                                                                          height: number

                                                                                                          desired webcam height

                                                                                                          -

                                                                                                          Optionalid

                                                                                                          id?: string

                                                                                                          deviceId of the video device to use

                                                                                                          -

                                                                                                          mode

                                                                                                          mode: "front" | "back"

                                                                                                          use front or back camera

                                                                                                          -

                                                                                                          width

                                                                                                          width: number

                                                                                                          desired webcam width

                                                                                                          -

                                                                                                          Settings

                                                                                                          Member Visibility

                                                                                                          On This Page

                                                                                                          Properties
                                                                                                          +
                                                                                                          height: number

                                                                                                          desired webcam height

                                                                                                          +
                                                                                                          id?: string

                                                                                                          deviceId of the video device to use

                                                                                                          +
                                                                                                          mode: "front" | "back"

                                                                                                          use front or back camera

                                                                                                          +
                                                                                                          width: number

                                                                                                          desired webcam width

                                                                                                          +

                                                                                                          Settings

                                                                                                          Member Visibility

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                                                                                                          Properties
                                                                                                          diff --git a/typedoc/interfaces/models.KernelOps.html b/typedoc/interfaces/models.KernelOps.html index ed85bfd1..402e1785 100644 --- a/typedoc/interfaces/models.KernelOps.html +++ b/typedoc/interfaces/models.KernelOps.html @@ -1,5 +1,5 @@ -KernelOps | @vladmandic/human - v3.3.5

                                                                                                          Interface KernelOps

                                                                                                          interface KernelOps {
                                                                                                              missing: string[];
                                                                                                              name: string;
                                                                                                              ops: string[];
                                                                                                              url: string;
                                                                                                          }

                                                                                                          Properties

                                                                                                          missing -name -ops -url -

                                                                                                          Properties

                                                                                                          missing

                                                                                                          missing: string[]

                                                                                                          name

                                                                                                          name: string

                                                                                                          ops

                                                                                                          ops: string[]

                                                                                                          url

                                                                                                          url: string

                                                                                                          Settings

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                                                                                                          +KernelOps | @vladmandic/human - v3.3.6
                                                                                                          @vladmandic/human - v3.3.6
                                                                                                            Preparing search index...

                                                                                                            Interface KernelOps

                                                                                                            interface KernelOps {
                                                                                                                missing: string[];
                                                                                                                name: string;
                                                                                                                ops: string[];
                                                                                                                url: string;
                                                                                                            }
                                                                                                            Index

                                                                                                            Properties

                                                                                                            missing +name +ops +url +

                                                                                                            Properties

                                                                                                            missing: string[]
                                                                                                            name: string
                                                                                                            ops: string[]
                                                                                                            url: string

                                                                                                            Settings

                                                                                                            Member Visibility

                                                                                                            On This Page

                                                                                                            Properties
                                                                                                            diff --git a/typedoc/interfaces/models.ModelStats.html b/typedoc/interfaces/models.ModelStats.html index 98a47fa6..a99fe26b 100644 --- a/typedoc/interfaces/models.ModelStats.html +++ b/typedoc/interfaces/models.ModelStats.html @@ -1,9 +1,9 @@ -ModelStats | @vladmandic/human - v3.3.5

                                                                                                            Interface ModelStats

                                                                                                            structure that holds global stats for currently loaded models

                                                                                                            -
                                                                                                            interface ModelStats {
                                                                                                                modelStats: ModelInfo[];
                                                                                                                numDefinedModels: number;
                                                                                                                numLoadedModels: number;
                                                                                                                percentageLoaded: number;
                                                                                                                totalSizeFromManifest: number;
                                                                                                                totalSizeLoading: number;
                                                                                                                totalSizeWeights: number;
                                                                                                            }

                                                                                                            Properties

                                                                                                            modelStats -numDefinedModels -numLoadedModels -percentageLoaded -totalSizeFromManifest -totalSizeLoading -totalSizeWeights -

                                                                                                            Properties

                                                                                                            modelStats

                                                                                                            modelStats: ModelInfo[]

                                                                                                            numDefinedModels

                                                                                                            numDefinedModels: number

                                                                                                            numLoadedModels

                                                                                                            numLoadedModels: number

                                                                                                            percentageLoaded

                                                                                                            percentageLoaded: number

                                                                                                            totalSizeFromManifest

                                                                                                            totalSizeFromManifest: number

                                                                                                            totalSizeLoading

                                                                                                            totalSizeLoading: number

                                                                                                            totalSizeWeights

                                                                                                            totalSizeWeights: number

                                                                                                            Settings

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                                                                                                            +ModelStats | @vladmandic/human - v3.3.6
                                                                                                            @vladmandic/human - v3.3.6
                                                                                                              Preparing search index...

                                                                                                              Interface ModelStats

                                                                                                              structure that holds global stats for currently loaded models

                                                                                                              +
                                                                                                              interface ModelStats {
                                                                                                                  modelStats: ModelInfo[];
                                                                                                                  numDefinedModels: number;
                                                                                                                  numLoadedModels: number;
                                                                                                                  percentageLoaded: number;
                                                                                                                  totalSizeFromManifest: number;
                                                                                                                  totalSizeLoading: number;
                                                                                                                  totalSizeWeights: number;
                                                                                                              }
                                                                                                              Index

                                                                                                              Properties

                                                                                                              modelStats +numDefinedModels +numLoadedModels +percentageLoaded +totalSizeFromManifest +totalSizeLoading +totalSizeWeights +

                                                                                                              Properties

                                                                                                              modelStats: ModelInfo[]
                                                                                                              numDefinedModels: number
                                                                                                              numLoadedModels: number
                                                                                                              percentageLoaded: number
                                                                                                              totalSizeFromManifest: number
                                                                                                              totalSizeLoading: number
                                                                                                              totalSizeWeights: number

                                                                                                              Settings

                                                                                                              Member Visibility

                                                                                                              On This Page

                                                                                                              Properties
                                                                                                              diff --git a/typedoc/modules/Tensor.html b/typedoc/modules/Tensor.html index e0643ccf..2fad1e7f 100644 --- a/typedoc/modules/Tensor.html +++ b/typedoc/modules/Tensor.html @@ -1,2 +1,2 @@ -Tensor | @vladmandic/human - v3.3.5

                                                                                                              Namespace Tensor

                                                                                                              Explict reexport of main @tensorflow/tfjs types

                                                                                                              -

                                                                                                              Settings

                                                                                                              Member Visibility
                                                                                                              +Tensor | @vladmandic/human - v3.3.6
                                                                                                              @vladmandic/human - v3.3.6
                                                                                                                Preparing search index...

                                                                                                                Namespace Tensor

                                                                                                                Explict reexport of main @tensorflow/tfjs types

                                                                                                                +

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                                                                                                                Member Visibility
                                                                                                                diff --git a/typedoc/modules/draw.html b/typedoc/modules/draw.html index 54060942..8ff16296 100644 --- a/typedoc/modules/draw.html +++ b/typedoc/modules/draw.html @@ -1,2 +1,2 @@ -draw | @vladmandic/human - v3.3.5

                                                                                                                Namespace draw

                                                                                                                Class Human as default export

                                                                                                                -

                                                                                                                Variables

                                                                                                                options

                                                                                                                Functions

                                                                                                                all
                                                                                                                body
                                                                                                                canvas
                                                                                                                face
                                                                                                                gesture
                                                                                                                hand
                                                                                                                init
                                                                                                                object
                                                                                                                person
                                                                                                                tensor

                                                                                                                Settings

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                                                                                                                On This Page

                                                                                                                Variables
                                                                                                                Functions
                                                                                                                +draw | @vladmandic/human - v3.3.6
                                                                                                                @vladmandic/human - v3.3.6
                                                                                                                  Preparing search index...

                                                                                                                  Namespace draw

                                                                                                                  Class Human as default export

                                                                                                                  +

                                                                                                                  Variables

                                                                                                                  options

                                                                                                                  Functions

                                                                                                                  all
                                                                                                                  body
                                                                                                                  canvas
                                                                                                                  face
                                                                                                                  gesture
                                                                                                                  hand
                                                                                                                  init
                                                                                                                  object
                                                                                                                  person
                                                                                                                  tensor

                                                                                                                  Settings

                                                                                                                  Member Visibility

                                                                                                                  On This Page

                                                                                                                  Variables
                                                                                                                  Functions
                                                                                                                  diff --git a/typedoc/modules/match.html b/typedoc/modules/match.html index 1d560a71..ef947d52 100644 --- a/typedoc/modules/match.html +++ b/typedoc/modules/match.html @@ -1,2 +1,2 @@ -match | @vladmandic/human - v3.3.5

                                                                                                                  Namespace match

                                                                                                                  Class Human as default export

                                                                                                                  -

                                                                                                                  Type Aliases

                                                                                                                  Descriptor
                                                                                                                  MatchOptions

                                                                                                                  Functions

                                                                                                                  distance
                                                                                                                  find
                                                                                                                  similarity

                                                                                                                  Settings

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                                                                                                                  On This Page

                                                                                                                  Type Aliases
                                                                                                                  Functions
                                                                                                                  +match | @vladmandic/human - v3.3.6
                                                                                                                  @vladmandic/human - v3.3.6
                                                                                                                    Preparing search index...

                                                                                                                    Namespace match

                                                                                                                    Class Human as default export

                                                                                                                    +

                                                                                                                    Type Aliases

                                                                                                                    Descriptor
                                                                                                                    MatchOptions

                                                                                                                    Functions

                                                                                                                    distance
                                                                                                                    find
                                                                                                                    similarity

                                                                                                                    Settings

                                                                                                                    Member Visibility

                                                                                                                    On This Page

                                                                                                                    Type Aliases
                                                                                                                    Functions
                                                                                                                    diff --git a/typedoc/modules/models.html b/typedoc/modules/models.html index 5e821b45..627a9c22 100644 --- a/typedoc/modules/models.html +++ b/typedoc/modules/models.html @@ -1,2 +1,2 @@ -models | @vladmandic/human - v3.3.5

                                                                                                                    Namespace models

                                                                                                                    Class Human as default export

                                                                                                                    -

                                                                                                                    Classes

                                                                                                                    Models

                                                                                                                    Interfaces

                                                                                                                    KernelOps
                                                                                                                    ModelStats

                                                                                                                    Functions

                                                                                                                    validateModel

                                                                                                                    Settings

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                                                                                                                    On This Page

                                                                                                                    Classes
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                                                                                                                    +models | @vladmandic/human - v3.3.6
                                                                                                                    @vladmandic/human - v3.3.6
                                                                                                                      Preparing search index...

                                                                                                                      Namespace models

                                                                                                                      Class Human as default export

                                                                                                                      +

                                                                                                                      Classes

                                                                                                                      Models

                                                                                                                      Interfaces

                                                                                                                      KernelOps
                                                                                                                      ModelStats

                                                                                                                      Functions

                                                                                                                      validateModel

                                                                                                                      Settings

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                                                                                                                      On This Page

                                                                                                                      Classes
                                                                                                                      Interfaces
                                                                                                                      Functions
                                                                                                                      diff --git a/typedoc/types/AnyCanvas.html b/typedoc/types/AnyCanvas.html index 8be0e6ea..01441932 100644 --- a/typedoc/types/AnyCanvas.html +++ b/typedoc/types/AnyCanvas.html @@ -1,2 +1,2 @@ -AnyCanvas | @vladmandic/human - v3.3.5

                                                                                                                      Type Alias AnyCanvas

                                                                                                                      AnyCanvas: HTMLCanvasElement | OffscreenCanvas

                                                                                                                      Defines all possible canvas types

                                                                                                                      -

                                                                                                                      Settings

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                                                                                                                      +AnyCanvas | @vladmandic/human - v3.3.6
                                                                                                                      @vladmandic/human - v3.3.6
                                                                                                                        Preparing search index...

                                                                                                                        Type Alias AnyCanvas

                                                                                                                        AnyCanvas: HTMLCanvasElement | OffscreenCanvas

                                                                                                                        Defines all possible canvas types

                                                                                                                        +

                                                                                                                        Settings

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                                                                                                                        diff --git a/typedoc/types/AnyImage.html b/typedoc/types/AnyImage.html index 949e1e1a..04acfbab 100644 --- a/typedoc/types/AnyImage.html +++ b/typedoc/types/AnyImage.html @@ -1,2 +1,2 @@ -AnyImage | @vladmandic/human - v3.3.5

                                                                                                                        Type Alias AnyImage

                                                                                                                        AnyImage: HTMLImageElement | typeof Image

                                                                                                                        Defines all possible image types

                                                                                                                        -

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                                                                                                                        +AnyImage | @vladmandic/human - v3.3.6
                                                                                                                        @vladmandic/human - v3.3.6
                                                                                                                          Preparing search index...

                                                                                                                          Type Alias AnyImage

                                                                                                                          AnyImage: HTMLImageElement | typeof Image

                                                                                                                          Defines all possible image types

                                                                                                                          +

                                                                                                                          Settings

                                                                                                                          Member Visibility
                                                                                                                          diff --git a/typedoc/types/AnyVideo.html b/typedoc/types/AnyVideo.html index cbd2a893..08cbb6cd 100644 --- a/typedoc/types/AnyVideo.html +++ b/typedoc/types/AnyVideo.html @@ -1,2 +1,2 @@ -AnyVideo | @vladmandic/human - v3.3.5

                                                                                                                          Type Alias AnyVideo

                                                                                                                          AnyVideo: HTMLMediaElement | HTMLVideoElement

                                                                                                                          Defines all possible video types

                                                                                                                          -

                                                                                                                          Settings

                                                                                                                          Member Visibility
                                                                                                                          +AnyVideo | @vladmandic/human - v3.3.6
                                                                                                                          @vladmandic/human - v3.3.6
                                                                                                                            Preparing search index...

                                                                                                                            Type Alias AnyVideo

                                                                                                                            AnyVideo: HTMLMediaElement | HTMLVideoElement

                                                                                                                            Defines all possible video types

                                                                                                                            +

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                                                                                                                            diff --git a/typedoc/types/BackendEnum.html b/typedoc/types/BackendEnum.html index b5ae9c02..4edf039f 100644 --- a/typedoc/types/BackendEnum.html +++ b/typedoc/types/BackendEnum.html @@ -1,2 +1,2 @@ -BackendEnum | @vladmandic/human - v3.3.5

                                                                                                                            Type Alias BackendEnum

                                                                                                                            BackendEnum:
                                                                                                                                | ""
                                                                                                                                | "cpu"
                                                                                                                                | "wasm"
                                                                                                                                | "webgl"
                                                                                                                                | "humangl"
                                                                                                                                | "tensorflow"
                                                                                                                                | "webgpu"
                                                                                                                                | "none"

                                                                                                                            Possible TensorFlow backends

                                                                                                                            -

                                                                                                                            Settings

                                                                                                                            Member Visibility
                                                                                                                            +BackendEnum | @vladmandic/human - v3.3.6
                                                                                                                            @vladmandic/human - v3.3.6
                                                                                                                              Preparing search index...

                                                                                                                              Type Alias BackendEnum

                                                                                                                              BackendEnum:
                                                                                                                                  | ""
                                                                                                                                  | "cpu"
                                                                                                                                  | "wasm"
                                                                                                                                  | "webgl"
                                                                                                                                  | "humangl"
                                                                                                                                  | "tensorflow"
                                                                                                                                  | "webgpu"
                                                                                                                                  | "none"

                                                                                                                              Possible TensorFlow backends

                                                                                                                              +

                                                                                                                              Settings

                                                                                                                              Member Visibility
                                                                                                                              diff --git a/typedoc/types/BodyAnnotation.html b/typedoc/types/BodyAnnotation.html index f5923a8a..01132a20 100644 --- a/typedoc/types/BodyAnnotation.html +++ b/typedoc/types/BodyAnnotation.html @@ -1 +1 @@ -BodyAnnotation | @vladmandic/human - v3.3.5

                                                                                                                              Type Alias BodyAnnotation

                                                                                                                              BodyAnnotation: BodyAnnotationBlazePose | BodyAnnotationEfficientPose

                                                                                                                              Settings

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                                                                                                                              +BodyAnnotation | @vladmandic/human - v3.3.6
                                                                                                                              @vladmandic/human - v3.3.6
                                                                                                                                Preparing search index...

                                                                                                                                Type Alias BodyAnnotation

                                                                                                                                BodyAnnotation: BodyAnnotationBlazePose | BodyAnnotationEfficientPose

                                                                                                                                Settings

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                                                                                                                                diff --git a/typedoc/types/BodyAnnotationBlazePose.html b/typedoc/types/BodyAnnotationBlazePose.html index 4d8ca346..15a50f26 100644 --- a/typedoc/types/BodyAnnotationBlazePose.html +++ b/typedoc/types/BodyAnnotationBlazePose.html @@ -1 +1 @@ -BodyAnnotationBlazePose | @vladmandic/human - v3.3.5

                                                                                                                                Type Alias BodyAnnotationBlazePose

                                                                                                                                BodyAnnotationBlazePose:
                                                                                                                                    | "leftLeg"
                                                                                                                                    | "rightLeg"
                                                                                                                                    | "torso"
                                                                                                                                    | "leftArm"
                                                                                                                                    | "rightArm"
                                                                                                                                    | "leftEye"
                                                                                                                                    | "rightEye"
                                                                                                                                    | "mouth"

                                                                                                                                Settings

                                                                                                                                Member Visibility
                                                                                                                                +BodyAnnotationBlazePose | @vladmandic/human - v3.3.6
                                                                                                                                @vladmandic/human - v3.3.6
                                                                                                                                  Preparing search index...

                                                                                                                                  Type Alias BodyAnnotationBlazePose

                                                                                                                                  BodyAnnotationBlazePose:
                                                                                                                                      | "leftLeg"
                                                                                                                                      | "rightLeg"
                                                                                                                                      | "torso"
                                                                                                                                      | "leftArm"
                                                                                                                                      | "rightArm"
                                                                                                                                      | "leftEye"
                                                                                                                                      | "rightEye"
                                                                                                                                      | "mouth"

                                                                                                                                  Settings

                                                                                                                                  Member Visibility
                                                                                                                                  diff --git a/typedoc/types/BodyAnnotationEfficientPose.html b/typedoc/types/BodyAnnotationEfficientPose.html index e8b68390..245d9310 100644 --- a/typedoc/types/BodyAnnotationEfficientPose.html +++ b/typedoc/types/BodyAnnotationEfficientPose.html @@ -1 +1 @@ -BodyAnnotationEfficientPose | @vladmandic/human - v3.3.5

                                                                                                                                  Type Alias BodyAnnotationEfficientPose

                                                                                                                                  BodyAnnotationEfficientPose:
                                                                                                                                      | "leftLeg"
                                                                                                                                      | "rightLeg"
                                                                                                                                      | "torso"
                                                                                                                                      | "leftArm"
                                                                                                                                      | "rightArm"
                                                                                                                                      | "head"

                                                                                                                                  Settings

                                                                                                                                  Member Visibility
                                                                                                                                  +BodyAnnotationEfficientPose | @vladmandic/human - v3.3.6
                                                                                                                                  @vladmandic/human - v3.3.6
                                                                                                                                    Preparing search index...

                                                                                                                                    Type Alias BodyAnnotationEfficientPose

                                                                                                                                    BodyAnnotationEfficientPose:
                                                                                                                                        | "leftLeg"
                                                                                                                                        | "rightLeg"
                                                                                                                                        | "torso"
                                                                                                                                        | "leftArm"
                                                                                                                                        | "rightArm"
                                                                                                                                        | "head"

                                                                                                                                    Settings

                                                                                                                                    Member Visibility
                                                                                                                                    diff --git a/typedoc/types/BodyGesture.html b/typedoc/types/BodyGesture.html index 6bb97295..eb4fdccd 100644 --- a/typedoc/types/BodyGesture.html +++ b/typedoc/types/BodyGesture.html @@ -1,2 +1,2 @@ -BodyGesture | @vladmandic/human - v3.3.5

                                                                                                                                    Type Alias BodyGesture

                                                                                                                                    BodyGesture:
                                                                                                                                        | `leaning ${"left"
                                                                                                                                        | "right"}`
                                                                                                                                        | `raise ${"left" | "right"} hand`
                                                                                                                                        | "i give up"

                                                                                                                                    body gesture type

                                                                                                                                    -

                                                                                                                                    Settings

                                                                                                                                    Member Visibility
                                                                                                                                    +BodyGesture | @vladmandic/human - v3.3.6
                                                                                                                                    @vladmandic/human - v3.3.6
                                                                                                                                      Preparing search index...

                                                                                                                                      Type Alias BodyGesture

                                                                                                                                      BodyGesture:
                                                                                                                                          | `leaning ${"left"
                                                                                                                                          | "right"}`
                                                                                                                                          | `raise ${"left" | "right"} hand`
                                                                                                                                          | "i give up"

                                                                                                                                      body gesture type

                                                                                                                                      +

                                                                                                                                      Settings

                                                                                                                                      Member Visibility
                                                                                                                                      diff --git a/typedoc/types/BodyLandmark.html b/typedoc/types/BodyLandmark.html index 00da03c1..ab10b210 100644 --- a/typedoc/types/BodyLandmark.html +++ b/typedoc/types/BodyLandmark.html @@ -1 +1 @@ -BodyLandmark | @vladmandic/human - v3.3.5

                                                                                                                                      Type Alias BodyLandmark

                                                                                                                                      BodyLandmark:
                                                                                                                                          | BodyLandmarkPoseNet
                                                                                                                                          | BodyLandmarkMoveNet
                                                                                                                                          | BodyLandmarkEfficientNet
                                                                                                                                          | BodyLandmarkBlazePose

                                                                                                                                      Settings

                                                                                                                                      Member Visibility
                                                                                                                                      +BodyLandmark | @vladmandic/human - v3.3.6
                                                                                                                                      @vladmandic/human - v3.3.6
                                                                                                                                        Preparing search index...

                                                                                                                                        Type Alias BodyLandmark

                                                                                                                                        BodyLandmark:
                                                                                                                                            | BodyLandmarkPoseNet
                                                                                                                                            | BodyLandmarkMoveNet
                                                                                                                                            | BodyLandmarkEfficientNet
                                                                                                                                            | BodyLandmarkBlazePose

                                                                                                                                        Settings

                                                                                                                                        Member Visibility
                                                                                                                                        diff --git a/typedoc/types/BodyLandmarkBlazePose.html b/typedoc/types/BodyLandmarkBlazePose.html index 7acd9b3d..bae8b918 100644 --- a/typedoc/types/BodyLandmarkBlazePose.html +++ b/typedoc/types/BodyLandmarkBlazePose.html @@ -1 +1 @@ -BodyLandmarkBlazePose | @vladmandic/human - v3.3.5

                                                                                                                                        Type Alias BodyLandmarkBlazePose

                                                                                                                                        BodyLandmarkBlazePose:
                                                                                                                                            | "nose"
                                                                                                                                            | "leftEyeInside"
                                                                                                                                            | "leftEye"
                                                                                                                                            | "leftEyeOutside"
                                                                                                                                            | "rightEyeInside"
                                                                                                                                            | "rightEye"
                                                                                                                                            | "rightEyeOutside"
                                                                                                                                            | "leftEar"
                                                                                                                                            | "rightEar"
                                                                                                                                            | "leftMouth"
                                                                                                                                            | "rightMouth"
                                                                                                                                            | "leftShoulder"
                                                                                                                                            | "rightShoulder"
                                                                                                                                            | "leftElbow"
                                                                                                                                            | "rightElbow"
                                                                                                                                            | "leftWrist"
                                                                                                                                            | "rightWrist"
                                                                                                                                            | "leftPinky"
                                                                                                                                            | "rightPinky"
                                                                                                                                            | "leftIndex"
                                                                                                                                            | "rightIndex"
                                                                                                                                            | "leftThumb"
                                                                                                                                            | "rightThumb"
                                                                                                                                            | "leftHip"
                                                                                                                                            | "rightHip"
                                                                                                                                            | "leftKnee"
                                                                                                                                            | "rightKnee"
                                                                                                                                            | "leftAnkle"
                                                                                                                                            | "rightAnkle"
                                                                                                                                            | "leftHeel"
                                                                                                                                            | "rightHeel"
                                                                                                                                            | "leftFoot"
                                                                                                                                            | "rightFoot"
                                                                                                                                            | "bodyCenter"
                                                                                                                                            | "bodyTop"
                                                                                                                                            | "leftPalm"
                                                                                                                                            | "leftHand"
                                                                                                                                            | "rightPalm"
                                                                                                                                            | "rightHand"

                                                                                                                                        Settings

                                                                                                                                        Member Visibility
                                                                                                                                        +BodyLandmarkBlazePose | @vladmandic/human - v3.3.6
                                                                                                                                        @vladmandic/human - v3.3.6
                                                                                                                                          Preparing search index...

                                                                                                                                          Type Alias BodyLandmarkBlazePose

                                                                                                                                          BodyLandmarkBlazePose:
                                                                                                                                              | "nose"
                                                                                                                                              | "leftEyeInside"
                                                                                                                                              | "leftEye"
                                                                                                                                              | "leftEyeOutside"
                                                                                                                                              | "rightEyeInside"
                                                                                                                                              | "rightEye"
                                                                                                                                              | "rightEyeOutside"
                                                                                                                                              | "leftEar"
                                                                                                                                              | "rightEar"
                                                                                                                                              | "leftMouth"
                                                                                                                                              | "rightMouth"
                                                                                                                                              | "leftShoulder"
                                                                                                                                              | "rightShoulder"
                                                                                                                                              | "leftElbow"
                                                                                                                                              | "rightElbow"
                                                                                                                                              | "leftWrist"
                                                                                                                                              | "rightWrist"
                                                                                                                                              | "leftPinky"
                                                                                                                                              | "rightPinky"
                                                                                                                                              | "leftIndex"
                                                                                                                                              | "rightIndex"
                                                                                                                                              | "leftThumb"
                                                                                                                                              | "rightThumb"
                                                                                                                                              | "leftHip"
                                                                                                                                              | "rightHip"
                                                                                                                                              | "leftKnee"
                                                                                                                                              | "rightKnee"
                                                                                                                                              | "leftAnkle"
                                                                                                                                              | "rightAnkle"
                                                                                                                                              | "leftHeel"
                                                                                                                                              | "rightHeel"
                                                                                                                                              | "leftFoot"
                                                                                                                                              | "rightFoot"
                                                                                                                                              | "bodyCenter"
                                                                                                                                              | "bodyTop"
                                                                                                                                              | "leftPalm"
                                                                                                                                              | "leftHand"
                                                                                                                                              | "rightPalm"
                                                                                                                                              | "rightHand"

                                                                                                                                          Settings

                                                                                                                                          Member Visibility
                                                                                                                                          diff --git a/typedoc/types/BodyLandmarkEfficientNet.html b/typedoc/types/BodyLandmarkEfficientNet.html index ca0b373a..483d41af 100644 --- a/typedoc/types/BodyLandmarkEfficientNet.html +++ b/typedoc/types/BodyLandmarkEfficientNet.html @@ -1 +1 @@ -BodyLandmarkEfficientNet | @vladmandic/human - v3.3.5

                                                                                                                                          Type Alias BodyLandmarkEfficientNet

                                                                                                                                          BodyLandmarkEfficientNet:
                                                                                                                                              | "head"
                                                                                                                                              | "neck"
                                                                                                                                              | "rightShoulder"
                                                                                                                                              | "rightElbow"
                                                                                                                                              | "rightWrist"
                                                                                                                                              | "chest"
                                                                                                                                              | "leftShoulder"
                                                                                                                                              | "leftElbow"
                                                                                                                                              | "leftWrist"
                                                                                                                                              | "bodyCenter"
                                                                                                                                              | "rightHip"
                                                                                                                                              | "rightKnee"
                                                                                                                                              | "rightAnkle"
                                                                                                                                              | "leftHip"
                                                                                                                                              | "leftKnee"
                                                                                                                                              | "leftAnkle"

                                                                                                                                          Settings

                                                                                                                                          Member Visibility
                                                                                                                                          +BodyLandmarkEfficientNet | @vladmandic/human - v3.3.6
                                                                                                                                          @vladmandic/human - v3.3.6
                                                                                                                                            Preparing search index...

                                                                                                                                            Type Alias BodyLandmarkEfficientNet

                                                                                                                                            BodyLandmarkEfficientNet:
                                                                                                                                                | "head"
                                                                                                                                                | "neck"
                                                                                                                                                | "rightShoulder"
                                                                                                                                                | "rightElbow"
                                                                                                                                                | "rightWrist"
                                                                                                                                                | "chest"
                                                                                                                                                | "leftShoulder"
                                                                                                                                                | "leftElbow"
                                                                                                                                                | "leftWrist"
                                                                                                                                                | "bodyCenter"
                                                                                                                                                | "rightHip"
                                                                                                                                                | "rightKnee"
                                                                                                                                                | "rightAnkle"
                                                                                                                                                | "leftHip"
                                                                                                                                                | "leftKnee"
                                                                                                                                                | "leftAnkle"

                                                                                                                                            Settings

                                                                                                                                            Member Visibility
                                                                                                                                            diff --git a/typedoc/types/BodyLandmarkMoveNet.html b/typedoc/types/BodyLandmarkMoveNet.html index 5766911f..a5d95d1b 100644 --- a/typedoc/types/BodyLandmarkMoveNet.html +++ b/typedoc/types/BodyLandmarkMoveNet.html @@ -1 +1 @@ -BodyLandmarkMoveNet | @vladmandic/human - v3.3.5

                                                                                                                                            Type Alias BodyLandmarkMoveNet

                                                                                                                                            BodyLandmarkMoveNet:
                                                                                                                                                | "nose"
                                                                                                                                                | "leftEye"
                                                                                                                                                | "rightEye"
                                                                                                                                                | "leftEar"
                                                                                                                                                | "rightEar"
                                                                                                                                                | "leftShoulder"
                                                                                                                                                | "rightShoulder"
                                                                                                                                                | "leftElbow"
                                                                                                                                                | "rightElbow"
                                                                                                                                                | "leftWrist"
                                                                                                                                                | "rightWrist"
                                                                                                                                                | "leftHip"
                                                                                                                                                | "rightHip"
                                                                                                                                                | "leftKnee"
                                                                                                                                                | "rightKnee"
                                                                                                                                                | "leftAnkle"
                                                                                                                                                | "rightAnkle"

                                                                                                                                            Settings

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                                                                                                                                            +BodyLandmarkMoveNet | @vladmandic/human - v3.3.6
                                                                                                                                            @vladmandic/human - v3.3.6
                                                                                                                                              Preparing search index...

                                                                                                                                              Type Alias BodyLandmarkMoveNet

                                                                                                                                              BodyLandmarkMoveNet:
                                                                                                                                                  | "nose"
                                                                                                                                                  | "leftEye"
                                                                                                                                                  | "rightEye"
                                                                                                                                                  | "leftEar"
                                                                                                                                                  | "rightEar"
                                                                                                                                                  | "leftShoulder"
                                                                                                                                                  | "rightShoulder"
                                                                                                                                                  | "leftElbow"
                                                                                                                                                  | "rightElbow"
                                                                                                                                                  | "leftWrist"
                                                                                                                                                  | "rightWrist"
                                                                                                                                                  | "leftHip"
                                                                                                                                                  | "rightHip"
                                                                                                                                                  | "leftKnee"
                                                                                                                                                  | "rightKnee"
                                                                                                                                                  | "leftAnkle"
                                                                                                                                                  | "rightAnkle"

                                                                                                                                              Settings

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                                                                                                                                              diff --git a/typedoc/types/BodyLandmarkPoseNet.html b/typedoc/types/BodyLandmarkPoseNet.html index ef8de922..85a382f0 100644 --- a/typedoc/types/BodyLandmarkPoseNet.html +++ b/typedoc/types/BodyLandmarkPoseNet.html @@ -1 +1 @@ -BodyLandmarkPoseNet | @vladmandic/human - v3.3.5

                                                                                                                                              Type Alias BodyLandmarkPoseNet

                                                                                                                                              BodyLandmarkPoseNet:
                                                                                                                                                  | "nose"
                                                                                                                                                  | "leftEye"
                                                                                                                                                  | "rightEye"
                                                                                                                                                  | "leftEar"
                                                                                                                                                  | "rightEar"
                                                                                                                                                  | "leftShoulder"
                                                                                                                                                  | "rightShoulder"
                                                                                                                                                  | "leftElbow"
                                                                                                                                                  | "rightElbow"
                                                                                                                                                  | "leftWrist"
                                                                                                                                                  | "rightWrist"
                                                                                                                                                  | "leftHip"
                                                                                                                                                  | "rightHip"
                                                                                                                                                  | "leftKnee"
                                                                                                                                                  | "rightKnee"
                                                                                                                                                  | "leftAnkle"
                                                                                                                                                  | "rightAnkle"

                                                                                                                                              Settings

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                                                                                                                                              +BodyLandmarkPoseNet | @vladmandic/human - v3.3.6
                                                                                                                                              @vladmandic/human - v3.3.6
                                                                                                                                                Preparing search index...

                                                                                                                                                Type Alias BodyLandmarkPoseNet

                                                                                                                                                BodyLandmarkPoseNet:
                                                                                                                                                    | "nose"
                                                                                                                                                    | "leftEye"
                                                                                                                                                    | "rightEye"
                                                                                                                                                    | "leftEar"
                                                                                                                                                    | "rightEar"
                                                                                                                                                    | "leftShoulder"
                                                                                                                                                    | "rightShoulder"
                                                                                                                                                    | "leftElbow"
                                                                                                                                                    | "rightElbow"
                                                                                                                                                    | "leftWrist"
                                                                                                                                                    | "rightWrist"
                                                                                                                                                    | "leftHip"
                                                                                                                                                    | "rightHip"
                                                                                                                                                    | "leftKnee"
                                                                                                                                                    | "rightKnee"
                                                                                                                                                    | "leftAnkle"
                                                                                                                                                    | "rightAnkle"

                                                                                                                                                Settings

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                                                                                                                                                diff --git a/typedoc/types/Box.html b/typedoc/types/Box.html index 5f65ba97..2a0f7799 100644 --- a/typedoc/types/Box.html +++ b/typedoc/types/Box.html @@ -1,2 +1,2 @@ -Box | @vladmandic/human - v3.3.5

                                                                                                                                                Type Alias Box

                                                                                                                                                Box: [number, number, number, number]

                                                                                                                                                generic box as [x, y, width, height]

                                                                                                                                                -

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                                                                                                                                                +Box | @vladmandic/human - v3.3.6
                                                                                                                                                @vladmandic/human - v3.3.6
                                                                                                                                                  Preparing search index...

                                                                                                                                                  Type Alias Box

                                                                                                                                                  Box: [number, number, number, number]

                                                                                                                                                  generic box as [x, y, width, height]

                                                                                                                                                  +

                                                                                                                                                  Settings

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                                                                                                                                                  diff --git a/typedoc/types/Emotion.html b/typedoc/types/Emotion.html index fbf77406..dbc5b02e 100644 --- a/typedoc/types/Emotion.html +++ b/typedoc/types/Emotion.html @@ -1 +1 @@ -Emotion | @vladmandic/human - v3.3.5

                                                                                                                                                  Type Alias Emotion

                                                                                                                                                  Emotion: "angry" | "disgust" | "fear" | "happy" | "sad" | "surprise" | "neutral"

                                                                                                                                                  Settings

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                                                                                                                                                  +Emotion | @vladmandic/human - v3.3.6
                                                                                                                                                  @vladmandic/human - v3.3.6
                                                                                                                                                    Preparing search index...

                                                                                                                                                    Type Alias Emotion

                                                                                                                                                    Emotion: "angry" | "disgust" | "fear" | "happy" | "sad" | "surprise" | "neutral"

                                                                                                                                                    Settings

                                                                                                                                                    Member Visibility
                                                                                                                                                    diff --git a/typedoc/types/Events.html b/typedoc/types/Events.html index 3e15575d..c433ed95 100644 --- a/typedoc/types/Events.html +++ b/typedoc/types/Events.html @@ -1,4 +1,4 @@ -Events | @vladmandic/human - v3.3.5

                                                                                                                                                    Type Alias Events

                                                                                                                                                    Events: "create" | "load" | "image" | "result" | "warmup" | "error"

                                                                                                                                                    Events dispatched by human.events

                                                                                                                                                    +Events | @vladmandic/human - v3.3.6
                                                                                                                                                    @vladmandic/human - v3.3.6
                                                                                                                                                      Preparing search index...

                                                                                                                                                      Type Alias Events

                                                                                                                                                      Events: "create" | "load" | "image" | "result" | "warmup" | "error"

                                                                                                                                                      Events dispatched by human.events

                                                                                                                                                      • create: triggered when Human object is instantiated
                                                                                                                                                      • load: triggered when models are loaded (explicitly or on-demand)
                                                                                                                                                        • @@ -6,4 +6,4 @@
                                                                                                                                                      • result: triggered when detection is complete
                                                                                                                                                      • warmup: triggered when warmup is complete
                                                                                                                                                      -

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                                                                                                                                                      +

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                                                                                                                                                      diff --git a/typedoc/types/ExternalCanvas.html b/typedoc/types/ExternalCanvas.html index b36b974a..e05ef698 100644 --- a/typedoc/types/ExternalCanvas.html +++ b/typedoc/types/ExternalCanvas.html @@ -1,2 +1,2 @@ -ExternalCanvas | @vladmandic/human - v3.3.5

                                                                                                                                                      Type Alias ExternalCanvas

                                                                                                                                                      ExternalCanvas: typeof Canvas

                                                                                                                                                      Defines possible externally defined canvas

                                                                                                                                                      -

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                                                                                                                                                      +ExternalCanvas | @vladmandic/human - v3.3.6
                                                                                                                                                      @vladmandic/human - v3.3.6
                                                                                                                                                        Preparing search index...

                                                                                                                                                        Type Alias ExternalCanvas

                                                                                                                                                        ExternalCanvas: typeof Canvas

                                                                                                                                                        Defines possible externally defined canvas

                                                                                                                                                        +

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                                                                                                                                                        diff --git a/typedoc/types/FaceGesture.html b/typedoc/types/FaceGesture.html index 937aa12c..d6a2e0b3 100644 --- a/typedoc/types/FaceGesture.html +++ b/typedoc/types/FaceGesture.html @@ -1,2 +1,2 @@ -FaceGesture | @vladmandic/human - v3.3.5

                                                                                                                                                        Type Alias FaceGesture

                                                                                                                                                        FaceGesture:
                                                                                                                                                            | `facing ${"left"
                                                                                                                                                            | "center"
                                                                                                                                                            | "right"}`
                                                                                                                                                            | `blink ${"left" | "right"} eye`
                                                                                                                                                            | `mouth ${number}% open`
                                                                                                                                                            | `head ${"up" | "down"}`

                                                                                                                                                        face gesture type

                                                                                                                                                        -

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                                                                                                                                                        +FaceGesture | @vladmandic/human - v3.3.6
                                                                                                                                                        @vladmandic/human - v3.3.6
                                                                                                                                                          Preparing search index...

                                                                                                                                                          Type Alias FaceGesture

                                                                                                                                                          FaceGesture:
                                                                                                                                                              | `facing ${"left"
                                                                                                                                                              | "center"
                                                                                                                                                              | "right"}`
                                                                                                                                                              | `blink ${"left" | "right"} eye`
                                                                                                                                                              | `mouth ${number}% open`
                                                                                                                                                              | `head ${"up" | "down"}`

                                                                                                                                                          face gesture type

                                                                                                                                                          +

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                                                                                                                                                          diff --git a/typedoc/types/FaceLandmark.html b/typedoc/types/FaceLandmark.html index c98b5478..3bee2dd6 100644 --- a/typedoc/types/FaceLandmark.html +++ b/typedoc/types/FaceLandmark.html @@ -1 +1 @@ -FaceLandmark | @vladmandic/human - v3.3.5

                                                                                                                                                          Type Alias FaceLandmark

                                                                                                                                                          FaceLandmark:
                                                                                                                                                              | "leftEye"
                                                                                                                                                              | "rightEye"
                                                                                                                                                              | "nose"
                                                                                                                                                              | "mouth"
                                                                                                                                                              | "leftEar"
                                                                                                                                                              | "rightEar"
                                                                                                                                                              | "symmetryLine"
                                                                                                                                                              | "silhouette"
                                                                                                                                                              | "lipsUpperOuter"
                                                                                                                                                              | "lipsLowerOuter"
                                                                                                                                                              | "lipsUpperInner"
                                                                                                                                                              | "lipsLowerInner"
                                                                                                                                                              | "rightEyeUpper0"
                                                                                                                                                              | "rightEyeLower0"
                                                                                                                                                              | "rightEyeUpper1"
                                                                                                                                                              | "rightEyeLower1"
                                                                                                                                                              | "rightEyeUpper2"
                                                                                                                                                              | "rightEyeLower2"
                                                                                                                                                              | "rightEyeLower3"
                                                                                                                                                              | "rightEyebrowUpper"
                                                                                                                                                              | "rightEyebrowLower"
                                                                                                                                                              | "rightEyeIris"
                                                                                                                                                              | "leftEyeUpper0"
                                                                                                                                                              | "leftEyeLower0"
                                                                                                                                                              | "leftEyeUpper1"
                                                                                                                                                              | "leftEyeLower1"
                                                                                                                                                              | "leftEyeUpper2"
                                                                                                                                                              | "leftEyeLower2"
                                                                                                                                                              | "leftEyeLower3"
                                                                                                                                                              | "leftEyebrowUpper"
                                                                                                                                                              | "leftEyebrowLower"
                                                                                                                                                              | "leftEyeIris"
                                                                                                                                                              | "midwayBetweenEyes"
                                                                                                                                                              | "noseTip"
                                                                                                                                                              | "noseBottom"
                                                                                                                                                              | "noseRightCorner"
                                                                                                                                                              | "noseLeftCorner"
                                                                                                                                                              | "rightCheek"
                                                                                                                                                              | "leftCheek"

                                                                                                                                                          Settings

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                                                                                                                                                          +FaceLandmark | @vladmandic/human - v3.3.6
                                                                                                                                                          @vladmandic/human - v3.3.6
                                                                                                                                                            Preparing search index...

                                                                                                                                                            Type Alias FaceLandmark

                                                                                                                                                            FaceLandmark:
                                                                                                                                                                | "leftEye"
                                                                                                                                                                | "rightEye"
                                                                                                                                                                | "nose"
                                                                                                                                                                | "mouth"
                                                                                                                                                                | "leftEar"
                                                                                                                                                                | "rightEar"
                                                                                                                                                                | "symmetryLine"
                                                                                                                                                                | "silhouette"
                                                                                                                                                                | "lipsUpperOuter"
                                                                                                                                                                | "lipsLowerOuter"
                                                                                                                                                                | "lipsUpperInner"
                                                                                                                                                                | "lipsLowerInner"
                                                                                                                                                                | "rightEyeUpper0"
                                                                                                                                                                | "rightEyeLower0"
                                                                                                                                                                | "rightEyeUpper1"
                                                                                                                                                                | "rightEyeLower1"
                                                                                                                                                                | "rightEyeUpper2"
                                                                                                                                                                | "rightEyeLower2"
                                                                                                                                                                | "rightEyeLower3"
                                                                                                                                                                | "rightEyebrowUpper"
                                                                                                                                                                | "rightEyebrowLower"
                                                                                                                                                                | "rightEyeIris"
                                                                                                                                                                | "leftEyeUpper0"
                                                                                                                                                                | "leftEyeLower0"
                                                                                                                                                                | "leftEyeUpper1"
                                                                                                                                                                | "leftEyeLower1"
                                                                                                                                                                | "leftEyeUpper2"
                                                                                                                                                                | "leftEyeLower2"
                                                                                                                                                                | "leftEyeLower3"
                                                                                                                                                                | "leftEyebrowUpper"
                                                                                                                                                                | "leftEyebrowLower"
                                                                                                                                                                | "leftEyeIris"
                                                                                                                                                                | "midwayBetweenEyes"
                                                                                                                                                                | "noseTip"
                                                                                                                                                                | "noseBottom"
                                                                                                                                                                | "noseRightCorner"
                                                                                                                                                                | "noseLeftCorner"
                                                                                                                                                                | "rightCheek"
                                                                                                                                                                | "leftCheek"

                                                                                                                                                            Settings

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                                                                                                                                                            diff --git a/typedoc/types/Finger.html b/typedoc/types/Finger.html index 092ca513..fa527d77 100644 --- a/typedoc/types/Finger.html +++ b/typedoc/types/Finger.html @@ -1 +1 @@ -Finger | @vladmandic/human - v3.3.5

                                                                                                                                                            Type Alias Finger

                                                                                                                                                            Finger: "index" | "middle" | "pinky" | "ring" | "thumb" | "palm"

                                                                                                                                                            Settings

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                                                                                                                                                            +Finger | @vladmandic/human - v3.3.6
                                                                                                                                                            @vladmandic/human - v3.3.6
                                                                                                                                                              Preparing search index...

                                                                                                                                                              Type Alias Finger

                                                                                                                                                              Finger: "index" | "middle" | "pinky" | "ring" | "thumb" | "palm"

                                                                                                                                                              Settings

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                                                                                                                                                              diff --git a/typedoc/types/FingerCurl.html b/typedoc/types/FingerCurl.html index e7e1d57d..a44caa0f 100644 --- a/typedoc/types/FingerCurl.html +++ b/typedoc/types/FingerCurl.html @@ -1 +1 @@ -FingerCurl | @vladmandic/human - v3.3.5

                                                                                                                                                              Type Alias FingerCurl

                                                                                                                                                              FingerCurl: "none" | "half" | "full"

                                                                                                                                                              Settings

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                                                                                                                                                              +FingerCurl | @vladmandic/human - v3.3.6
                                                                                                                                                              @vladmandic/human - v3.3.6
                                                                                                                                                                Preparing search index...

                                                                                                                                                                Type Alias FingerCurl

                                                                                                                                                                FingerCurl: "none" | "half" | "full"

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                                                                                                                                                                diff --git a/typedoc/types/FingerDirection.html b/typedoc/types/FingerDirection.html index 9966923d..8015234d 100644 --- a/typedoc/types/FingerDirection.html +++ b/typedoc/types/FingerDirection.html @@ -1 +1 @@ -FingerDirection | @vladmandic/human - v3.3.5

                                                                                                                                                                Type Alias FingerDirection

                                                                                                                                                                FingerDirection:
                                                                                                                                                                    | "verticalUp"
                                                                                                                                                                    | "verticalDown"
                                                                                                                                                                    | "horizontalLeft"
                                                                                                                                                                    | "horizontalRight"
                                                                                                                                                                    | "diagonalUpRight"
                                                                                                                                                                    | "diagonalUpLeft"
                                                                                                                                                                    | "diagonalDownRight"
                                                                                                                                                                    | "diagonalDownLeft"

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                                                                                                                                                                +FingerDirection | @vladmandic/human - v3.3.6
                                                                                                                                                                @vladmandic/human - v3.3.6
                                                                                                                                                                  Preparing search index...

                                                                                                                                                                  Type Alias FingerDirection

                                                                                                                                                                  FingerDirection:
                                                                                                                                                                      | "verticalUp"
                                                                                                                                                                      | "verticalDown"
                                                                                                                                                                      | "horizontalLeft"
                                                                                                                                                                      | "horizontalRight"
                                                                                                                                                                      | "diagonalUpRight"
                                                                                                                                                                      | "diagonalUpLeft"
                                                                                                                                                                      | "diagonalDownRight"
                                                                                                                                                                      | "diagonalDownLeft"

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                                                                                                                                                                  diff --git a/typedoc/types/Gender.html b/typedoc/types/Gender.html index 245370fa..622f999a 100644 --- a/typedoc/types/Gender.html +++ b/typedoc/types/Gender.html @@ -1 +1 @@ -Gender | @vladmandic/human - v3.3.5

                                                                                                                                                                  Type Alias Gender

                                                                                                                                                                  Gender: "male" | "female" | "unknown"

                                                                                                                                                                  Settings

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                                                                                                                                                                  +Gender | @vladmandic/human - v3.3.6
                                                                                                                                                                  @vladmandic/human - v3.3.6
                                                                                                                                                                    Preparing search index...

                                                                                                                                                                    Type Alias Gender

                                                                                                                                                                    Gender: "male" | "female" | "unknown"

                                                                                                                                                                    Settings

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                                                                                                                                                                    diff --git a/typedoc/types/GestureResult.html b/typedoc/types/GestureResult.html index a62f4741..8cd17a76 100644 --- a/typedoc/types/GestureResult.html +++ b/typedoc/types/GestureResult.html @@ -1,7 +1,7 @@ -GestureResult | @vladmandic/human - v3.3.5

                                                                                                                                                                    Type Alias GestureResult

                                                                                                                                                                    GestureResult:
                                                                                                                                                                        | { face: number; gesture: FaceGesture }
                                                                                                                                                                        | { gesture: IrisGesture; iris: number }
                                                                                                                                                                        | { body: number; gesture: BodyGesture }
                                                                                                                                                                        | { gesture: HandGesture; hand: number }

                                                                                                                                                                    Gesture combined results +GestureResult | @vladmandic/human - v3.3.6

                                                                                                                                                                    @vladmandic/human - v3.3.6
                                                                                                                                                                      Preparing search index...

                                                                                                                                                                      Type Alias GestureResult

                                                                                                                                                                      GestureResult:
                                                                                                                                                                          | { face: number; gesture: FaceGesture }
                                                                                                                                                                          | { gesture: IrisGesture; iris: number }
                                                                                                                                                                          | { body: number; gesture: BodyGesture }
                                                                                                                                                                          | { gesture: HandGesture; hand: number }

                                                                                                                                                                      Gesture combined results Each result has:

                                                                                                                                                                      • part: part name and number where gesture was detected: face, iris, body, hand
                                                                                                                                                                      • gesture: gesture detected
                                                                                                                                                                      -

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                                                                                                                                                                      +

                                                                                                                                                                      Settings

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                                                                                                                                                                      diff --git a/typedoc/types/HandGesture.html b/typedoc/types/HandGesture.html index 15775176..a88250d5 100644 --- a/typedoc/types/HandGesture.html +++ b/typedoc/types/HandGesture.html @@ -1,2 +1,2 @@ -HandGesture | @vladmandic/human - v3.3.5

                                                                                                                                                                      Type Alias HandGesture

                                                                                                                                                                      HandGesture:
                                                                                                                                                                          | `${"thumb"
                                                                                                                                                                          | "index"
                                                                                                                                                                          | "middle"
                                                                                                                                                                          | "ring"
                                                                                                                                                                          | "pinky"} forward`
                                                                                                                                                                          | `${"thumb" | "index" | "middle" | "ring" | "pinky"} up`
                                                                                                                                                                          | "victory"
                                                                                                                                                                          | "thumbs up"

                                                                                                                                                                      hand gesture type

                                                                                                                                                                      -

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                                                                                                                                                                      +HandGesture | @vladmandic/human - v3.3.6
                                                                                                                                                                      @vladmandic/human - v3.3.6
                                                                                                                                                                        Preparing search index...

                                                                                                                                                                        Type Alias HandGesture

                                                                                                                                                                        HandGesture:
                                                                                                                                                                            | `${"thumb"
                                                                                                                                                                            | "index"
                                                                                                                                                                            | "middle"
                                                                                                                                                                            | "ring"
                                                                                                                                                                            | "pinky"} forward`
                                                                                                                                                                            | `${"thumb" | "index" | "middle" | "ring" | "pinky"} up`
                                                                                                                                                                            | "victory"
                                                                                                                                                                            | "thumbs up"

                                                                                                                                                                        hand gesture type

                                                                                                                                                                        +

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                                                                                                                                                                        diff --git a/typedoc/types/HandType.html b/typedoc/types/HandType.html index b76664d5..87f95c11 100644 --- a/typedoc/types/HandType.html +++ b/typedoc/types/HandType.html @@ -1 +1 @@ -HandType | @vladmandic/human - v3.3.5

                                                                                                                                                                        Type Alias HandType

                                                                                                                                                                        HandType: "hand" | "fist" | "pinch" | "point" | "face" | "tip" | "pinchtip"

                                                                                                                                                                        Settings

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                                                                                                                                                                        +HandType | @vladmandic/human - v3.3.6
                                                                                                                                                                        @vladmandic/human - v3.3.6
                                                                                                                                                                          Preparing search index...

                                                                                                                                                                          Type Alias HandType

                                                                                                                                                                          HandType: "hand" | "fist" | "pinch" | "point" | "face" | "tip" | "pinchtip"

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                                                                                                                                                                          diff --git a/typedoc/types/ImageObjects.html b/typedoc/types/ImageObjects.html index 4880582e..df6dfe3c 100644 --- a/typedoc/types/ImageObjects.html +++ b/typedoc/types/ImageObjects.html @@ -1,2 +1,2 @@ -ImageObjects | @vladmandic/human - v3.3.5

                                                                                                                                                                          Type Alias ImageObjects

                                                                                                                                                                          ImageObjects: ImageData | ImageBitmap

                                                                                                                                                                          Defines all possible image objects

                                                                                                                                                                          -

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                                                                                                                                                                          +ImageObjects | @vladmandic/human - v3.3.6
                                                                                                                                                                          @vladmandic/human - v3.3.6
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                                                                                                                                                                            Type Alias ImageObjects

                                                                                                                                                                            ImageObjects: ImageData | ImageBitmap

                                                                                                                                                                            Defines all possible image objects

                                                                                                                                                                            +

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                                                                                                                                                                            diff --git a/typedoc/types/Input.html b/typedoc/types/Input.html index b2ca2c2b..44a22e69 100644 --- a/typedoc/types/Input.html +++ b/typedoc/types/Input.html @@ -1,2 +1,2 @@ -Input | @vladmandic/human - v3.3.5

                                                                                                                                                                            Type Alias Input

                                                                                                                                                                            Input: Tensor | AnyCanvas | AnyImage | AnyVideo | ImageObjects | ExternalCanvas

                                                                                                                                                                            Defines all possible input types for Human detection

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                                                                                                                                                                            +Input | @vladmandic/human - v3.3.6
                                                                                                                                                                            @vladmandic/human - v3.3.6
                                                                                                                                                                              Preparing search index...

                                                                                                                                                                              Type Alias Input

                                                                                                                                                                              Input: Tensor | AnyCanvas | AnyImage | AnyVideo | ImageObjects | ExternalCanvas

                                                                                                                                                                              Defines all possible input types for Human detection

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                                                                                                                                                                              diff --git a/typedoc/types/IrisGesture.html b/typedoc/types/IrisGesture.html index a8d78190..626d8032 100644 --- a/typedoc/types/IrisGesture.html +++ b/typedoc/types/IrisGesture.html @@ -1,2 +1,2 @@ -IrisGesture | @vladmandic/human - v3.3.5

                                                                                                                                                                              Type Alias IrisGesture

                                                                                                                                                                              IrisGesture:
                                                                                                                                                                                  | "facing center"
                                                                                                                                                                                  | `looking ${"left"
                                                                                                                                                                                  | "right"
                                                                                                                                                                                  | "up"
                                                                                                                                                                                  | "down"}`
                                                                                                                                                                                  | "looking center"

                                                                                                                                                                              iris gesture type

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                                                                                                                                                                              +IrisGesture | @vladmandic/human - v3.3.6
                                                                                                                                                                              @vladmandic/human - v3.3.6
                                                                                                                                                                                Preparing search index...

                                                                                                                                                                                Type Alias IrisGesture

                                                                                                                                                                                IrisGesture:
                                                                                                                                                                                    | "facing center"
                                                                                                                                                                                    | `looking ${"left"
                                                                                                                                                                                    | "right"
                                                                                                                                                                                    | "up"
                                                                                                                                                                                    | "down"}`
                                                                                                                                                                                    | "looking center"

                                                                                                                                                                                iris gesture type

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                                                                                                                                                                                diff --git a/typedoc/types/ObjectType.html b/typedoc/types/ObjectType.html index ef8feb67..4a100813 100644 --- a/typedoc/types/ObjectType.html +++ b/typedoc/types/ObjectType.html @@ -1 +1 @@ -ObjectType | @vladmandic/human - v3.3.5

                                                                                                                                                                                Type Alias ObjectType

                                                                                                                                                                                ObjectType:
                                                                                                                                                                                    | "person"
                                                                                                                                                                                    | "bicycle"
                                                                                                                                                                                    | "car"
                                                                                                                                                                                    | "motorcycle"
                                                                                                                                                                                    | "airplane"
                                                                                                                                                                                    | "bus"
                                                                                                                                                                                    | "train"
                                                                                                                                                                                    | "truck"
                                                                                                                                                                                    | "boat"
                                                                                                                                                                                    | "traffic light"
                                                                                                                                                                                    | "fire hydrant"
                                                                                                                                                                                    | "stop sign"
                                                                                                                                                                                    | "parking meter"
                                                                                                                                                                                    | "bench"
                                                                                                                                                                                    | "bird"
                                                                                                                                                                                    | "cat"
                                                                                                                                                                                    | "dog"
                                                                                                                                                                                    | "horse"
                                                                                                                                                                                    | "sheep"
                                                                                                                                                                                    | "cow"
                                                                                                                                                                                    | "elephant"
                                                                                                                                                                                    | "bear"
                                                                                                                                                                                    | "zebra"
                                                                                                                                                                                    | "giraffe"
                                                                                                                                                                                    | "backpack"
                                                                                                                                                                                    | "umbrella"
                                                                                                                                                                                    | "handbag"
                                                                                                                                                                                    | "tie"
                                                                                                                                                                                    | "suitcase"
                                                                                                                                                                                    | "frisbee"
                                                                                                                                                                                    | "skis"
                                                                                                                                                                                    | "snowboard"
                                                                                                                                                                                    | "sports ball"
                                                                                                                                                                                    | "kite"
                                                                                                                                                                                    | "baseball bat"
                                                                                                                                                                                    | "baseball glove"
                                                                                                                                                                                    | "skateboard"
                                                                                                                                                                                    | "surfboard"
                                                                                                                                                                                    | "tennis racket"
                                                                                                                                                                                    | "bottle"
                                                                                                                                                                                    | "wine glass"
                                                                                                                                                                                    | "cup"
                                                                                                                                                                                    | "fork"
                                                                                                                                                                                    | "knife"
                                                                                                                                                                                    | "spoon"
                                                                                                                                                                                    | "bowl"
                                                                                                                                                                                    | "banana"
                                                                                                                                                                                    | "apple"
                                                                                                                                                                                    | "sandwich"
                                                                                                                                                                                    | "orange"
                                                                                                                                                                                    | "broccoli"
                                                                                                                                                                                    | "carrot"
                                                                                                                                                                                    | "hot dog"
                                                                                                                                                                                    | "pizza"
                                                                                                                                                                                    | "donut"
                                                                                                                                                                                    | "cake"
                                                                                                                                                                                    | "chair"
                                                                                                                                                                                    | "couch"
                                                                                                                                                                                    | "potted plant"
                                                                                                                                                                                    | "bed"
                                                                                                                                                                                    | "dining table"
                                                                                                                                                                                    | "toilet"
                                                                                                                                                                                    | "tv"
                                                                                                                                                                                    | "laptop"
                                                                                                                                                                                    | "mouse"
                                                                                                                                                                                    | "remote"
                                                                                                                                                                                    | "keyboard"
                                                                                                                                                                                    | "cell phone"
                                                                                                                                                                                    | "microwave"
                                                                                                                                                                                    | "oven"
                                                                                                                                                                                    | "toaster"
                                                                                                                                                                                    | "sink"
                                                                                                                                                                                    | "refrigerator"
                                                                                                                                                                                    | "book"
                                                                                                                                                                                    | "clock"
                                                                                                                                                                                    | "vase"
                                                                                                                                                                                    | "scissors"
                                                                                                                                                                                    | "teddy bear"
                                                                                                                                                                                    | "hair drier"
                                                                                                                                                                                    | "toothbrush"

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                                                                                                                                                                                +ObjectType | @vladmandic/human - v3.3.6
                                                                                                                                                                                @vladmandic/human - v3.3.6
                                                                                                                                                                                  Preparing search index...

                                                                                                                                                                                  Type Alias ObjectType

                                                                                                                                                                                  ObjectType:
                                                                                                                                                                                      | "person"
                                                                                                                                                                                      | "bicycle"
                                                                                                                                                                                      | "car"
                                                                                                                                                                                      | "motorcycle"
                                                                                                                                                                                      | "airplane"
                                                                                                                                                                                      | "bus"
                                                                                                                                                                                      | "train"
                                                                                                                                                                                      | "truck"
                                                                                                                                                                                      | "boat"
                                                                                                                                                                                      | "traffic light"
                                                                                                                                                                                      | "fire hydrant"
                                                                                                                                                                                      | "stop sign"
                                                                                                                                                                                      | "parking meter"
                                                                                                                                                                                      | "bench"
                                                                                                                                                                                      | "bird"
                                                                                                                                                                                      | "cat"
                                                                                                                                                                                      | "dog"
                                                                                                                                                                                      | "horse"
                                                                                                                                                                                      | "sheep"
                                                                                                                                                                                      | "cow"
                                                                                                                                                                                      | "elephant"
                                                                                                                                                                                      | "bear"
                                                                                                                                                                                      | "zebra"
                                                                                                                                                                                      | "giraffe"
                                                                                                                                                                                      | "backpack"
                                                                                                                                                                                      | "umbrella"
                                                                                                                                                                                      | "handbag"
                                                                                                                                                                                      | "tie"
                                                                                                                                                                                      | "suitcase"
                                                                                                                                                                                      | "frisbee"
                                                                                                                                                                                      | "skis"
                                                                                                                                                                                      | "snowboard"
                                                                                                                                                                                      | "sports ball"
                                                                                                                                                                                      | "kite"
                                                                                                                                                                                      | "baseball bat"
                                                                                                                                                                                      | "baseball glove"
                                                                                                                                                                                      | "skateboard"
                                                                                                                                                                                      | "surfboard"
                                                                                                                                                                                      | "tennis racket"
                                                                                                                                                                                      | "bottle"
                                                                                                                                                                                      | "wine glass"
                                                                                                                                                                                      | "cup"
                                                                                                                                                                                      | "fork"
                                                                                                                                                                                      | "knife"
                                                                                                                                                                                      | "spoon"
                                                                                                                                                                                      | "bowl"
                                                                                                                                                                                      | "banana"
                                                                                                                                                                                      | "apple"
                                                                                                                                                                                      | "sandwich"
                                                                                                                                                                                      | "orange"
                                                                                                                                                                                      | "broccoli"
                                                                                                                                                                                      | "carrot"
                                                                                                                                                                                      | "hot dog"
                                                                                                                                                                                      | "pizza"
                                                                                                                                                                                      | "donut"
                                                                                                                                                                                      | "cake"
                                                                                                                                                                                      | "chair"
                                                                                                                                                                                      | "couch"
                                                                                                                                                                                      | "potted plant"
                                                                                                                                                                                      | "bed"
                                                                                                                                                                                      | "dining table"
                                                                                                                                                                                      | "toilet"
                                                                                                                                                                                      | "tv"
                                                                                                                                                                                      | "laptop"
                                                                                                                                                                                      | "mouse"
                                                                                                                                                                                      | "remote"
                                                                                                                                                                                      | "keyboard"
                                                                                                                                                                                      | "cell phone"
                                                                                                                                                                                      | "microwave"
                                                                                                                                                                                      | "oven"
                                                                                                                                                                                      | "toaster"
                                                                                                                                                                                      | "sink"
                                                                                                                                                                                      | "refrigerator"
                                                                                                                                                                                      | "book"
                                                                                                                                                                                      | "clock"
                                                                                                                                                                                      | "vase"
                                                                                                                                                                                      | "scissors"
                                                                                                                                                                                      | "teddy bear"
                                                                                                                                                                                      | "hair drier"
                                                                                                                                                                                      | "toothbrush"

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                                                                                                                                                                                  diff --git a/typedoc/types/Point.html b/typedoc/types/Point.html index ce73c9c1..d4cd4420 100644 --- a/typedoc/types/Point.html +++ b/typedoc/types/Point.html @@ -1,2 +1,2 @@ -Point | @vladmandic/human - v3.3.5

                                                                                                                                                                                  Type Alias Point

                                                                                                                                                                                  Point: [number, number, number?]

                                                                                                                                                                                  generic point as [x, y, z?]

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                                                                                                                                                                                  +Point | @vladmandic/human - v3.3.6
                                                                                                                                                                                  @vladmandic/human - v3.3.6
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                                                                                                                                                                                    Type Alias Point

                                                                                                                                                                                    Point: [number, number, number?]

                                                                                                                                                                                    generic point as [x, y, z?]

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                                                                                                                                                                                    diff --git a/typedoc/types/Race.html b/typedoc/types/Race.html index 1f5b6360..862cd822 100644 --- a/typedoc/types/Race.html +++ b/typedoc/types/Race.html @@ -1 +1 @@ -Race | @vladmandic/human - v3.3.5

                                                                                                                                                                                    Type Alias Race

                                                                                                                                                                                    Race: "white" | "black" | "asian" | "indian" | "other"

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                                                                                                                                                                                    +Race | @vladmandic/human - v3.3.6
                                                                                                                                                                                    @vladmandic/human - v3.3.6
                                                                                                                                                                                      Preparing search index...

                                                                                                                                                                                      Type Alias Race

                                                                                                                                                                                      Race: "white" | "black" | "asian" | "indian" | "other"

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                                                                                                                                                                                      diff --git a/typedoc/types/SegmentationEnum.html b/typedoc/types/SegmentationEnum.html index 34659610..cfcffc74 100644 --- a/typedoc/types/SegmentationEnum.html +++ b/typedoc/types/SegmentationEnum.html @@ -1,2 +1,2 @@ -SegmentationEnum | @vladmandic/human - v3.3.5

                                                                                                                                                                                      Type Alias SegmentationEnum

                                                                                                                                                                                      SegmentationEnum: "default" | "alpha" | "foreground" | "state"

                                                                                                                                                                                      Possible segmentation model behavior

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                                                                                                                                                                                      +SegmentationEnum | @vladmandic/human - v3.3.6
                                                                                                                                                                                      @vladmandic/human - v3.3.6
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                                                                                                                                                                                        Type Alias SegmentationEnum

                                                                                                                                                                                        SegmentationEnum: "default" | "alpha" | "foreground" | "state"

                                                                                                                                                                                        Possible segmentation model behavior

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                                                                                                                                                                                        diff --git a/typedoc/types/Tensor1D.html b/typedoc/types/Tensor1D.html index 0a01fd27..4001c98f 100644 --- a/typedoc/types/Tensor1D.html +++ b/typedoc/types/Tensor1D.html @@ -1,2 +1,2 @@ -Tensor1D | @vladmandic/human - v3.3.5

                                                                                                                                                                                        Type Alias Tensor1D

                                                                                                                                                                                        Tensor1D: Tensor<R1>

                                                                                                                                                                                        Doclink

                                                                                                                                                                                        Tensor

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                                                                                                                                                                                        +Tensor1D | @vladmandic/human - v3.3.6
                                                                                                                                                                                        @vladmandic/human - v3.3.6
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                                                                                                                                                                                          Type Alias Tensor1D

                                                                                                                                                                                          Tensor1D: Tensor<R1>

                                                                                                                                                                                          Tensor

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                                                                                                                                                                                          diff --git a/typedoc/types/Tensor2D.html b/typedoc/types/Tensor2D.html index 3c791486..5d315e6d 100644 --- a/typedoc/types/Tensor2D.html +++ b/typedoc/types/Tensor2D.html @@ -1,2 +1,2 @@ -Tensor2D | @vladmandic/human - v3.3.5

                                                                                                                                                                                          Type Alias Tensor2D

                                                                                                                                                                                          Tensor2D: Tensor<R2>

                                                                                                                                                                                          Doclink

                                                                                                                                                                                          Tensor

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                                                                                                                                                                                          +Tensor2D | @vladmandic/human - v3.3.6
                                                                                                                                                                                          @vladmandic/human - v3.3.6
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                                                                                                                                                                                            Type Alias Tensor2D

                                                                                                                                                                                            Tensor2D: Tensor<R2>

                                                                                                                                                                                            Tensor

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                                                                                                                                                                                            diff --git a/typedoc/types/Tensor3D.html b/typedoc/types/Tensor3D.html index b839a078..7c532b0d 100644 --- a/typedoc/types/Tensor3D.html +++ b/typedoc/types/Tensor3D.html @@ -1,2 +1,2 @@ -Tensor3D | @vladmandic/human - v3.3.5

                                                                                                                                                                                            Type Alias Tensor3D

                                                                                                                                                                                            Tensor3D: Tensor<R3>

                                                                                                                                                                                            Doclink

                                                                                                                                                                                            Tensor

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                                                                                                                                                                                            +Tensor3D | @vladmandic/human - v3.3.6
                                                                                                                                                                                            @vladmandic/human - v3.3.6
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                                                                                                                                                                                              Type Alias Tensor3D

                                                                                                                                                                                              Tensor3D: Tensor<R3>

                                                                                                                                                                                              Tensor

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                                                                                                                                                                                              diff --git a/typedoc/types/Tensor4D.html b/typedoc/types/Tensor4D.html index b88cde03..8acd3b3c 100644 --- a/typedoc/types/Tensor4D.html +++ b/typedoc/types/Tensor4D.html @@ -1,2 +1,2 @@ -Tensor4D | @vladmandic/human - v3.3.5

                                                                                                                                                                                              Type Alias Tensor4D

                                                                                                                                                                                              Tensor4D: Tensor<R4>

                                                                                                                                                                                              Doclink

                                                                                                                                                                                              Tensor

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                                                                                                                                                                                              +Tensor4D | @vladmandic/human - v3.3.6
                                                                                                                                                                                              @vladmandic/human - v3.3.6
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                                                                                                                                                                                                Type Alias Tensor4D

                                                                                                                                                                                                Tensor4D: Tensor<R4>

                                                                                                                                                                                                Tensor

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                                                                                                                                                                                                diff --git a/typedoc/types/TensorLike.html b/typedoc/types/TensorLike.html index 9c4b4deb..6514b47b 100644 --- a/typedoc/types/TensorLike.html +++ b/typedoc/types/TensorLike.html @@ -1,2 +1,2 @@ -TensorLike | @vladmandic/human - v3.3.5

                                                                                                                                                                                                Type Alias TensorLike

                                                                                                                                                                                                TensorLike:
                                                                                                                                                                                                    | TypedArray
                                                                                                                                                                                                    | number
                                                                                                                                                                                                    | boolean
                                                                                                                                                                                                    | string
                                                                                                                                                                                                    | RecursiveArray<number | number[] | TypedArray>
                                                                                                                                                                                                    | RecursiveArray<boolean>
                                                                                                                                                                                                    | RecursiveArray<string>
                                                                                                                                                                                                    | Uint8Array[]

                                                                                                                                                                                                Docalias

                                                                                                                                                                                                TypedArray|Array

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                                                                                                                                                                                                +TensorLike | @vladmandic/human - v3.3.6
                                                                                                                                                                                                @vladmandic/human - v3.3.6
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                                                                                                                                                                                                  Type Alias TensorLike

                                                                                                                                                                                                  TensorLike:
                                                                                                                                                                                                      | TypedArray
                                                                                                                                                                                                      | number
                                                                                                                                                                                                      | boolean
                                                                                                                                                                                                      | string
                                                                                                                                                                                                      | RecursiveArray<number | number[] | TypedArray>
                                                                                                                                                                                                      | RecursiveArray<boolean>
                                                                                                                                                                                                      | RecursiveArray<string>
                                                                                                                                                                                                      | Uint8Array[]

                                                                                                                                                                                                  TypedArray|Array

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                                                                                                                                                                                                  diff --git a/typedoc/types/WarmupEnum.html b/typedoc/types/WarmupEnum.html index cbc1b105..b072a023 100644 --- a/typedoc/types/WarmupEnum.html +++ b/typedoc/types/WarmupEnum.html @@ -1,2 +1,2 @@ -WarmupEnum | @vladmandic/human - v3.3.5

                                                                                                                                                                                                  Type Alias WarmupEnum

                                                                                                                                                                                                  WarmupEnum: "" | "none" | "face" | "full" | "body"

                                                                                                                                                                                                  Possible values for human.warmup

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                                                                                                                                                                                                  +WarmupEnum | @vladmandic/human - v3.3.6
                                                                                                                                                                                                  @vladmandic/human - v3.3.6
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                                                                                                                                                                                                    Type Alias WarmupEnum

                                                                                                                                                                                                    WarmupEnum: "" | "none" | "face" | "full" | "body"

                                                                                                                                                                                                    Possible values for human.warmup

                                                                                                                                                                                                    +

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                                                                                                                                                                                                    diff --git a/typedoc/types/match.Descriptor.html b/typedoc/types/match.Descriptor.html index 1ab16a75..76fef892 100644 --- a/typedoc/types/match.Descriptor.html +++ b/typedoc/types/match.Descriptor.html @@ -1,2 +1,2 @@ -Descriptor | @vladmandic/human - v3.3.5

                                                                                                                                                                                                    Type Alias Descriptor

                                                                                                                                                                                                    Descriptor: number[]

                                                                                                                                                                                                    Face descriptor type as number array

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                                                                                                                                                                                                    +Descriptor | @vladmandic/human - v3.3.6
                                                                                                                                                                                                    @vladmandic/human - v3.3.6
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                                                                                                                                                                                                      Type Alias Descriptor

                                                                                                                                                                                                      Descriptor: number[]

                                                                                                                                                                                                      Face descriptor type as number array

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                                                                                                                                                                                                      diff --git a/typedoc/types/match.MatchOptions.html b/typedoc/types/match.MatchOptions.html index 75aae439..43ec901d 100644 --- a/typedoc/types/match.MatchOptions.html +++ b/typedoc/types/match.MatchOptions.html @@ -1 +1 @@ -MatchOptions | @vladmandic/human - v3.3.5

                                                                                                                                                                                                      Type Alias MatchOptions

                                                                                                                                                                                                      MatchOptions:
                                                                                                                                                                                                          | {
                                                                                                                                                                                                              max?: number;
                                                                                                                                                                                                              min?: number;
                                                                                                                                                                                                              multiplier?: number;
                                                                                                                                                                                                              order?: number;
                                                                                                                                                                                                              threshold?: number;
                                                                                                                                                                                                          }
                                                                                                                                                                                                          | undefined

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                                                                                                                                                                                                      +MatchOptions | @vladmandic/human - v3.3.6
                                                                                                                                                                                                      @vladmandic/human - v3.3.6
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                                                                                                                                                                                                        Type Alias MatchOptions

                                                                                                                                                                                                        MatchOptions:
                                                                                                                                                                                                            | {
                                                                                                                                                                                                                max?: number;
                                                                                                                                                                                                                min?: number;
                                                                                                                                                                                                                multiplier?: number;
                                                                                                                                                                                                                order?: number;
                                                                                                                                                                                                                threshold?: number;
                                                                                                                                                                                                            }
                                                                                                                                                                                                            | undefined

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                                                                                                                                                                                                        diff --git a/typedoc/variables/defaults.html b/typedoc/variables/defaults.html index f50ac2be..60f79557 100644 --- a/typedoc/variables/defaults.html +++ b/typedoc/variables/defaults.html @@ -1,4 +1,4 @@ -defaults | @vladmandic/human - v3.3.5

                                                                                                                                                                                                        Variable defaultsConst

                                                                                                                                                                                                        defaults: Config = ...

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                                                                                                                                                                                                        diff --git a/typedoc/variables/draw.options.html b/typedoc/variables/draw.options.html index 0a3ffe86..6d5ee37d 100644 --- a/typedoc/variables/draw.options.html +++ b/typedoc/variables/draw.options.html @@ -1,2 +1,2 @@ -options | @vladmandic/human - v3.3.5

                                                                                                                                                                                                        Variable optionsConst

                                                                                                                                                                                                        options: DrawOptions = ...

                                                                                                                                                                                                        currently set draw options DrawOptions

                                                                                                                                                                                                        -

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                                                                                                                                                                                                        +options | @vladmandic/human - v3.3.6
                                                                                                                                                                                                        @vladmandic/human - v3.3.6
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                                                                                                                                                                                                          Variable optionsConst

                                                                                                                                                                                                          options: DrawOptions = ...

                                                                                                                                                                                                          currently set draw options DrawOptions

                                                                                                                                                                                                          +

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                                                                                                                                                                                                          diff --git a/typedoc/variables/env-1.html b/typedoc/variables/env-1.html deleted file mode 100644 index 3ee621f5..00000000 --- a/typedoc/variables/env-1.html +++ /dev/null @@ -1 +0,0 @@ -env | @vladmandic/human - v3.3.5

                                                                                                                                                                                                          Variable envConst

                                                                                                                                                                                                          env: Env = ...

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                                                                                                                                                                                                          diff --git a/typedoc/variables/env.html b/typedoc/variables/env.html new file mode 100644 index 00000000..e7961877 --- /dev/null +++ b/typedoc/variables/env.html @@ -0,0 +1 @@ +env | @vladmandic/human - v3.3.6
                                                                                                                                                                                                          @vladmandic/human - v3.3.6
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                                                                                                                                                                                                            Variable envConst

                                                                                                                                                                                                            env: Env = ...

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